Digital chromatographic microfluidic biochip and use method thereof

Abstract

Compositions and methods for quantifying target molecules from a sample using digital chromatography implemented on a microfluidic biochip are described. A microfluidic device is provided that includes two symmetrical microfluidic channels, each comprising a hydrophobic filter structure and a high density hydrophilic flow-capture junction array (FT-JA). The centrally positioned turbine valve increases resistance in the flow passage to direct fluid laterally through the capture passage. Microfluidic devices (e.g., chips) are configured to facilitate simultaneous, parallel capture of control and test samples including target molecules (e.g., biomarkers) immobilized on micron-sized particles (e.g., microbeads) by capturing beads in FT-JA. In some forms, the microfluidic chip quantifies biomarkers within a biological sample at 90% efficiency for imaging in a compact region (e.g., 30 mm2) over a low time range (e.g., 80 seconds). Exemplary biomarkers that can be quantified according to the described methods include tumor antigens and biomarkers derived from pathogens. Exemplary samples include tears, plasma, and blood.

Description

[0001] Cross-reference to related applications

[0002] This application claims the benefit and priority of U.S. Provisional Application No. 63 / 643,773, filed May 7, 2024, the entire contents of which are incorporated herein by reference. Technical Field

[0003] This invention relates to the automated detection of biomarkers in micron- and nano-sized samples, and more particularly to the use of digital chromatography for highly sensitive immunoassays. Background Technology

[0004] Precise quantification of protein biomarkers at ultra-low concentrations has become a cornerstone of early disease diagnosis and personalized treatment strategies. While traditional enzyme-linked immunosorbent assays (ELISAs) remain prevalent in clinical practice, their continuous analog signal detection imposes fundamental sensitivity limitations, particularly for detecting rare biomarkers in small biological samples such as tears and cerebrospinal fluid. The advent of digital ELISA has revolutionized molecular diagnostics by enabling single-molecule counting through microcompartmentation and achieving attomolar sensitivity by converting continuous signals into discrete digital events. This paradigm relies on antibody-coated magnetic beads to capture target molecules, which are then separated into microwells or droplets containing chemiluminescent substrates for individual signal quantification. Despite achieving significant sensitivity, current digital ELISA platforms employing magnetic beads face several persistent obstacles.

[0005] First, compared to silica beads (SiO2, n = 1.45), the opacity and refractive index of the magnetic bead core (Fe2O3, n = 2.42) significantly attenuated the fluorescence intensity, reducing the detectable signal intensity by approximately three times. This optical interference fundamentally limits the signal-to-noise ratio and spatial resolution in full-field imaging. Second, the magnetic bead-based method exhibits limited capture efficiency in complex biological samples, ranging from 40% to 50%, as previously studied. low microbead detection rates (10-30%) further reduce assay effectiveness due to limited imaging throughput and restricted bead array density. Third, magnetic field-induced bead aggregation often leads to non-specific interactions and incomplete purification, which collectively reduce assay specificity and precision. Environmental variables such as pH fluctuations, thermal variations, and non-specific molecular adsorption further complicate accurate biomarker quantification, requiring elaborate calibration procedures and standardized assay conditions. These limitations are complicated by the statistical constraints inherent to the Poisson distribution assumption, where scientists typically estimate total concentrations of molecules by analyzing the frequency of positive signals in microbeads (AMB, average molecules per bead). Although increasing the fraction of beads analyzed (analysis fraction > 80%) theoretically improves measurement precision by reducing counting errors and enhancing statistical reliability (%CV = Vn / n), practical implementation remains hindered by limitations in imaging throughput. Conversely, reducing bead usage adversely affects the kinetics of molecular interactions (k on ∝ [bead]2), thus compromising both dynamic range and practical feasibility. Consequently, the detection window narrows due to signal saturation at elevated concentrations (typically > 100 molecules / bead) and elevated random noise at lower limits, limiting overall measurement precision. For samples with bimodal concentration distributions (e.g., 0.1 fM & 0.1 nM coexisting), sparse bead arrays cannot simultaneously detect (sensitivity ratio > 1000x).

[0006] Recent strategies attempt to address these challenges by adjusting capture efficiency and assay methodology. For example, microdroplet-based methods exhibit superior capture efficiency (up to 95%), although this advantage is typically traded for complexity in processing their imaging due to reduced array density. Conversely, assays employing fewer beads achieve greater enzyme occupancy per bead, enhancing signal, but at the expense of prolonged incubation periods (up to three hours), highlighting the inherent tradeoff between dynamic range and throughput. While droplet-casting assays improve processing efficiency, their reliance on evaporation-mediated assembly introduces environmental instability that limits clinical utility.

[0007] In parallel, traditional lateral flow immunoassays (LFIA) offer rapid, magnet-free operation by direct immunochromatographic principles. A biological sample migrates along a nitrocellulose membrane by capillary action, interacting with antibody-conjugated nanoparticles to produce visually distinguishable colored bands in the test and control regions. However, the reliance of LFIA on subjective colorimetric interpretation significantly limits its quantitative accuracy and sensitivity. Even with the use of reader devices or enhanced labels, traditional LFIA typically achieve detection limits in the high picomolar to nanomolar range (about 0.1-10 ng / mL) and rarely below this range. Immuno-chromatography (ICA) is renowned for its speed, directness, and reliability, and is widely used for point-of-care testing, eliminating the need for complex instrumentation or specialized laboratory settings. In the immunochromatographic process, introduction of a fluid sample onto a membrane strip allows target molecules (antigens) to bind with labeled antibodies present on the strip, resulting in the appearance of visible colored lines in the test and control regions.

[0008] While visual detection of colored lines on a test strip enables qualitative analysis, this method is limited by the subjective interpretation of color intensity, which is especially problematic in quantitative determination of biomarkers present in trace amounts.

[0009] Accordingly, there is a need for compositions and methods for biomarker detection and analysis that are more efficient, more automatable, have enhanced sensitivity, have enhanced reproducibility, require less sample, and require lower concentrations of biomarkers.

[0010] It is therefore an object of the present invention to provide systems and methods for automated detection of biomarkers with high sensitivity.

[0011] It is another object of the present invention to provide systems and methods for simultaneous detection of multiple biomarkers from a single sample.

[0012] It is another object of the present invention to provide methods that simplify assay procedures and increase the throughput and scalability of diagnostic systems.

[0013] It is another object of the present invention to provide methods for reliable analysis of biological samples that are not affected by variations in environmental factors, such as pH, temperature, ionic concentration, and interfacial effects.

[0014] It is another object of the present invention to provide methods that use low sample volumes and / or low concentrations of target biomarkers to reduce background noise and enhance detection, to enhance the diagnosis of diseases and conditions. SUMMARY

[0015] Compositions and methods have been developed for scalable, automated detection of biomarkers using microfluidic systems. The compositions and methods employ digital immuno-chromatography (ICA) to implement traditional ICA with enhanced sensitivity found in digital ELISA. Methods for clinical diagnosis by detecting protein biomarkers in small volume samples are also provided.

[0016] Microfluidic chips and methods of making and using the disclosed chips are disclosed. The microfluidic chips generally include a microfluidic platform that includes one or more microfluidic flow paths. In some forms, the microfluidic flow path includes two inlet conduits, a flow-trap junction (FTJ) array structure, and two outlet conduits. In some forms, the microfluidic flow path is configured to move fluid from the inlet conduits into the FTJ structure and from the FTJ structure into the outlet conduits.

[0017] In some forms, the inlet conduits are wider at a location where fluid moves from the inlet conduits into the FTJ structure than at a location where fluid is introduced into the inlet conduits. In some forms, the inlet conduits contain a plurality of hydrophobic micro-pillar structures.

[0018] In some forms, the FTJ structure includes a flow layer and a trap layer. In some forms, the flow layer is in contact with, on top of, and overlapping the trap layer.

[0019] In some forms, the flow layer includes a plurality of flow microfluidic channels, each flow microfluidic channel including a top, a sidewall, and an opening on a bottom. In some forms, the surfaces of the flow microfluidic channels are hydrophobic. In some forms, the flow microfluidic channels allow free passage of micrometer-scale particles and nanometer-scale objects. In some forms, fluid flows in the same direction in all of the flow microfluidic channels.

[0020] In some forms, the flow microfluidic channels are parallel to each other. In some forms, the trap layer includes a plurality of trap microfluidic channels, each trap microfluidic channel including a bottom, a sidewall, and an opening on a top. In some forms, the trap microfluidic channels are parallel to each other. In some forms, the surfaces of the trap microfluidic channels are hydrophilic. In some forms, fluid flows in the same direction in all of the trap microfluidic channels.

[0021] In some forms, the flow microfluidic channels are not parallel to the trap microfluidic channels. In some forms, the flow microfluidic channels and the trap microfluidic channels allow fluid to move from the flow microfluidic channels into the trap microfluidic channels via the openings on the bottoms and the openings on the tops, respectively.

[0022] In some forms, the oppositely oriented channels positioned at the end of each flow channel create a turbine valve structure in which a centrally positioned valve in the flow layer causes a controlled hydrodynamic resistance to direct fluid laterally into the trap network. The system facilitates selective bead trapping through an adaptive zigzag path dynamically controlled by trap occupancy state: unoccupied traps exhibit minimal resistance, enabling beads to enter and settle, while occupied traps create an elevated resistance profile that redirects fluid recirculation to the main channel and diverts subsequent beads to an adjacent available site. This self-regulating process ensures spatially ordered bead deposition as the hydrodynamics autonomously adjust to real-time trap availability. Upon saturation of all trap sites, excess beads undergo controlled evacuation via the end flow conduit, preventing overcrowding while maintaining optimal particle density. The architecture operates with autonomous fluid regulation, leveraging occupancy-dependent resistance modulation, self-guided particle routing, and systematic overfill prevention to achieve non-mechanical flow control, self-limited deposition, and scalable array formation without reliance on external control systems or active monitoring components.

[0023] In some forms, the combination of the trap microfluidic channel, the transition from the flow microfluidic channel to the trap microfluidic channel, or both the trap microfluidic channel and the transition from the flow microfluidic channel to the trap microfluidic channel is configured to allow nanoscale objects to pass through the trap microfluidic channel, whereby the nanoscale objects that have passed flow into the outlet conduit.

[0024] In some forms, the combination of the trap microfluidic channel, the transition from the flow microfluidic channel to the trap microfluidic channel, or both the trap microfluidic channel and the transition from the flow microfluidic channel to the trap microfluidic channel is configured to trap micrometer-scale particles in the trap microfluidic channel, whereby the trapped micrometer-scale particles combine to form an array within the FTJ structure.

[0025] In some forms, the sidewalls of the trap microfluidic channel are straight and parallel to each other, wherein the height of the trap microfluidic channel is less than the diameter of the micrometer-scale particles. In some forms, the height of the trap microfluidic channel is greater than the diameter or long dimension of the nanoscale objects.

[0026] In some forms, the sidewalls of the trap microfluidic channel are not straight such that the width of the trap microfluidic channel varies in a regular pattern along its length. In some forms, the pattern of width variation forms a narrowing in the width of the trap microfluidic channel, wherein the width of the narrowing is less than the diameter of the micrometer-scale particles. In some forms, the width of the narrowing is greater than the diameter or long dimension of the nanoscale objects.

[0027] In some forms, all or a subset of the constrictions overlap a flow layer between some or each opening on the floor of an adjacent flow microfluidic channel. In some forms, all or a subset of the constrictions overlap an opening on the floor of some or each flow microfluidic channel. In some forms, a subset of the constrictions overlap an opening on the floor of some or each flow microfluidic channel, and a subset of the constrictions overlap a flow layer between some or each opening on the floor of an adjacent flow microfluidic channel. In some forms, a subset of the constrictions overlap an opening on the floor of each flow microfluidic channel, and a subset of the constrictions overlap a flow layer between each opening on the floor of an adjacent flow microfluidic channel.

[0028] In some forms, a constriction that overlaps an opening on the floor of a flow microfluidic channel forms a small capture inlet on a downflow side of the opening and a large capture inlet on the downflow side of the opening for alternating capture microfluidic channels. In some forms, the small capture inlet is smaller in size than a diameter of a micron-scale particle. In some forms, the small capture inlet is larger in size than a diameter or long dimension of a nanoscale object. In some forms, the large capture inlet is larger in size than a diameter of a micron-scale particle.

[0029] In some forms, the flow microfluidic channel and the capture microfluidic channel are at a right angle to each other. In some forms, the flow microfluidic channel and the capture microfluidic channel are at an oblique angle to each other. In some forms, the flow microfluidic channel and the capture microfluidic channel are at an angle of 60° to 90°, 70° to 90°, 80° to 90°, 85° to 90°, 87° to 90°, 88° to 90°, or 89° to 90° to each other.

[0030] In some forms, a sidewall of the capture microfluidic channel is angled toward an upflow end of the flow microfluidic channel.

[0031] In some forms, the microfluidic flow path further comprises a sample inlet. In some forms, the microfluidic flow path is configured for moving fluid from the sample inlet into the inlet conduit.

[0032] In some forms, the microfluidic flow path further comprises a plurality of outlet channels, wherein the microfluidic flow path is configured for moving fluid from the capture microfluidic channel into the outlet channels and from the outlet channels into the outlet conduit. In some forms, each capture microfluidic channel is flowably connected to a different one of the outlet channels.

[0033] In some forms, the flow layer of the FTJ structure further comprises a plurality of outlet channels. In some forms, the outlet channels are interspersed among and parallel to the flow microfluidic channels. In some forms, the outlet channels each comprise a top, sidewalls, and an opening on a bottom. In some forms, the outlet channels and the capture microfluidic channels allow fluid to move from the capture microfluidic channels into the outlet channels via the openings on the bottoms and the tops, respectively. In some forms, the microfluidic flow paths are configured for fluid to move from the capture microfluidic channels into the outlet channels and from the outlet channels into the outlet conduits.

[0034] In some forms, the outlet channels alternate with the flow microfluidic channels in the flow layer of the FTJ structure.

[0035] In some forms, the flow layer of the FTJ structure further comprises an outlet channel. In some forms, the outlet channel comprises a top, sidewalls, and an opening on a bottom. In some forms, the outlet channel overlaps with the downward flow end of the capture microfluidic channel. In some forms, the outlet channels and the capture microfluidic channels allow fluid to move from the capture microfluidic channels into the outlet channels via the openings on the bottoms and the tops, respectively. In some forms, the microfluidic flow paths are configured for fluid to move from the capture microfluidic channels into the outlet channels and from the outlet channels into the outlet conduits.

[0036] In some forms, the chip is transparent in one or more regions. In some forms, the chip is transparent in a region corresponding to the array. In some forms, the array comprises a surface area of 25 mm 2 or less.

[0037] In some forms, the microfluidic platform comprises two microfluidic flow paths. In some forms, the two microfluidic flow paths are flowably connected to a single fluid reservoir. In some forms, the fluid reservoir is flowably connected to respective inlet conduits of the two microfluidic flow paths.

[0038] In some forms, the two microfluidic flow paths are symmetrically disposed on the microfluidic platform. In some forms, the two microfluidic flow paths are symmetrically disposed on the chip.

[0039] In some forms, the microfluidic chip further comprises a plurality of microparticles. In some forms, the microparticles comprise microbeads. In some forms, the microbeads comprise magnetic microbeads.

[0040] In some forms, the microparticles further comprise a first capture agent specific for a target biomarker. In some forms, the first capture agent is conjugated to the microparticles via streptavidin. In some forms, one or more of the first capture agents bind to the target biomarker.

[0041] In some forms, the microparticles have a diameter of between about 1 pm and about 5 pm, inclusive. In some forms, each microparticle has about 200,000 to about 400,000 first capture agents, inclusive.

[0042] In some forms, the first capture agent is selected from the group consisting of an antibody, a nucleic acid, a protein, a lipid, a carbohydrate, and a small molecule. In some forms, the first capture agent comprises DNA or RNA, or both.

[0043] In some forms, the microparticles are located within an array. In some forms, the microparticles are located within the array at a density of about 100 microparticles per pm 2 In some forms, the array comprises about 1 x 10 4 microparticles to about 1 x 10 6 microparticles, inclusive. In some forms, the array comprises about 4 x 10 5 microparticles. In some forms, the array has an area of about 10 mm 2 to about 50 mm 2 , inclusive, optionally about 25 mm 2 .

[0044] In some forms, the microfluidic chip comprises a plurality of nanoscale objects. In some forms, the nanoscale objects comprise a second capture agent specific for a target biomarker. In some forms, one of the second capture agents binds to one or more of the target biomarkers bound to the first capture agents.

[0045] In some forms, the nanoscale objects further comprise a reporter molecule. In some forms, the reporter molecule comprises a highly bright quantum dot nanoparticle.

[0046] In some forms, the microposts span the height of the inlet channel. In some forms, the capture microfluidic channel comprises a hydrophilic polymer. In some forms, the hydrophilic polymer comprises polyethylene glycol (PEG).

[0047] Methods for detecting a target biomarker in a fluid sample are disclosed. The methods generally comprise (a) introducing a fluid sample into one or more of the microfluidic flow paths of a microfluidic chip as disclosed herein and (b) performing digital chromatography on the chip. In some forms, the fluid sample comprises, or is brought into contact with, a plurality of microparticles and a plurality of nanoscale objects after its introduction. In some forms, the digital chromatography identifies the presence of the target biomarker and / or the amount of the target biomarker in the fluid sample.

[0048] In some forms, step (a) further comprises introducing a control sample comprising a known amount of the target biomarker to a different microfluidic flow path of the same chip.

[0049] In some forms, the performing of digital chromatography of step (b) comprises actuating movement of fluid through the microfluidic flow path in the microfluidic chip. In some forms, the movement filters and washes microparticles within the FTJ structure. In some forms, the filtering of microparticles in the FTJ structure traps microparticles within the FTJ structure and forms an array of microparticles within the FTJ structure.

[0050] In some forms, step (b) further comprises imaging the array of microparticles within the microfluidic chip.

[0051] In some forms, the microparticles comprise microbeads. In some forms, the microbeads comprise magnetic microbeads. In some forms, the microparticles further comprise a first capture agent specific for the target biomarker. In some forms, the first capture agent is conjugated to the microparticles via streptavidin. In some forms, one or more of the first capture agents bind to the target biomarker.

[0052] In some forms, the nanoscale objects comprise a second capture agent specific for the target biomarker. In some forms, one of the second capture agents binds to one or more of the target biomarkers bound to the first capture agents.

[0053] In some forms, the nanoscale objects further comprise a reporter molecule. In some forms, the reporter molecule comprises a highly bright quantum dot nanoparticle.

[0054] In some forms, step (b) further comprises detecting and measuring the target biomarker bound to the first capture agent on the microparticles within the array.

[0055] In some forms, the method further comprises, prior to step (a), (i) incubating the fluid sample with the microparticles for a period of time, and the amount of incubation is effective to bind the target biomarker to the first capture agent. In some forms, after step (i), the microparticles are washed.

[0056] In some forms, the method further comprises, prior to step (a), contacting the microparticles with the nanoscale objects for a period of time, and the amount of contacting is effective to bind the target biomarker to the second capture agent.

[0057] In some forms, steps (a) and / or (b) comprise a total time of between about 10 seconds and 1000 seconds, including endpoints, optionally about 180 seconds.

[0058] In some forms, the method detects at least 90%, e.g., 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99%, or 100% of the biomarker within the fluid sample. In some forms, the fluid sample comprises the biomarker at a concentration of about 1 IU / mL. In some forms, the fluid sample comprises the biomarker at a concentration of about 10 -18 to 10 -5 IU / mL. In some forms, the fluid sample comprises a volume of between about 1.0 μL to about 100 μL, including the endpoints, optionally about 50 μL.

[0059] In some forms, the fluid sample comprises 1 to 100 different biomarkers, optionally 1 to 10 different kinds of biomarkers. In some forms, one biomarker is an immunoglobulin. In some forms, one biomarker is IgE. In some forms, one biomarker is an interferon. In some forms, one biomarker is TNF-a.

[0060] In some forms, the target biomarker within the fluid sample is derived from a bodily fluid from the subject. In some forms, the bodily fluid is selected from the group consisting of blood, sweat, semen, serum, bile, saliva, tears, pus, mucus, pleural fluid, vitreous fluid, spinal fluid, synovial fluid, amniotic fluid, and urine. In some forms, the bodily fluid is tears. BRIEF DESCRIPTION OF DRAWINGS

[0061] Figures 1A-1E is an image providing an overview of single molecule protein detection with BiZi-FIA. Figure 1A is a schematic of a BiZi-FIA chip showing the detection area (FT-JA) conforming to the maximum objective field limit of 6 x 6 mm 2 . Figure 1B depicts a bright field microscope image of a microbead array, imaged area 30 mm 2 . Images were obtained in two conduits to identify "on" and "off" beads. Figure 1C is a diagram of BiZi-FIA chip structure and function showing the two-layer composition of the BiZi-FIA chip, including a hydrophobic flow layer (top) comprising test (T) and control (C) areas, both connected to the same flow input via a double lumen catheter; and a hydrophilic capture layer (bottom). Figure 1D is an expanded view of the hydrophobic flow layer depicted in Figure 1C , showing the direction of fluid flow within and through the test sample and control areas. Figure 1EThis is a flowchart depicting a test region and a control region with a test sample for an exemplary assay, which includes (i) filtration, whereby particles navigate through a fractal filter structure comprising micropillars—this structure promotes constant collisions between microbeads and micropillars, leading to the dissociation of non-specifically adsorbed nanoparticles—the micropillars, having non-specific adsorption properties with hydrophobic surfaces, capture and filter out the nanoparticles; (ii) washing and alignment occur when particles enter the trapping region; the trapping layer is modified with a PEG reagent to exhibit hydrophilic properties in the FT-JA region—this modification facilitates the elution of non-specific nanoparticles and ensures selective retention of target microbeads—single protein molecules are captured on the microbeads and labeled with ultra-bright fluorescent nanoparticles (Qt dot labels). The control region is characterized by a specular reflective structure that serves as the test region. The control region is tested in the control sample by manipulating a single variable; and (iii) when the fluorescence signal detected in the FT-JA is considered background noise, the concentration of the test sample is determined, thereby guiding an adaptive algorithm to minimize systematic error; the control concentration is input and the test sample concentration is output. Keywords for the flowchart are at the bottom.

[0062] Figures 2A-2J A schematic diagram and mechanism of FT-JA for microbead capture are depicted. Figure 2A The diagram shows a schematic of an FT-JA setup, which includes a reservoir and a vent port. Figure 2B The detailed structure of the FT-JA, highlighting its flow resistance (Rf) and trapping resistance (Rt), is shown in the image. A microscopic image showing a sequential array of microbeads trapped by the FT-JA is also included. Figure 2C As shown in the figure. The 3D illustration of FT-JA illustrates the capture process of microspheres in a sequential array, as shown in the figure. Figure 2D As shown; in Figure 2E The diagram depicts the flow and motion of particles within each of the sequential and supplementary arrays, including a simulation of a turbine valve that is centrally positioned to induce controlled hydrodynamic resistance and guide the fluid laterally into a network of vertical inlet and horizontal drain pipes within the trapping section. Figure 2F The diagram describes a highly restricted capture mechanism that effectively guides and captures microbeads within a flow channel. When a microbead enters the flow channel, microbead-1 bypasses an occupied capture section and continues along the path. Microbead-2 replenishes the empty capture channel, followed by microbead-3. Microbeads-4, exceeding the capture capacity, are discharged through the terminal flow channel. Once all eligible capture sections are occupied, the main flow is redirected to a straight flow channel. Subsequent microbeads, unable to enter occupied capture sections, follow the main flow out of the device. Figure 2G The image shows a microscopic image of a sequentially complementary array illustrating the microbead trapping process. Specifically, in... Figure 2HFigures in the middle depict plots of fluid and bead flow within the sequential (test) array, and in Figure 2I Figures in the middle depict plots of fluid and bead flow within the supplemental array. Figure 2J Figures depict simulation results of total capture efficiency and total resistance affected by the magnitude of ΔRt(x,y) and ΔRf(x,y) and the spatial arrangement of x and y values.

[0063] Figures 3A-3K Figures show performance evaluation of the BiZi-FIA system. Simulated pressure gradients driving bi-lateral zigzag flow through the FT-JA ( Figure 3A ); cross-sectional views of flow channels with different widths ( Figure 3B ); simulated 2D multi-zigzag flow paths ( Figure 3C ); and schematic diagrams of 3D single-zigzag and microbead capture in wide and narrow flow channels ( Figure 3D ). Microscopic images of bead arrays in each of the wide ( Figure 3E ), medium ( Figure 3F ), and narrow ( Figure 3G ) channels indicate an increase in array density with narrower channels. Figures 3H-3J are graphs showing capture efficiency ( Figure 3H ) and array density ( Figure 3I ) trends across flow channel widths, and analysis of variance of microbead capture in bi-lateral arrays, indicating high stability across batches, respectively ( Figure 3J ). Simulated 3D multi-zigzag flow paths are depicted in Figure 3K .

[0064] Figures 4A-4C is a schematic diagram showing the process of the BiZi-FIA platform for single molecule detection with automated filtration and washing mechanisms. Figure 4A is a low-chart depicting reagent preparation and incubation in the BiZi-FIA workflow. Figures 4B-4C is a BiZi-FIA schematic showing filtration and washing mechanisms, effective filtration ( Figure 4C ) using hydrophobic and hydrophilic surfaces ( Figure 4B ).

[0065] Figures 5A-5B is a graph of silica bead-enhanced fluorescence for large field-of-view detection, showing a comparison of light intensity between magnetic beads and silica beads at a wavelength of 611 nm, showing a significant increase in signal for silica beads ( Figure 5A ); and a comparison of fluorescence intensity across various magnifications (4X, 10X, 20X, 40X), showing the performance of magnetic beads and silica beads in enhancing signal detection ( Figure 5B ).

[0066] Figures 6A-6D Analysis of BiZi-FIA platform sensitivity and array density under different conditions is shown. Figure 6A is a schematic of the capture antibody labeling method using the SA-biotin system. Figure 6B is a schematic of the reverse setup of the detection antibody labeling method using the SA-biotin system. Figures 6C-6D is a plot of the 400,000 beads used, the change in LOD with different fractions of beads analyzed, showing the best performance with higher fractions and lower objective magnification Figure 6C ; and the relationship between the limit of detection (LOD) in attomoles (aM) and the number of beads used to demonstrate the increased sensitivity Figure 6D ) with higher bead density. As the number of beads used increases above the capture capacity, a certain number of beads will be lost at an accelerated rate, which results in a decrease in the fraction of microbeads analyzed.

[0067] Figures 7A-7I is a plot showing the comparison of sensitivity of silica beads based on BiZi-FIA chip washing and magnetic beads based on traditional magnetic washing. Figures 7A-7C shows the digital immunoassay calibration curves for IgE Figure 7A ), TNF-a Figure 7B ), NFL Figure 7C ) BiZi-FIA; and Figures 7D-7F shows the respective traditional digital immunoassay calibration curves for IgE Figure 7D ), TNF-a Figure 7E ), and NFL Figure 7F ), respectively. The dashed lines represent the calculated limit of detection (LODs). Figures 7G-7I shows the comparison of signal-to-background ratio between silica beads and traditional magnetic beads for both BiZi-FIA and digital immunoassay for each of IgE Figure 7G ), TNF-a Figure 7H ), and NFL Figure 7I ), respectively.

[0068] Figures 8A-8C is a schematic depicting the adaptive differential algorithm, showing the differential mean value theorem for signal interpretation in adaptive correction. The fluorescence intensity versus concentration curve shows the relationship between S0 (background noise), S Q (quantitative sample signal), S C (control sample signal), and S t (test sample signal). The gradient differentials derived from the concentration change (f'(CQ), f'(CO), f'(Q0)) are critical for adaptive correction. The shaded area highlights the difference between the fluorescence units for precise gradient optimization Figure 8A) ; examples of fluorescence signal distribution across different sample types used to train ADNC. Red fluorescent dots represent raw emission signals captured from the sample, while green circles represent signals that exceed a certain threshold and are subsequently identified by the software as valid events. Blank sample (S O ), quantified sample (S Q ), control sample (S C ), and test sample (S t1 , S t2 , S t3 ) show varying signal distribution Figure 8B ) ; and a flowchart outlining the key steps of the ADNC algorithm (including input setup, signal switching, and gradient threshold verification) including a 3D loss function surface plot (L(x)) demonstrating the iterative gradient descent process used to minimize the difference of unknown test sample concentrations (x1, x2, x3). The algorithm ensures optimal calibration of robust signal correction and accurate concentration prediction Figure 8C

[0069] Figures 9A-9J are plots of TNF-a Figure 9A , Figure 9B ) and NFL Figure 9C , Figure 9D ) generated using ADNC software at known concentrations. Linear and accuracy analysis of TNF-a Figure 9E , Figure 9F ) and NFL Figure 9G , Figure 9H ) measurements showing strong correlation between spiked and measured concentrations (R2>0.99). Figure 91 shows a quantitative comparison between the ADNC algorithm and a Poisson-based algorithm, which shows a wider dynamic range achieved with the ADNC algorithm. Figure 9J shows a combined biomarker analysis showing the concentration distribution of IgE, TNF-a, and NFL across cases, highlighting their wide dynamic range and potential for clinical diagnosis of allergic conjunctivitis and related inflammatory conditions.

[0070] Figure 10 is a flowchart of the microfluidic device of BiZi-FIA fabricated using traditional lithography and soft-lithography techniques. After the binding of the capture layer and flow layer, hydrophilic and hydrophobic surface treatment is performed. To distinguish the hydrophobic filter region at the inlet and the hydrophilic array region near the outlet, a hydrophilic reagent will be injected from one of the outlets under sealed inlet conditions, reaching the symmetric end of the outlet. DETAILED DESCRIPTION

[0071] ​The disclosed chip design incorporates overlapping hydrophobic and hydrophilic interfaces to enhance filtration and washing within the chip, thereby improving signal-to-noise ratio and eliminating background noise. The superhydrophobic interface is strategically placed at the inlet of the array and connected filtration microposts, while the hydrophilic interface is located at the center of the array and connected to the outlet at the center of the array.

[0072] The chip features a symmetric design of test and control regions, sharing the same wash buffer inlet on either side. This symmetry in structure facilitates simultaneous introduction of sample and processing in both regions, allowing integrated control and comparative analysis, a unique approach in assay technology.

[0073] The biochip includes a double-layer design, where the upper layer has symmetrically positioned inlets to the particle bifurcated structure and superhydrophobic filtration microposts. This design ensures uniform distribution and flow of particles in the array region. The lower layer includes an interlaced honeycomb mesh structure, facilitating high-density particle array formation and alignment of the upper and lower chip layers without the need for high-precision alignment systems. This double-layer design with specific flow and distribution control is innovative and not obvious to one skilled in the art.

[0074] The design of the chip allows for microbead-based antibody capture and direct modification on the chip surface, demonstrating versatility in applications. It provides excellent dispersion capability for analytes and the ability to control individual variables during testing. This versatility and control are unique in the context of immunoassays.

[0075] The design of the chip allows for strict control of individual variables during the assay. It differentiates the effective signal from the background noise based on the control region reading. If the background noise exceeds the error range of the standard curve, the test is considered invalid; otherwise, the effective signal is calculated by subtracting the background noise from the test signal. This method of signal differentiation and control is both innovative and non-obvious.

[0076] In summary, aspects of the chip include integration of surface chemistry, structural design for filtration and washing, double-layer architecture for particle control, selective surface modification, and versatile application in immunoassay technology with enhanced signal control and differentiation. These features collectively contribute to the distinction of the described system and method from the prior art.

[0077] I. DEFINITIONS

[0078] The term "nucleotide" refers to a molecule comprising a base moiety, a sugar moiety, and a phosphate moiety. Nucleotides are typically linked together by their phosphate moieties and sugar moieties, creating internucleosidic linkages. The base moiety of a nucleotide can be an adenine 9-base (A), a cytosine 1-base (C), a guanine 9-base (G), a uracil 1-base (U), and a thymine 1-base (T). The sugar moiety of a nucleotide is either ribose or deoxyribose. The phosphate moiety of a nucleotide is a pentavalent phosphate. Non-limiting examples of nucleotides are 3'-AMP (3'-adenosine monophosphate) or 5'-GMP (5'-guanosine monophosphate).

[0079] The term "residue" of a chemical species refers to a portion of the chemical species as the resulting product of the chemical species in a particular reaction scheme or subsequent formulation or chemical product, whether or not the portion is actually obtained from the chemical species. Thus, an ethylene glycol residue in a polymer refers to one or more -OCH2CH2O- units in the polymer, whether or not ethylene glycol was used to make the polyester. As another example, in a polymer of monomeric subunits, the incorporated monomeric subunits can be referred to as residues of the un-polymerized monomer.

[0080] The term "nucleotide analog" refers to a nucleotide containing some type of modification to the base, sugar, or phosphate moiety. Modifications to nucleotides are well known in the art and would include, for example, 5-methylcytosine (5-me-C), 5-hydroxymethylcytosine, aminophenyl RNA, benzyl RNA, 5'-phosphor - thioates, and the like. There are many varieties of these types of molecules available in the art and available herein.

[0081] The term "nucleotide surrogate" refers to a nucleotide molecule that has similar functional properties to a nucleotide but does not contain a phosphate moiety. An exemplary nucleotide surrogate is a peptide nucleic acid (PNA). Nucleotide surrogates are molecules that will recognize nucleic acids in a Watson-Crick or Hoogsteen fashion, but are linked together by moieties other than phosphate moieties. Nucleotide surrogates are capable of assuming a double helix type structure when interacting with an appropriate target nucleic acid. Other types of molecules (conjugates) can also be attached to nucleotides or nucleotide analogs to enhance, for example, the interaction with DNA. The conjugates can be chemically linked to the nucleotides or nucleotide analogs. Exemplary conjugates include, but are not limited to, lipid moieties, such as a cholesterol moiety.

[0082] The terms "nucleic acid," "polynucleotide," and "oligonucleotide" are interchangeable and refer to deoxyribonucleotides or ribonucleotides, either naturally occurring or known analogs thereof, in linear or circular conformation, and in either single- or double-stranded form. For the purposes of this disclosure, these terms should not be construed as limiting with respect to the length of a biopolymer. The term can encompass known analogs of natural nucleotides, as well as nucleotides that are modified at the base, sugar, and / or phosphate moieties (e.g., phosphorothioate backbones, locked nucleic acids). Typically, and unless otherwise specified, an analog of a particular nucleotide has the same base-pairing specificity; i.e., an analog of A will base pair with T. When describing double-stranded DNA, the DNA can be described as A-DNA, B-DNA, or Z-DNA, depending on the conformation adopted by the helical DNA. B-DNA, described by James Watson and Francis Crick, is believed to predominate in cells, and extends about 1.5 A per 10 bp sequence A-DNA extends about 1.5 A per 10 bp sequence Z-DNA extends about 2.0 A per 10 bp sequence

[0083] In some cases, nucleotide sequences are provided using the International Union of Pure and Applied Chemistry (IUPAC) recommended character designations or a subset thereof. The IUPAC nucleotide code includes, A = Adenine; C = Cytosine; G = Guanine; T = Thymine; U = Uracil; R = A or G; Y = C or T; S = G or C; W = A or T; K = G or T; M = A or C; B = C or G or T; D = A or G or T; H = A or C or T; V = A or C or G; N = any base; “.” or “-” = gap. In some forms, the character set is (A, C, G, T, U), representing adenosine, cytidine, guanosine, thymidine, and uridine, respectively. In certain forms, the character set is (A, C, G, T, U, I, X, Y, ), representing adenosine, cytidine, guanosine, thymidine, uridine, inosine, uridine, xanthosine, and pseudouridine, respectively. In some forms, the character set is (A, C, G, T, U, I, X, Y, R, N), representing adenosine, cytidine, guanosine, thymidine, uridine, inosine, uridine, xanthosine, pseudouridine, unspecified purine, unspecified pyrimidine, and unspecified nucleotide, respectively.

[0084] The terms “polypeptide,” “peptide,” and “protein” are used interchangeably and refer to polymers of amino acid residues. The terms also apply to amino acid polymers in which one or more amino acid residues are chemical analogs or modified derivatives of the corresponding naturally occurring amino acids.

[0085] The percent (%) nucleotide and / or amino acid sequence identity is understood to mean the percentage of nucleotide or amino acid residues in a candidate sequence that are identical with those in a reference sequence when the two sequences are aligned. To determine percent identity, the sequences are aligned and gaps introduced if necessary to achieve maximum percent sequence identity. Sequence alignment programs to determine percent identity are well known to those skilled in the art. Sequence alignment is typically performed using publicly available computer software such as BLAST, BLAST2, ALIGN2, or MEGALIGN (DNASTAR) software. One skilled in the art can determine appropriate parameters for measuring alignment, including any formulae required for introducing gaps in the sequences being compared. When sequences are aligned, the percent sequence identity of a given sequence A to, and, or for a given sequence B can be calculated as: percent sequence identity = X / Y 100, where X is the number of residues scored as identical by the sequence alignment program or formula for the alignment of A and B, and Y is the total number of residues in B. If the length of sequence A is not equal to the length of sequence B, then the percent sequence identity of A to B will not equal the percent sequence identity of B to A. Mismatches can be similarly defined as differences between naturally associated partners of nucleotides. The number, location, and type of mismatches can be calculated and used for identification or ranking purposes.

[0086] The phrase "specifically binds" to a target means a binding reaction that is determinative of the presence of the molecule in the presence of a heterogeneous population of other biological agents. Thus, under designated immunoassay conditions, the specified molecules preferentially bind to the specified target and do not bind in a significant amount to other biological agents present in the sample. Specific binding to the target by an antibody requires that the antibody be selected for its specificity for the target. A variety of immunoassay formats can be used to select antibodies specifically immunoreactive with the protein. For example, solid-phase ELISA immunoassays are routinely used to select monoclonal antibodies specifically immunoreactive with a protein. See, e.g., Harlow and Lane (1988) Antibodies, A Laboratory Manual, Cold Spring Harbor Publications, New York, for a description of immunoassay formats and conditions that can be used to determine specific immunoreactivity. The term "specifically binds," e.g., between two entities, means at least 10 6 ,10 7 ,10 8 ,10 9 or 10 10 M"1affinity. Preferably greater than 10 8 M"1affinity.

[0087] The term "target molecule" refers to a substance that is desired to be detected and / or quantified, e.g., from a mixture of molecules, including non-target molecules.

[0088] The terms "antibody" and "immunoglobulin" include intact antibodies and binding fragments thereof. Typically, the fragments compete with the intact antibody from which they were derived with respect to specific binding to an antigenic fragment, including separate heavy chains, light chains Fab, Fab' F(ab')2, Fabc, and Fv. The fragments are produced by recombinant DNA techniques, or by enzymatic or chemical separation of intact immunoglobulins. The term "antibody" also includes one or more immunoglobulin chains chemically conjugated to other proteins or expressed as a fusion protein with other proteins. The term "antibody" also includes bispecific antibodies. Bispecific or bifunctional antibodies are artificial hybrids of two different immunoglobulin chains which have different specificities. Bispecific antibodies can be produced by a variety of methods including fusion of hybridomas or linking of Fab' fragments. See, e.g., Songsivilai and Lachmann, Clin. Exp. Immunol., 79:315-321 (1990); Kostelny et al., J. Immunol., 148, 1547-1553 (1992).

[0089] The terms "epitope" and "antigenic determinant" refer to a site on an antigen to which B and / or T cells respond. B cell epitopes can be formed both by contiguous amino acids or noncontiguous amino acids juxtaposed by tertiary folding of the protein. Epitopes formed from contiguous amino acids are typically preserved when the protein is exposed to denaturing solvents, whereas epitopes formed by tertiary folding are typically lost upon denaturing the protein. Epitopes typically include at least 3, and more usually, at least 5 or 8-10 amino acids in a unique spatial conformation. Methods of determining spatial conformation of epitopes include, for example, x-ray crystallography and 2-dimensional nuclear magnetic resonance.

[0090] The term "small molecule" as used herein generally refers to an organic molecule having a molecular weight of less than about 2,000 g / mol, less than about 1,500 g / mol, less than about 1,000 g / mol, less than about 800 g / mol, or less than about 500 g / mol. Small molecules are non-polymeric and / or non-oligomeric.

[0091] The term "bead" or "magnetic bead" refers to a solid structure that serves as a support matrix for one or more reagents when used in a method, e.g., such as a digital immunoassay. The bead can be any suitable bead.

[0092] The terms "wash reagent," "wash buffer," "wash," and "rinsing solution" refer to a solution used to purify and remove one or more reagents from a sample. Typically, a wash buffer is a solvent that effectively solvates and removes reagents from immobilized molecules (e.g., immobilized biomarkers).

[0093] The term "wash conditions" refers to the environmental / external conditions under which a combination with a wash reagent is performed (i.e., a different "wash step"). For example, a wash can be performed by combining one or more wash reagents with a solution containing a biomarker or an immobilized support.

[0094] The terms "microfluidic device," "microfluidics," "microfluidic chip," and "microfluidic platform" refer to any device or system that supports and / or enables or actuates the movement of sub-microliter volumes of fluid. Typically, a microfluidic device enables components and devices for controlling user-defined fluid movement in a controlled manner as well as modifying or changing one or more physicochemical properties (such as temperature, charge, light, magnetic force, etc.). In some forms, a microfluidic device controls the movement, behavior, and manipulation of fluids through one or more mechanisms for actuating fluid movement. Exemplary microfluidic devices actuate fluid movement through mechanisms including continuous flow, fluid dispensing, EWOD, pressure, optical, or combinations thereof. A microfluidic device can be "open" (i.e., containing, moving, and manipulating fluids on a single surface) or "closed" (i.e., containing, moving, and manipulating fluids between two surfaces). In some forms, the term "microfluidic device" is used interchangeably with "microfluidic system" and includes devices for inputting user-defined fluid manipulation controls (e.g., a general-purpose user interface that employs computer software to control the movement of fluids within the device). The term "microfluidic system" also refers to additional devices, such as devices external to the device for controlling fluid movement, e.g., devices for controlling parameters such as temperature, light, pressure, humidity, etc. In some forms, a "microfluidic device" includes devices and systems for inputting data for controlling the movement or manipulation of droplets on a microfluidic platform, which is located proximal to or at a distance from the data input site. In some forms, the data input device is a computer or incorporates a computer. In some forms, the system or device includes one or more systems for providing information to the control system, e.g., devices for providing feedback. In some forms, the data input is autonomous (e.g., can perform computational tasks autonomously, like a program running on a traditional silicon computer, but here in a liquid state).

[0095] Microfluidic chips 100 and methods of making and using the disclosed chips are disclosed. The microfluidic chips 100 generally include a microfluidic platform 110 that includes one or more microfluidic flow paths 120. In some forms, the microfluidic flow path 120 includes an inlet channel 130, a flow-trapping junction (FTJ) structure 140, and an outlet channel 300. In some forms, the microfluidic flow path 120 is configured to move fluid 400 from the inlet channel 130 into the FTJ structure 140 and from the FTJ structure 140 into the outlet channel 300.

[0096] In some forms, the inlet channel 130 is wider at a location where fluid 400 moves from the inlet channel 130 into the FTJ structure 140 than at a location where fluid 400 is introduced into the inlet channel 130. In some forms, the inlet channel 130 contains a plurality of hydrophobic micro-pillar structures 135.

[0097] In some forms, the FTJ structure 140 includes a flow layer 150 and a trapping layer 190. In some forms, the flow layer 150 is in contact with, on top of, and overlapping the trapping layer 190.

[0098] In some forms, the flow layer 150 includes a plurality of flow microfluidic channels 160, each flow microfluidic channel 160 including a top 162, a sidewall 164, and an opening on a bottom 166. In some forms, the surfaces of the flow microfluidic channels 160 are hydrophobic. In some forms, the flow microfluidic channels 160 allow micrometer-scale particles 500 and nanometer-scale objects 600 to pass freely. In some forms, the fluid 400 flows in the same direction in all of the flow microfluidic channels 160.

[0099] In some forms, the flow microfluidic channels 160 are parallel to each other. In some forms, the trapping layer 190 includes a plurality of trapping microfluidic channels 200, each trapping microfluidic channel 200 including a bottom 202, a sidewall 204, and an opening on a top 206. In some forms, the trapping microfluidic channels 200 are parallel to each other. In some forms, the surfaces of the trapping microfluidic channels 200 are hydrophilic. In some forms, the fluid 400 flows in the same direction in all of the trapping microfluidic channels 200.

[0100] In some forms, the flow microfluidic channels 160 are not parallel to the trapping microfluidic channels 200. In some forms, the flow microfluidic channels 160 and the trapping microfluidic channels 200 allow fluid 400 to move from the flow microfluidic channels 160 into the trapping microfluidic channels 200 via the openings on the bottom 166 and the openings on the top 206, respectively.

[0101] In some forms, the capture microfluidic channel 200, the transition from the flow microfluidic channel 160 to the capture microfluidic channel 200, or a combination of both the capture microfluidic channel 200 and the transition from the flow microfluidic channel 160 to the capture microfluidic channel 200, is configured to allow the nanoscale objects 600 to pass through the capture microfluidic channel 200, whereby the passed nanoscale objects 600 flow into the outlet conduit 300.

[0102] In some forms, the capture microfluidic channel 200, the transition from the flow microfluidic channel 160 to the capture microfluidic channel 200, or a combination of both the capture microfluidic channel 200 and the transition from the flow microfluidic channel 160 to the capture microfluidic channel 200, is configured to capture the micrometer-scale particles 500 in the capture microfluidic channel 200, whereby the captured micrometer-scale particles 500 collectively form the array 230 within the FTJ structure 140.

[0103] In some forms, the sidewalls 204 of the capture microfluidic channel 200 are straight and parallel to each other, wherein a height of the capture microfluidic channel 200 is less than a diameter of the micrometer-scale particles 500. In some forms, the height of the capture microfluidic channel 200 is greater than a diameter or a long dimension of the nanoscale objects 600.

[0104] In some forms, the sidewalls 204 of the capture microfluidic channel 200 are not straight, such that a width of the capture microfluidic channel 200 varies in a regular pattern along its length. In some forms, the pattern of width variation forms a constriction 208 in the width of the capture microfluidic channel 200, wherein a width of the constriction 208 is less than a diameter of the micrometer-scale particles 500. In some forms, the width of the constriction 208 is greater than a diameter or a long dimension of the nanoscale objects 600.

[0105] In some forms, all or a subset of the constrictions 208 overlap the flow layer 150 between some or each of the openings 166 on the bottom of an adjacent flow microfluidic channel 160. In some forms, all or a subset of the constrictions 208 overlap the openings 166 on the bottom of some or each of the flow microfluidic channels 160. In some forms, a subset of the constrictions 208 overlap the openings 166 on the bottom of some or each of the flow microfluidic channels 160, and a subset of the constrictions 208 overlap the flow layer 150 between some or each of the openings 166 on the bottom of an adjacent flow microfluidic channel 160. In some forms, a subset of the constrictions 208 overlap the openings 166 on the bottom of each of the flow microfluidic channels 160, and a subset of the constrictions 208 overlap the flow layer 150 between each of the openings 166 on the bottom of an adjacent flow microfluidic channel 160.

[0106] In some forms, the constriction 208 overlapping the opening 166 on the bottom of the flow microfluidic channel 160 forms a small capture inlet 210 on the downward flow side 214 of the opening 206 and a large capture inlet 212 on the downward flow side 214 of the opening 206 for alternating capture of the microfluidic channel 200. In some forms, the small capture inlet 210 is sized smaller than the diameter of the micron-scale particles 500. In some forms, the small capture inlet 210 is sized larger than the diameter or long dimension of the nanoscale objects 600. In some forms, the large capture inlet 212 is sized larger than the diameter of the micron-scale particles 500.

[0107] In some forms, the flow microfluidic channel 160 and the capture microfluidic channel 200 are at a right angle to each other. In some forms, the flow microfluidic channel 160 and the capture microfluidic channel 200 are at an oblique angle to each other. In some forms, the flow microfluidic channel 160 and the capture microfluidic channel 200 are at an angle of 60° to 90°, 70° to 90°, 80° to 90°, 85° to 90°, 87° to 90°, 88° to 90°, or 89° to 90° to each other.

[0108] In some forms, the sidewall 204 of the capture microfluidic channel 200 is angled toward the upward flow end 168 of the flow microfluidic channel 160.

[0109] In some forms, the microfluidic flow path 120 further includes a sample inlet 125. In some forms, the microfluidic flow path 120 is configured for movement of the fluid 400 from the sample inlet 125 into the inlet conduit 130.

[0110] In some forms, the microfluidic flow path 120 further includes a plurality of outlet channels 290, wherein the microfluidic flow path 120 is configured for movement of the fluid 400 from the capture microfluidic channel 200 into the outlet channels 290 and from the outlet channels 290 into the outlet conduit 300. In some forms, each capture microfluidic channel is flowably connected to a different one of the outlet channels 290.

[0111] In some forms, the flow layer 150 of the FTJ structure 140 further includes a plurality of outlet channels 290. In some forms, the outlet channels 290 are interspersed among and parallel to the flow microfluidic channels 160. In some forms, the outlet channels 290 each include a top 292, a sidewall 294, and an opening 296 on a bottom. In some forms, the outlet channels 290 and the capture microfluidic channels 200 allow the fluid 400 to move from the capture microfluidic channels 200 into the outlet channels 290 via the openings 296 on the bottom and the openings 206 on the top, respectively. In some forms, the microfluidic flow path 120 is configured for moving the fluid 400 from the capture microfluidic channels 200 into the outlet channels 290 and from the outlet channels 290 into the outlet tubing 300.

[0112] In some forms, the outlet channels 290 alternate with the flow microfluidic channels 160 in the flow layer 150 of the FTJ structure 140.

[0113] In some forms, the flow layer 150 of the FTJ structure 140 further includes outlet channels 290. In some forms, the outlet channels 290 include a top 292, a sidewall 294, and an opening 296 on a bottom. In some forms, the outlet channels 290 overlap with the downward flow ends 216 of the capture microfluidic channels 200. In some forms, the outlet channels 290 and the capture microfluidic channels 200 allow the fluid 400 to move from the capture microfluidic channels 200 into the outlet channels 290 via the openings 296 on the bottom and the openings 206 on the top, respectively. In some forms, the microfluidic flow path 120 is configured for moving the fluid 400 from the capture microfluidic channels 200 into the outlet channels 290 and from the outlet channels 290 into the outlet tubing 300.

[0114] In some forms, the chip is transparent in one or more regions. In some forms, the chip is transparent in a region corresponding to the array 230. In some forms, the array 230 includes a surface area of 25 mm 2 or less.

[0115] In some forms, the microfluidic platform 110 includes two microfluidic flow paths 120. In some forms, the two microfluidic flow paths 120 are flowably connected to a single fluid reservoir. In some forms, the fluid reservoir is flowably connected to respective inlet tubing 130 of the two microfluidic flow paths 120.

[0116] In some forms, the two microfluidic flow paths 120 are symmetrically disposed on the microfluidic platform 110. In some forms, the two microfluidic flow paths 120 are symmetrically disposed on the chip.

[0117] In some forms, the microfluidic chip 100 further comprises a plurality of micrometer- scale particles 500. In some forms, the micrometer-scale particles comprise microbeads. In some forms, the microbeads comprise magnetic microbeads.

[0118] In some forms, the micrometer-scale particles 500 further comprise a first capture agent 710 specific for a target biomarker 910. In some forms, the first capture agent 710 is conjugated to the micrometer-scale particle via streptavidin. In some forms, one or more of the first capture agents 710 bind to the target biomarker 910.

[0119] In some forms, the micrometer-scale particles 500 have a diameter between about 1 pm and about 5 pm, inclusive. In some forms, each micrometer-scale particle 500 has about 200,000 to about 400,000 first capture agents 710, inclusive.

[0120] In some forms, the first capture agent 710 is selected from the group consisting of an antibody, a nucleic acid, a protein, a lipid, a carbohydrate, and a small molecule. In some forms, the first capture agent 710 comprises DNA or RNA or both.

[0121] In some forms, the micrometer-scale particles 500 are located within the array 230. In some forms, the micrometer-scale particles 500 are located within the array 230 at a density of about 100 micrometer-scale particles per pm 2 In some forms, the array 230 comprises about 1 x 10 4 micrometer-scale particles to about 1 x 10 6 micrometer-scale particles, inclusive. In some forms, the array 230 comprises about 4 x 10 5 micrometer-scale particles. In some forms, the array 230 has an area of about 10 mm 2 to about 50 mm 2 , inclusive, optionally about 25 mm 2 .

[0122] In some forms, the microfluidic chip 100 comprises a plurality of nanometer-scale objects 600. In some forms, the nanometer-scale objects 600 comprise a second capture agent 720 specific for a target biomarker 910. In some forms, one of the second capture agents 720 binds to one or more of the target biomarkers 910 bound to the first capture agents 710.

[0123] In some forms, the nanometer-scale objects 600 further comprise a reporter molecule 800. In some forms, the reporter molecule 800 comprises a high luminescence quantum dot nanoparticle 810.

[0124] In some forms, the micro-pillar structures 135 span the height of the inlet conduit 130. In some forms, the capture microfluidic channel 200 comprises a hydrophilic polymer. In some forms, the hydrophilic polymer comprises polyethylene glycol (PEG).

[0125] A method for detecting a target biomarker 910 in a fluid sample 900 is disclosed. The method generally comprises (a) introducing the fluid sample 900 into one or more of the microfluidic flow paths 120 of a microfluidic chip 100 as disclosed herein, and (b) performing digital chromatography on the chip. In some forms, the fluid sample 900 comprises or is contacted with a plurality of micron-scale particles 500 and a plurality of nanoscale objects 600 after its introduction. In some forms, the digital chromatography identifies the presence of the target biomarker 910 and / or the amount of the target biomarker 910 in the fluid sample 900.

[0126] In some forms, step (a) further comprises introducing a control sample comprising a known amount of the target biomarker 910 to a different microfluidic flow path 120 of the same chip.

[0127] In some forms, the performing of the digital chromatography of step (b) comprises actuating movement of the fluid 400 through the microfluidic flow path 120 in the microfluidic chip 100. In some forms, the movement filters and washes the micron-scale particles 500 within the FTJ structure 140. In some forms, the filtering of the micron-scale particles 500 in the FTJ structure 140 traps the micron-scale particles 500 within the FTJ structure 140 and forms an array 230 thereof.

[0128] In some forms, step (b) further comprises imaging the array 230 of micron-scale particles 500 within the microfluidic chip 100.

[0129] In some forms, the micron-scale particles 500 comprise microbeads. In some forms, the microbeads comprise magnetic microbeads. In some forms, the micron-scale particles 500 further comprise a first capture agent 710 specific for the target biomarker 910. In some forms, the first capture agent 710 is conjugated to the micron-scale particles by streptavidin. In some forms, one or more of the first capture agents 710 bind to one or more of the target biomarkers 910.

[0130] In some forms, the nanoscale objects 600 comprise a second capture agent 720 specific for the target biomarker 910. In some forms, one of the second capture agents 720 binds to one or more of the target biomarkers 910 bound to the first capture agents 710.

[0131] In some forms, the nanoscale object 600 further comprises a reporter molecule 800. In some forms, the reporter molecule 800 comprises a high luminescence quantum dot nanoparticle 810.

[0132] In some forms, step (b) further comprises detecting and measuring the target biomarker 910 bound to the first capture agent 710 on the microscale particle 500 within the array 230.

[0133] In some forms, the method further comprises, prior to step (a), (i) incubating the fluid sample 900 with the microscale particle 500 for a period of time, and the period of incubation is effective to bind the target biomarker 910 to the first capture agent 710. In some forms, following step (i), the microscale particle 500 is washed.

[0134] In some forms, the method further comprises, prior to step (a), contacting the microscale particle 500 with the nanoscale object 600 for a period of time, and the period of contact is effective to bind the target biomarker 910 to the second capture agent 720.

[0135] In some forms, step (a) and / or (b) comprises a total time of between about 10 seconds and 1000 seconds, including endpoints, optionally about 180 seconds.

[0136] In some forms, the method detects at least 90%, such as 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99%, or 100% of the biomarker within the fluid sample 900. In some forms, the fluid sample 900 comprises the biomarker at a concentration of about 1 IU / mL. In some forms, the fluid sample 900 comprises the biomarker at a concentration of about 10 -18 to 10 -5 the biomarker. In some forms, the fluid sample 900 comprises a volume of between about 1.0 pL to about 100 pL, including endpoints, optionally about 50 pL.

[0137] In some forms, the fluid sample 900 comprises 1 to 100 different biomarkers, optionally 1 to 10 different kinds of biomarkers. In some forms, one of the biomarkers is an immunoglobulin. In some forms, one of the biomarkers is IgE. In some forms, one of the biomarkers is an interferon. In some forms, one of the biomarkers is TNF-a.

[0138] In some forms, the target biomarker 910 within the fluid sample 900 is derived from a bodily fluid from a subject. In some forms, the bodily fluid is selected from the group consisting of blood, sweat, semen, serum, bile, saliva, tears, pus, mucus, pleural fluid, vitreous fluid, spinal fluid, synovial fluid, amniotic fluid, and urine. In some forms, the bodily fluid is tears.

[0139] Component reference designations

[0140]

[0141]

[0142]

[0143] II. System for digital immunoassay

[0144] Systems and methods have been developed for in vitro detection and quantification of target biomarkers within a sample using digital immuno-chromatography.

[0145] To overcome the quantitative limitations of traditional lateral flow immunoassays (LFIA) without sacrificing their advantages, a silica-bead based bi-lateral zigzag flow immunoassay (BiZi-FIA) with an adaptive differential noise correction (ADNC) algorithm has been developed as a platform that merges the ease-of-use of lateral flow with the precision of digital immunoassay technology. An overview of an exemplary assay is shown in Figures 1A-1B ).

[0146] The described BiZi-FIA system utilizes a symmetric bi-lateral assay configuration, where test and control samples are introduced from opposite sides of a microfluidic channel, inducing a bi-lateral zigzag flow through a grid array of flow and capture channels (FT-JA). This double-layer FT-JA produces a high density array of beads (e.g., 9.7 x 103beads / mm 2 ) with a capture efficiency of about 93% or more within 180 seconds or less. In some forms, the BiZi-FIA captures beads in an amount greater than a traditional single molecule array, for example, a density that is 1.1 times to 10 times, such as a density that is 5 times.

[0147] The described design employs a symmetric environmental control to ensure consistent sample exposure, enabling real-time concentration determination through an adaptive dynamic noise correction (ADNC). As an advanced machine learning framework, ADNC eliminates the traditional calibration curve requirement through differential signal analysis of test / control channels, thereby revolutionizing the analytical workflow. The algorithm uses gradient descent to optimize dynamic refinement of analysis parameters, iteratively enhancing model precision through accumulation of experimental data. This iterative process establishes a robust correlation between predicted and measured values, accurately resolving a signal gradient of 6 orders of magnitude (0.01-10,000 fM) with atto-molar sensitivity.

[0148] Overall, these integrated innovations establish an effective digital immunochromatographic platform that was rigorously validated through multiplexed detection of three clinically significant biomarkers, immunoglobulin E (IgE), tumor necrosis factor-alpha (TNF-a), and neurofilament light chain (NFL), in a controlled laboratory environment and human tear matrix. The system demonstrated exceptional sensitivity with limits of detection (LOD, 3o standard) of 13 aM (0.013 fM) for IgE, 50 aM for TNF-a, and 810 aM for NFL, representing 2-3 orders of magnitude improvement over traditional lateral flow assays. The complete analytical workflow required only 40 minutes of total processing time: 30 minutes for incubation (target capture / antibody conjugation), followed by <10 minutes for automated bead array imaging and signal quantification, representing an 80% reduction compared to standard digital ELISA protocols. The accuracy of BiZi-FIA was rigorously validated through spike-recovery experiments. For all three biomarkers (IgE, TNF-a, NFL), measured concentrations showed quantitative recoveries of 92-107% in clinically relevant ranges. This demonstrated robustness to matrix effects in complex biological fluids. Key clinical validation using minimally processed human tear samples (2.2 pL volume, 1:32 PBS dilution) confirmed combined diagnostic capabilities with a broader concentration range of multiple biomarkers (n = 3 replicates per sample). The attomolar sensitivity range specifically positions this technology for non-invasive monitoring of chronic inflammatory disorders (via TNF-a / IgE) and neurodegenerative disorders (via NFL), with immediate applications in early dry eye disease diagnosis, real-time tracking of allergic reaction kinetics, and point-of-care neurotrauma assessment.

[0149] This performance profile, combining microsample requirements, rapid processing, and molecular-level sensitivity, addresses key gaps in current diagnostic paradigms. By enabling quantitative multiplexed analysis from non-invasive specimens, this platform establishes a new framework for precision medicine implementation in resource-limited settings.

[0150] A. Compositions for digital immunochromatography (BiZi-FIA)

[0151] Compositions for BiZi-FIA are described that include microfluidic platforms for actuating the detection of one or more target molecules within a fluid sample by digital immunoassay. Generally, the compositions include a microfluidic chip that includes a microfluidic channel and a filtration system that excludes contaminants based on size and charge. In some forms, the BiZi-FIA system utilizes luminescent quantum dot nanoparticles as biomarker labels. In some forms, the BiZi-FIA system utilizes gold or other metal nanoparticles to amplify the detection signal. In some forms, the BiZi-FIA system incorporates a dual-layer flow and capture (FT) junction array that functions as a molecular sieve. For example, in some forms, the BiZi-FIA system includes one or more filters disposed within the microfluidic channel that are sized to selectively retain microbeads carrying target biomarkers and to expel nanoscale particles to improve capture efficiency and selectively minimize non-specific signal interference.

[0152] The described compositions and methods for BiZi-FIA enable easy bead-based accurate analyte assays to improve capture efficiency of target molecule biomarkers and minimize non-specific signal interference. As described in the methods, implementation of BiZi-FIA with high-density FT junction arrays has demonstrated a significant increase in microbead capture efficiency - from 40% to 90% - enabling the arrangement of 4 x 10 2 ) beads on a 2D plane (30 mm 5 ) that facilitates fast imaging and accurate analysis of a comprehensive range of target biomarkers within a sample with a 6.25 mm field of view diameter (Nikon ECLIPSE Ti2, 20 mm FOV).

[0153] The design of the chip allows for bead-based antibody capture and direct modification on the chip surface, demonstrating versatility in applications. The design of the chip provides excellent dispersion capability for the analyte and the ability to control individual variables during testing. This versatility and control is unique in the context of immunoassays.

[0154] The design of the chip allows for strict control of individual variables during the assay. It differentiates the effective signal from the background noise based on the control region reading. If the background noise exceeds the error range of the standard curve, the test is considered invalid; otherwise, the effective signal is calculated by subtracting the background noise from the test signal. This method of signal differentiation and control is both innovative and non-obvious.

[0155] The design of the chip allows for aspects of the chip to include integration of surface chemistry, structural design for filtration and washing, dual-layer architecture for particle control, selective surface modification, and versatile application in immunoassay technology with enhanced signal control and differentiation. These features collectively contribute to the distinction of the described system and method from the prior art.

[0156] BiZi-FIA platforms, e.g., chips, are described that are designed according to the requirements of the BiZi-FIA method for detecting and measuring biomarkers within a biological sample. Also described are flow phase matrices, e.g., microparticles, for use in the device. Also described are target biomarkers and capture agents for specifically binding to target biomarkers. Also described are reporter molecules, e.g., nanoscale fluorescent markers or labels for detection.

[0157] (i) BiZi-FIA platform design

[0158] In some forms, the system for BiZi-FIA is implemented within a standalone unit, such as a plate, biochip, or chip or array. In exemplary forms, the system for BiZi-FIA is implemented within a single chip. The chip generally includes a platform for forming an array of microbeads, including:

[0159] (i) one or more microfluidic channels for directing fluid within and through the chip;

[0160] (ii) a flow-capture junction filtration structure (including a size exclusion (capture) filtration system and a hydrophilic (flow) filtration system and a hydrophobic filtration system);

[0161] The flow-capture junction filtration structure is implemented within the microfluidic channel, and the capture portion collects microparticles for visualization.

[0162] In some forms, the BiZi-FIA includes two or more different layers of composition, e.g., an upper layer and a lower layer combined to form a defined microfluidic channel.

[0163] Generally, at least a portion of the BiZi-FIA includes an area that allows for visualization, e.g., by light microscopy. Generally, the portion of the BiZi-FIA that includes the capture area will produce an array of concentrated microparticles that will be imaged, e.g., by light microscopy.

[0164] In some forms, all or a portion of at least one layer or section of the BiZi-FIA device is formed from or includes a polymer, such as an inert polymer. In some forms, all or a portion of the BiZi-FIA device is formed from or includes a metal. In some forms, all or a portion of the BiZi-FIA device is formed from or includes a glass. In some forms, all or a portion of the BiZi-FIA device is formed from or includes an opaque structure that does not allow light to pass through the structure. In some forms, the BiZi-FIA device is formed from or includes a transparent material, e.g., that allows light of one or more different wavelengths to pass through the device.

[0165] Generally, the BiZi-FIA device is configured to impart uniform distribution and / or flow of particles through the microfluidic channel.

[0166] (a) microfluidic channel

[0167] Exemplary chips are configured to include one or more microfluidic channels for directing fluid through the chip. Generally, each chip includes at least two microfluidic channels. Each microfluidic channel includes an inlet and an outlet. Generally, the microfluidic channel connects the inlet to the outlet. In some forms, the microfluidic channel includes one or more intermediate sections between the inlet and the outlet. Generally, the inlet of the microfluidic channel is connected to one or more fluid reservoirs. Exemplary fluid reservoirs include wash buffers.

[0168] In some forms, each microfluidic channel of a chip having multiple microfluidic channels includes an inlet, whereby the inlet is opened to add a sample into the microfluidic chamber. Thus, in some forms, the inlet includes a well sized to accommodate a pipette tip.

[0169] In some forms, the chip design includes one or more first microfluidic channels for directing a test sample through, and one or more second microfluidic channels for directing a control sample through. Exemplary control samples are control samples that include a known amount and / or concentration of at least one known biomarker. In some forms, the one or more first microfluidic channels and the one or more second microfluidic channels share the same wash buffer inlet on either side. For example, in some forms, the chip design includes one or more "test" microfluidic channels for directing a sample through and one or more "control" microfluidic channels for directing a control sample through arranged in a symmetric pattern on either side of the same chip. In some forms, the symmetry in the structure facilitates simultaneous introduction and / or processing of samples in both microfluidic channels, allowing integrated control and comparative analysis. In some forms, the structure of the chip is configured to facilitate simultaneous quantification of a control sample and a test sample in similar microfluidic chambers on two structures, the microfluidic chambers being positioned opposite each other on the chip in a symmetric configuration. In some forms, the chip is configured such that the flow and distribution of microparticles within similar test and control microfluidic chambers of the chip are equivalent.

[0170] (b) Bi-directional zig-zag flow directed high density microbead capture (flow / capture junction array)

[0171] In exemplary chips, the microfluidic channels incorporate flow- capture junctions (FTJs) for filtering and washing solid phase particles within the BiZi-FIA device.

[0172] In single-molecule protein detection, the capture efficiency of microbeads within a limited detection area is crucial due to the low concentration of target proteins. Efficient microbead capture is essential to enhance the reliability of the measurement and maintain the sensitivity of the assay. Inadequate capture can lead to significant errors, such as underestimation of protein concentration and false negatives. To address this challenge, BiZi-FIA utilizes the FT-JA function as a refined molecular sieve, characterized by overlapping flow and capture layers that separate and localize target molecules within a confined space Figure 2A ). Each flow channel is connected to both sides of the inlet duct through branching structures in the upper layer, while each capture channel is connected to both sides of the outlet through reservoirs in the lower layer Figure 2B ). The cross-FT junction, composed of interlaced straight flow channels and capture channels, generates a narrowed recessed area that serves as the main capture section for microbeads, creating a zigzag flow path for the microfluidic system. In terms of microbead loading, when the capture section is empty, it provides lower flow resistance compared to the flow direction, causing microbeads to enter the capture channel and be captured Figure 2C ). Once inside, the captured microbeads act as c, significantly increasing the flow resistance within the capture section and thus redirecting the main flow stream back into the flow channel. Subsequent microbeads are guided along the flow channel, bypassing the occupied capture section and seeking the next available capture section with a low-pressure area Figure 2D ).

[0173] The primary purpose of the FTJ is to capture micron-sized solid-phase particles (i.e., microbeads) for efficient washing and filtration. Typically, the FTJ is configured to uniformly distribute biomarkers following a Poisson distribution within a confined area. Typically, the arrangement of the FTJ facilitates efficient separation and precise spatial localization of target biomarkers for super-bright fluorescent labeling.

[0174] The flow and capture junction array (FTJA) is a high-density filtration system characterized by its overlapping flow and capture layers.

[0175] The fluid dynamics in the flow and capture junction array (FTJA) allow for simultaneous transportation and immobilization of a large number of particles. The primary purpose of the FTJA is to facilitate the smooth and efficient guidance of microspheres into capture capture sections by utilizing slightly inclined branches. Capturing and immobilizing microbeads in the FTJA is achieved by introducing forces opposite to their flow direction, resulting in localized areas of increased flow resistance.

[0176] The filter-like flow and capture junction concept requires a strategic arrangement of flow and capture ducts to generate a directional flow pattern that guides microspheres towards effective capture capture sections. Inspired by the asymmetric behavior of filtration devices, the FTJA exhibits a favorable flow pattern that allows microspheres to easily enter capture regions while minimizing backflow, thereby improving capture efficiency.

[0177] To facilitate smooth and easy guiding of particles towards the capture traps, a tilted support flow and trap structure is developed instead of being perpendicular to the main flow direction. This design criterion ensures that microspheres encounter reduced drag during transport, thereby facilitating smooth movement towards the capture traps. The tilted carrier flow direction provides a balanced trade-off between minimizing drag and maximizing trapping efficiency, contributing to successful microsphere immobilization.

[0178] By arranging the flow and trap conduits at different horizontal levels and establishing vertical connections, the FTJA enables parallel multi-conduit operation. This configuration allows simultaneous trapping of multiple microspheres and enhances the overall trapping capacity, which is crucial for various applications requiring large-scale microsphere operations. By aligning the trapping direction with the support flow direction, trapping efficiency is optimized while minimizing the drag experienced by microspheres. This design ensures that particles undergo minimal energy loss during trapping, resulting in more efficient and stable immobilization within the traps.

[0179] The described high-density filter-like flow and trap junctions offer several key advantages. First, this design improves trapping efficiency, ensuring a high capture rate of microspheres passing through the device. Second, reduced fluidic drag provides smoother particle guidance, reducing the likelihood of particle escape or clogging. Additionally, the junction's filter-like behavior promotes unidirectional flow, minimizing backflow and improving the reliability and reproducibility of the overall system. To achieve high throughput and accommodate large numbers of particles, a high-density and parallel multi-conduit design is implemented.

[0180] This involves layering the flow and trap conduits at different horizontal levels and establishing vertical connections. As a result, the device can simultaneously trap multiple microspheres, ensuring efficient use of available trapping area and enhancing the overall particle trapping capacity.

[0181] In some forms, the FT junction array comprises two distinct layers: a "flow" layer of a hydrophobic PDMS surface and a "trap" layer with a PEG-coated surface.

[0182] Typically, the upper flow layer comprises a number of parallel flow conduits, while the lower trap layer is composed of an array of parallel trap conduits. In some forms, these two layers are combined together to produce a flow and trap junction array.

[0183] Each flow and trap junction is made from a straight flow conduit paired with a recessed trap conduit. These conduits are interwoven to form a single FT junction. Typically, the narrowed recessed region underneath the straight flow conduit serves as the primary trapping site.

[0184] Typically, each flow conduit is connected to an inlet conduit with a branching structure, while each capture conduit is directly connected to an outlet. When a capture is empty, the capture conduit provides lower flow resistance compared to the flow conduit. Once inside, the microbeads act as a barrier, significantly increasing the flow resistance within the capture conduit. This forces the main current back into the flow conduit. Any subsequent microbeads are then directed along the flow conduit, effectively bypassing any occupied captures, and actively seeking the next available capture.

[0185] In some forms, the outlet conduit of the device includes one or more turbine valves configured to provide a directed fluid flow within the outlet conduit. These valves are referred to as "Tesla-type" turbine valves. In some forms, each flow microfluidic channel includes an end region proximate to the outlet conduit that is configured to include a curvature at the end of the flow microfluidic channel. Typically, the curvature is less than 90 degrees and is uniform across all microfluidic channels within the device. The curvature is typically sufficient to direct fluid flow out of the FTJ in a direction opposite to the direction of the directed fluid flow within the outlet conduit. Thus, in some forms, the opposite flow directionality provides a resistance within the flow microfluidic channel. Typically, the resistance is an amount that is effective to direct fluid lateral motion through the capture microfluidic channels within the FTJ. Figure 2E Opposite flow resulting from the curved configuration of the turbine valve / flow channel is depicted in the middle.

[0186] (1) FTJ Design Parameters

[0187] The concept and design criteria of high-density filter-like flow and capture junctions generally provide a solution for efficient microsphere capture and immobilization within microfluidic devices.

[0188] By exploiting the isotropic nature of spherical microbeads and strategically arranging flow and capture conduits, the FT junction array ensures efficient microsphere capture, providing a tool for a variety of applications, including biological panel diagnostics, cell sorting, and drug delivery systems.

[0189] In some forms, the FTJ includes multiple overlapping hydrophobic and hydrophilic interfaces, for example, adapted to enhance filtration and washing of microparticles within the chip. The structure of the filtration system also includes "captures" that block microparticles from passing at a single location, but allow nanoscale particles to pass. The capture structure concentrates microparticles within an area sized to image the concentrated microparticles in a single frame of a microscope.

[0190] To ensure efficient microbead capture of the double-sided zigzag flow, the electrical resistance in the capture direction (Rt) should be less than the electrical resistance in the flow direction (Rf). By calculating and assigning these resistance ratios, the design of the flow and capture channels can be directed to optimize capture efficiency and flow performance. The electrical resistance in the flow direction is defined as R f(x,y) , and the electrical resistance in the capture direction is defined as Rt(x,y) ,like Figure 2B As shown. As boundary conditions of the FT-JA coordinate system, the origin (0,0) represents the final FT junction closest to the outlet, where the minimum resistance is denoted as R. f (0,0)=ΔR fo and R t (0,0)=ΔR to These values ​​are influenced by the geometry of the flow and trapping units, respectively [23,24]. Considering the parallelism between the flow and trapping directions at each junction, the resistance R at a junction... j(x,y) It is expressed as:

[0191]

[0192] For positions where x≥1 and y≥0, the resistance R in the flow direction is... f(x,y) The resistance R at the previous junction along the x-axis (flow direction) j(x-1,y) and the resistance ΔR of the current flow cell f(x,y) (It is determined by the geometry of the flow element at (x,y):

[0193]

[0194] Similarly, for x≥0 and y≥1, the resistance R in the capture direction t(x,y) From the previous engagement R along the y-axis (capture direction) j(x,y-1) and the resistance ΔR of the current capture unit t(x,y) (It is determined by the geometry of the captured unit at (x,y))

[0195]

[0196] Based on these recursive relationships, the capture efficiency of FT-JA in the BiZi-FIA system can be determined by the resistivity R between the capture and flow directions. t (x,y) / R f Predict using (x,y). When R... t (x,y) / R f When (x,y)<1, the corresponding junctions effectively function to capture microbeads. This is demonstrated through equivalent circuit simulation (…). Figure 2B Calculate the total resistance R of FT-JA. total .

[0197]

[0198] Simulation results (Table 1) show that maintaining a higher number of trapping channels relative to the flow channels reduces R by minimizing the resistance in the trapping direction. totak This is beneficial for microbead capture. Specifically, ΔRt (x,y) < AR f The scenario of (x,y) achieves maximum capture efficiency. To facilitate this, we designed a tilted capture channel that preferentially directs flow lines into the capture layer. In contrast, a vertical capture channel increases resistance and hinders capture efficiency. Thus, the tilted capture channel design optimizes capture efficiency within the BiZi-FIA system. Figure 2F

[0199] In some forms, for laminar flow in the microchannel, the pressure drop is analyzed using the Hagen-Poiseuille equation for a cylindrical pipe, which represents the pressure drop due to viscous flow in a cylindrical pipe. The Darcy friction factor, f, is related to the aspect ratio, a, and the Reynolds number, Re = pVd / m, where m is the fluid viscosity, p is the fluid density, V is the average velocity of the fluid, and D is the hydraulic diameter. The aspect ratio, a, is defined as the ratio of the smaller dimension (height, H) to the larger dimension (width, W) of the channel, a = H / W, such that 0 < a < 1. The product of the Darcy friction factor, f, and the Reynolds number, Re, is a constant that depends on the aspect ratio, i.e., f- Re = C(a), where C(a) represents a constant as a function of a. After simplification, the resulting expression is:

[0200]

[0201] where L is the length of the channel.

[0202] In exemplary forms, the FTJ includes a hydrophobic layer and a hydrophilic layer, each layer filled with equidistant structures. Typically, the two layers of structures are arranged vertically, forming a high-density FT junction array. The FT junction array allows high-resolution identification of target biomarkers with super-bright fluorescent nanoscale labels (nanoparticles of quantum dots). The FT junction array provides a high-resolution, high-efficiency, and high-throughput single-molecule array.

[0203] Typically, the tilted capture junction optimizes the injection performance of mechanical pumps and pipettes under low flow resistance conditions. For example, in some forms, during operation, the particle suspension enters the side channel on the capture layer from the main channel on the flow layer. Due to the presence of the tilted plane, only particles with a diameter smaller than the height of the tilted plane can pass through the side channel, while particles with a diameter larger than the height of the tilted plane will be blocked at the entrance of the side channel and eventually concentrated in the main channel. This structure enables precise sorting and controlled concentration of high-density particle arrays. When the flow direction is biased, the original particle array can be collected again. Thus, the microbeads are repeatedly arranged, washed, and resuspended in the chip with low flow resistance.

[0204] Typically, the size of the entire area surrounding the flow-capture junction structure is designed to conform to the maximum objective field of view of a brightfield microscope, such as about 30 mm 2 ​To capture images including, for example, two or more microbead arrays. Typically, the concentrated microbeads within the capture structure of a single array occupy a total area of about 2.5 x 2.5 mm. For example, in some forms, for brightfield microscopy, the array occupies an area of about 2.5 x 2.5 mm for imaging within a single frame. Typically, images are obtained in two passes to identify "on" and "off" beads.

[0205] (2) Drainage channels / exit

[0206] The described FTJ includes a plurality of drainage channels for passing fluid through the microfluidic device. Typically, the channels overlap with the access channels, forming rows of a grid-like array of chambers. Thus, in some forms, the structure forms a filter-like bead separator system. When using typical fluidic dynamics restrictions to displace liquid in the system, the device provides a convenient and efficient platform to transport and immobilize microbeads, inject reagents, and arrange microbeads in an array fashion as required for many biological analysis applications.

[0207] In some forms, the chip structure incorporates a design optimized to efficiently eliminate non-specific molecular impurities. This is achieved by having interlocking comb-like flow channels and drainage channels. These channels are arranged in an overlapping and interlaced pattern on the same plane, forming a grid-like array of chambers within the capture channel. This complex layout optimizes the separation and removal of undesired molecules, enhancing the precision and effectiveness of the immunoassay process.

[0208] The designed unidirectional microcapillary flow allows for the simultaneous transportation and immobilization of many microbeads in the chip. Parallel perfusion of multiple channels is more efficient compared to single-channel perfusion with negative injection pressure. Typically, the drainage channels are parallel to the extension direction of the array channels. That is, the extension direction of the guide channels and the capture channels are uniform. Typically, the array chambers capture particles in the liquid, and excess liquid flows into the drainage channels.

[0209] Drainage chambers

[0210] In some forms, the drainage channels include drainage chambers, which are, for example, liquid storage tanks located on the left and / or right, configured to store liquid after passing through the array chambers.

[0211] In some forms, the drain channel has a row of 3D dimples. When the cross-section of the dimples is semi-elliptical and tilted, shear forces are more likely to shear the liquid and the liquid enters the dimples. Bubbles and dead zones are difficult to create in tilted dimples compared to vertical dimples. In general, tilted channels can reduce pinning effects better than vertical channels, enabling faster liquid transport. In some forms, the tilted channels provide the ability to achieve spontaneous flow of liquid using three-dimensional surface energy gradients and Laplace pressure differences, such that the liquid fills the reservoir under the capillary forces of the structure of the bionic Nepenthes peristome, without the need for complex mechanical injection pump equipment, with the features of simple operation and high repeatability.

[0212] Upon entering the middle array region, the single molecule immune complexes captured by the magnetic beads are subjected to contrasting properties of the surface of the array region. The hydrophilic and anti-static adsorption properties of this region, in contrast to the hydrophobic properties of the flow separation zone, effectively prevent the non-specific adsorption of quantum dot microspheres in the array region. As a result, they continue to be expelled from the chip with hydrodynamics. The chip designed specifically for this purpose effectively removes the remaining non-specific impurities in the separation zone of its flow layer. The design of the chip takes advantage of the hydrophobic properties of polydimethylsiloxane (PDMS), ensuring the effective removal of non-specifically bound proteins and other impurities. This design significantly improves the signal-to-noise ratio and sensitivity of the detection.

[0213] Due to the combination of surface interactions (electrostatic, van der Waals, steric, hydrophobic, and hydration forces), most of the seeded quantum dot nanospheres adhere to the microwell surface and therefore do not show Brownian motion.

[0214] (3) Hydrophobic interface

[0215] In an exemplary chip, the microfluidic channel contains a plurality of hydrophobic interfaces. The hydrophobic interfaces generally include a filtering fractal structure that includes a plurality of microposts; in operation, particles (e.g., microparticles) navigate through the filtering fractal structure, which promotes constant collisions of the microbeads with the microposts, resulting in dissociation of non-specifically adsorbed nanoparticles.

[0216] In some forms, the plurality of hydrophobic (e.g., superhydrophobic) structures are in the form of micro-pillar structures. Exemplary micro-pillars span all or a portion of the distance between the top and bottom of the microfluidic channel. Thus, in some forms, the micro-pillars of the plurality of micro-pillars include structures that connect the top and bottom of the microfluidic channel. In other forms, the micro-pillars span at least 50% of the distance between the top and bottom of the microfluidic channel. In some forms, the size and shape of the micro-pillars, and the location thereof, are adapted to impede and / or alter the passage of microparticles within the microfluidic channel, but not to prevent the passage of microparticles. In some forms, the shape of the micro-pillars is circular, such that a microparticle contacting the micro-pillar will skim across the micro-pillar, such that the trajectory of the microparticle is altered after contact with the micro-pillar. Thus, in some forms, the plurality of micro-pillars are positioned in a manner that directs the motion of microparticles flowing within the microfluidic channel.

[0217] Generally, the micro-pillars are formed of a hydrophobic material and have hydrophobic fluidic properties. In some forms, the micro-pillars include a hydrophobic surface that imparts non-specific adsorption properties. Thus, in some forms, the micro-pillars include a hydrophobic surface to capture and filter out non-specifically bound particles, such as nanoparticles. The plurality of micro-pillars located within a region of the microfluidic channel form a microfluidic hydrophobic flow layer.

[0218] In some forms, the filtration system includes a plurality of symmetrically positioned inlets to one or more particle bifurcation structures and / or a plurality of hydrophobic (e.g., superhydrophobic) filtration micro-pillar structures. In some forms, the filtration system includes a hydrophobic flow layer. In some forms, the hydrophobic flow layer provides a water collection device that facilitates the formation of high density microbead arrays for imaging.

[0219] (4) Hydrophilic Interfaces

[0220] In some forms, the microfluidic channel incorporates a plurality of hydrophilic interfaces that form a “trapping” or porous mesh structure to selectively retain microparticles while allowing elution of smaller particles, such as nanoscale particles. Exemplary trapping portions include an interlaced regular or irregular honeycomb mesh structure. The mesh structure is sized and configured such that microbeads (e.g., moving through the microfluidic channel from an inlet and into the mesh structure in the direction of microfluidic flow) become stuck within the mesh. Thus, the size of each hole or space within the mesh is designed to retain micrometer scale particles. The trapping structure thereby facilitates high density particle / array formation. The mesh structure is or includes a hydrophilic coating that imparts hydrophilic properties to the mesh or trapping portion structure. Generally, the hydrophilic trapping layer is modified with a PEG reagent and exhibits hydrophilic properties in the FT junction array region. This modification allows for elution of non-specific nanoparticles, ensuring selective retention of target microbeads.

[0221] In some forms, the compositions and methods distinguish hydrophobic filter inlets by modifying and perfusing the hydrophilic reagents, as shown. The method is applicable to a wide range of different spatially reflective symmetric microfluidic biochip designs. The described methods employ techniques focused on changing the chemical properties of specific regions of the microfluidic biochip to ensure efficient separation and analysis, tailored specifically for microfluidic biochips with spatially reflective symmetric designs in digital immunoassays.

[0222] Hydrophilic polymers

[0223] In some forms, the described plurality of hydrophilic interfaces within the apparatus for performing BiZi-FIA include a plurality of surfaces coated with or otherwise comprising a hydrophilic polymer. Any hydrophilic polymer can be implemented within the hydrophilic components of the apparatus, including poly-beta-amino esters and 1,2-amino alcohol lipids. In some embodiments, the polymer is an alkyl-modified polymer, such as an alkyl-modified poly(ethylene glycol). Other exemplary polymers include poly(alkylene glycol), polysaccharides, poly(vinyl alcohol), polypyrrolidone, polyoxyethylene block copolymers (e.g., PLURONICS®, FICOLL®, and KOLLIPHIC®), polyethylene glycol (PEG) and copolymers thereof. In some forms, the hydrophilic polymer includes, but is not limited to, poly(alkylene glycol)s such as polyethylene glycol (PEG), poly(propylene glycol) (PPG), and copolymers of ethylene glycol and propylene glycol, poly(oxyethylated polyols), poly(olefinic alcohols), poly(polyvinylpyrrolidone), poly(hydroxyalkyl methacrylamides), poly(hydroxyalkyl methacrylates), poly(saccharides), poly(amino acids), poly(hydroxy acids), poly(vinyl alcohol), copolymers, terpolymers, and mixtures thereof.

[0224] In some forms, one or more of the hydrophilic polymer components contain poly(alkylene glycol) chains. The poly(alkylene glycol) chains can contain 1 to 500 repeating units, more preferably 40 to 500 repeating units. Suitable poly(alkylene glycol)s include polyethylene glycol, poly 1,2-polypropylene glycol, poly(propylene oxide), poly 1,3-polypropylene glycol, and copolymers thereof. In some forms, one or more of the hydrophilic polymer components is a copolymer containing one or more polyethylene oxide (PEO) blocks and one or more blocks composed of other biocompatible polymers (e.g., poly(lactide), poly(glycolide), poly(lactide-co-glycolide), or polycaprolactone). One or more of the hydrophilic polymer segments can be a copolymer containing one or more PEO blocks and one or more blocks containing polypropylene oxide (PPO). Specific examples include triblock copolymers of PEO-PPO-PEO, such as POLOXAMERS TM and PLURONICS TM ​In some forms, the hydrophilic polymer includes one or more moieties that impart different structural and functional properties to the polymer. For example, in some embodiments, one or more hydrophilic polymers are modified by the addition of polypeptides or other small molecules. The modified hydrophilic polymer can be used to impart one or more different functional or structural properties to the hydrophilic component of the BiZi-FIA device, as compared to the same device in the absence of the modification. Exemplary functional or structural properties include changes in the hydrophilicity and binding selectivity of the hydrophilic component of the BiZi-FIA device.

[0225] Polyethylene glycol (PEG)

[0226] In some forms, the hydrophilic polymer is or includes polyethylene glycol (PEG). PEG is one of the most commonly used hydrophilic polymer reagents. The size, relative amount, and distribution of amphiphilic PEG included in the MDNP can influence the biophysical characteristics of the resulting modified dendrimer-based nanoparticle (MDNP), such as structural characteristics and charge density. In some forms, one or more physical properties of the hydrophilic interface are directly related to the size, relative amount, and distribution of PEG used to coat the interface (i.e., the properties and extent of PEGylation imparted to the device). Exemplary properties that can be altered include the rate and efficacy of retention of therapeutic, prophylactic, and diagnostic agents, as well as charge neutralization of the microparticles in the hydrophilic region of the device.

[0227] In some forms, the PEG includes short chain oligoethylene glycols. Exemplary oligoethylene glycols include diethylene glycol, triethylene glycol, tetraethylene glycol, pentaethylene glycol, hexaethylene glycol, and the like.

[0228]

[0229] Formula I: Repeat unit of short chain oligoethylene glycol (n = 1-6) PEG monomer.

[0230] In some forms, the hydrophilic polymer is or includes monomethoxypolyethylene glycol (mPEG). In certain embodiments, the PEG or mPEG is branched or "multi-armed" PEG. In some forms, the hydrophilic polymer is or includes polyethylene glycol polymers having different molecular weights. For example, the PEG can have a molecular weight between about 100 Da (i.e., PEG 100 Da) and about 12,000 kDa (i.e., PEG 12 kDa), inclusive. In some forms, the hydrophilic polymer is or includes a single species of PEG, or PEG from two or more different species. In some forms, the hydrophilic polymer is or includes a single polymer species, or a mixture of multiple different polymer species. In some forms, the hydrophilic polymer is modified, e.g., with one or more adducts. For example, the hydrophilic polymer can be modified with the same or different adducts. In some forms, the hydrophilic polymer is or includes the amphiphilic polymer DSPE-mPEG. In some forms, the hydrophilic polymer is or includes DSPE-mPEG molecules having different molecular weights of mPEG, e.g., DSPE-mPEG(350); DSPE-mPEG(550); DSPE-mPEG(750); DSPE-mPEG(1000); DSPE-mPEG(2000); DSPE-mPEG(3000); or DSPE-mPEG(5000). The lipid component can include saturated or unsaturated fatty acid moieties.

[0231] (5) High-density microbead array

[0232] (HDBA)

[0233] The BiZi-FIA device includes one or more structures that concentrate a plurality of microbeads within one or more defined regions of a microfluidic channel to form a microbead array. In some forms, the array is a high-density bead array (HDBA). The described BiZi-FIA system uses HDBA to capture and isolate bead-based immuno-complexes and identify them with super-bright fluorescent labels. Typically, each bead filter (BD) is an FT junction node between a hydrophobic conduit and a hydrophilic conduit with a height difference. The FT junction precisely controls single-bead immobilization of a specific size and filtration with a specific target molecule. Thus, compared to traditional digital Elisa, BiZi-FIA with HDBDA is able to process samples without pumps, reduce cost, and enhance sensitivity.

[0234] The array is typically formed by immobilizing a solid phase substrate (e.g., microbeads) within the networked "capture" structure of the BiZi-FIA. In exemplary forms, the microbead array includes about 1 x 107to about 1 x 1010microbeads on a 2D plane (e.g., an area of about 5 x 5 mm 2 4 6 ​​A single bead, for example, in a 2D plane (e.g., approximately 5×5mm). 2 The area includes approximately 4×10 5 Individual beads are used to achieve imaging and analysis. In some configurations, the position and size of the array region correspond to a field of view diameter of approximately 6.25 mm (e.g., using Nikon ECLIPSE Ti2, 20 mm FOV).

[0235] The number of possible multiplex measurements is limited by the array size, the camera field of view, and the ability of the optical system to reliably distinguish unique fluorescence signals on the array. In some configurations, the upper limit for the number of apertures that can be imaged using a CCD camera is approximately 200,000. Therefore, each measurement typically yields approximately 200,000 apertures to maintain the dynamic range of multiplex measurements.

[0236] (6) Multi-layer structure

[0237] In some forms, the chip includes a multilayer structure. For example, in some forms, the chip includes a two-layer structure. An exemplary two-layer structure includes a first layer and a second layer. In an exemplary form, the first layer is the upper layer, and the second layer is the lower layer. For example, in some forms, the upper layer is positioned directly on top of or directly above the lower layer. For example, in some forms, the upper layer contacts the lower layer. Thus, in some forms, the lower surface of the upper layer contacts the upper surface of the lower layer to form a two-layer structure. In some forms, the upper and lower structures of the two-layer chip have the same or different dimensions. For example, in some forms, the upper and lower structures of the chip include the same or different surface areas. In some forms, the upper and lower structures of the chip are combined to form the top and bottom of one or more microfluidic channels.

[0238] Exemplary first-layer structure

[0239] In some forms, the first layer includes multiple symmetrically positioned inlets leading to one or more particulate bifurcated structures and / or multiple hydrophobic (e.g., superhydrophobic) filter micropillar structures. In some forms, the first layer is a hydrophobic flow layer. In some forms, the first layer is an upper layer. Therefore, in some forms, the upper layer is a hydrophobic flow layer.

[0240] Exemplary lower structure

[0241] In some forms, the second layer incorporates a hydrophilic interface component of the RTJ structure within the BiZi-FIA device. For example, in some forms, the lower layer is combined with the upper layer to form a "trap" or porous mesh structure that selectively retains microparticles while allowing elution of smaller particles, such as nanoscale particles. Exemplary traps include staggered regular or irregular honeycomb mesh structures with staggered honeycomb mesh structures. When the second layer includes a staggered honeycomb mesh structure, the second layer facilitates high density particle array formation. Thus, in some forms, the second layer is a hydrophilic trapping layer.

[0242] (ii) Exemplary BiZi-FIA Chip Design

[0243] An exemplary platform for BiZi-FIA is a biochip for single molecule protein detection that includes a two-layer structure with a first (upper) hydrophobic flow layer and a second (lower) hydrophilic trapping layer whereby the lower surface of the upper layer contacts the upper surface of the lower layer to form a single continuous microfluidic channel with a base that includes a trap. In the first (upper) hydrophobic flow layer, particles (e.g., microparticles) navigate through a filter fractal structure that includes a plurality of features with hydrophobic surfaces that have non-specific adsorption characteristics that capture and filter out nanoscale particles. The second (lower) hydrophilic trapping layer is modified with PEG reagents and exhibits hydrophilic characteristics in the FT junction array region. This modification facilitates elution of non-specific nanoscale particles, ensuring selective retention of target microbeads. Single protein molecules are captured on the microbeads and labeled with Qt-dot nanoscale particles. Simultaneously, gold nanoscale particles that bind within the streptavidin affinity system are introduced to act as "amplification antennae" for the fluorescent signal. This dual labeling approach - initial labeling with quantum dots and signal enhancement with gold nanoscale particles.

[0244] Meanwhile, the control region is characterized by a mirror-finished reflective structure as a test region. The control region is subjected to testing in the control sample by manipulating a single variable. The exemplary FT junction array has an upper size limit of 6 x 6 mm 2 , with each microbead array imaging area of 30 mm 2 . Images are obtained in both conduits to identify "on" beads and "off" beads. The fluorescent signal detected for a blank sample of the FT junction array is considered background noise, guiding an adaptive algorithm to minimize systematic error. Repeated testing of a standard curve is not required under varying environmental conditions. This indicates a robust assay or measurement system that maintains its accuracy and consistency despite changes in the environment, thereby reducing the need for frequent recalibration or reevaluation of the standard curve. This feature is particularly valuable in terms of time efficiency and reliability in a sustained or long-term experimental or analytical setting.

[0245] An exemplary platform for BiZi-FIA is depicted in Figures 1A-1D .

[0246] (iii) solid phase matrix

[0247] In some forms, the BiZi-FIA is configured to attach the target biomarker to a capture probe complexed or conjugated to a solid phase matrix. Generally, the BiZi-FIA is configured to preserve a microscale matrix. An exemplary microscale solid phase matrix is a particle, e.g., a microparticle. A micrometer scale particle or "microparticle" has a size of about 1.0 micrometers to 1000.0 micrometers, inclusive, e.g., about 1 pm, or 2, 3, 4, 5, 6, 7, 8, 9, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 150, 200, 300, 400, 500, 600, 700, 800, 900, or 999, or 1,000 pm. The hydrodynamic diameter (Dh) of a molecule is defined as the diameter of a perfect solid sphere that would exhibit the same hydrodynamic friction as the molecule of interest. For example, Dh values reflect primarily hydrodynamic friction, but are generally also a good estimate of the absolute size of a molecule. For example, in some forms, the microparticle has a hydrodynamic volume (Dh) of between about 1.0 and 1000.0 micrometers, inclusive, such as about 1 pm, or 2, 3, 4, 5, 6, 7, 8, 9, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 150, 200, 300, 400, 500, 600, 700, 800, 900, or 999, or 1,000 pm. In other forms, the microparticle has a diameter of between about 1.0 and 1000.0 micrometers, inclusive, such as about 1 pm, or 2, 3, 4, 5, 6, 7, 8, 9, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 150, 200, 300, 400, 500, 600, 700, 800, 900, or 999, or 1,000 pm. In exemplary forms, the microbead has a diameter of about 3 pm.

[0248] In some forms, the microparticle is formed from or includes a polymer, e.g., an inert polymer. In some forms, the microparticle is formed from or includes a metal. In some forms, the microparticle is formed from or includes a glass. In some forms, the microparticle is opaque. In some forms, the microparticle is transparent or allows one or more different wavelengths of light to pass through the particle. In some forms, the microbead is a biotinylated bead. In some forms, the number of beads used should exceed the number of molecules of the target biomarker within the sample. For example, in some forms, the number of microbeads is at least equal to or greater than the number of molecules of the target biomarker within the sample. In some forms, the number of microbeads is 100%, 150%, 200%, 300%, 400%, 500%, 600%, or greater than 600% of the total number of molecules of the target biomarker within the sample.

[0249] Exemplary solid phase substrates include Dynabeads, such as Dynabeads that include 2.8 pm diameter carboxyl and epoxy-linked superparamagnetic beads. In exemplary forms, the solid phase substrate is or includes about 100,000 to 1,000,000 microbeads. An exemplary number of microbeads is about 450,000 microbeads. Typically, the beads are conjugated with one or more capture agents specific for one or more target biomarkers.

[0250] Without the restriction of magnetic forces, microbeads that exhibit excellent uniformity and optical transparency are a preferred choice, including silica, polystyrene, glass microbeads, gold nanoparticle beads. The controlled and uniform capture and distribution of these microbeads further minimizes random errors. The consistent behavior and transparency of the microbeads aid in more accurate measurements and clearer observations in the assay or analysis procedure.

[0251] (a) capture agent

[0252] Typically, the described BiZi-FIA apparatus incorporates capture agents for specific binding to target biomarkers. The capture agents are typically nanoscale particles, however the capture agents can include or can be conjugated to a solid phase substrate (e.g., a bead). Exemplary capture agents include, but are not limited to, immunoglobulins, nucleic acids such as DNA and RNA, such as RNA aptamers, and small molecules, such as ligands for specific proteins and / or agents. The term “capture probe” refers to any molecule capable of capturing (directly or indirectly) and / or labeling a target molecule (e.g., a biomarker of interest) in a biological sample. In some forms, the capture probe is a nucleic acid or polypeptide that includes at least one analyte capture sequence. In some forms, the capture probe includes a capture tag, e.g., for conjugation to a solid phase substrate.

[0253] (1) capture tag

[0254] In some forms, the capture agent includes one or more capture tags, e.g., to couple the capture agent to a solid support substrate or another molecule. Preferably, the capture tag is a chemical compound, such as a ligand or hapten, that binds or interacts with another chemical compound, such as a ligand binding capture agent or an antibody capture agent.

[0255] It is also preferred that this interaction between the capture tag and the capture component is a specific interaction, such as between a hapten and an antibody or between a ligand and a ligand binding molecule.

[0256] A preferred capture tag is biotin. In exemplary forms, the capture agent is a biotinylated capture agent. In preferred forms, the biotinylated capture agent is a biotinylated antibody.

[0257] The capture tag incorporated into the capture agent allows the capture agent to be captured, adhered, or coupled to a substrate, such as a transparent microbead.

[0258] (b) reporter molecule

[0259] Generally, the described BiZi-FIA device incorporates a reporter molecule, such as a nanoscale fluorescent marker or label, for detecting the biomarker. Reporter molecules and labels are known in the art. Any nanoscale reporter molecule known in the art can be included in the described system for BiZi-FIA measurement of a biomarker.

[0260] Exemplary reporter molecules for the BiZi-FIA system include luminescent quantum dot nanoparticles. Exemplary quantum dot nanoparticles have a diameter of about 120 nm. Exemplary quantum dot nanoparticles have an emission peak of 625 nm. Generally, gold nanoparticles bound within the streptavidin-affinity system are introduced to amplify the fluorescent signal. This dual-labeling approach - using a fluorescent label for initial labeling and gold nanoparticles for signal enhancement - improves the overall accuracy and sensitivity of the system.

[0261] Generally, the reporter molecule has affinity for the capture agent that has bound to the target biomarker. Generally, the reporter molecule is sized according to the pores within the hydrophilic capture / web portion of the RTJ structure of the BiZi-FIA apparatus, such that un-conjugated reporter label washes away from the conjugated microparticles within the BiZi-FIA apparatus. In some forms, the bandwidth of the optical system limits the number of available dyes to about four (e.g., red, green, blue, yellow). In some forms, the reporter molecule includes one or more molecules that act as detectable labels or dyes. In some forms, the label is an optically detectable moiety (e.g., a fluorophore). Non-limiting examples of types of optically detectable labels include fluorescent, chemiluminescent, or electrochemiluminescent labels. Examples of fluorescent labels include, but are not limited to, 4-acetamido-4'-isothiocyanatostilbene-2,2' disulfonic acid; acridine and derivatives thereof, such as acridine, acridine isothiocyanate; 5-(2'-Aminoethyl) aminonaphthalene-l-sulfonic acid (EDANS); 4-Amino-N-[3-vinylsulfonyl)phenyl]naphthalimide-3,5 disulfonate; N-(4-Anilino-l-naphthyl)maleimide; anthranilamide; BODIPY; Brilliant Yellow; coumarin and derivatives; coumarin, 7-amino-4-methyl coumarin (AMC, Coumarin 120), 7-amino-4-trifluoromethylcoumarin (Coumarin 151); cyanine dyes; cyanine; 4',6-diamidino-2-phenylindole (DAPI); 5',5"-Dibromopyrogallol-sulfonaphthalen (bromopyrogallol red); 7-Diethylamino-3-(4'-isothiocyanatophenyl)-4-methylcoumarin; diethylenetriaminepentaacetate; 4,4'-Dihydroxy-dihydro-stilbene-2,2'-disulfonic acid; 4,4'-Dihydroxystilbene-2,2'-disulfonic acid; 5-[Dimethylamino]naphthalene-l-sulfonyl chloride (DNS, dansyl chloride); 4-Dimethylaminophenylazophenyl-4'-isothiocyanate (DABITC); eosin and derivatives; eosin, eosin isothiocyanate, erythrosin and derivatives; erythrosin B, erythrosin, isothiocyanate; ethidium; fluorescein and derivatives; 5-Carboxyfluorescein (FAM), 5-(4,6-Dichlorotriazin-2-yl)aminofluorescein (DTAF), 2',7'-Dimethoxy-4'5'-dichloro-6-carboxyfluorescein, fluorescein, fluorescein isothiocyanate, QFITC, (XRITC); fluorescamine; IR144; IR1446; malachite green isothiocyanate; 4-Methylumbelliferone ortho-methoxyphenolphthalein; nitrotyrosine; pararosaniline; phenol red; B-phycoerythrin; o-Phthaldehyde; pyrene and derivatives: pyrene, pyrene butyrate, succinimidyl 1-pyrene; butyrate quantum dots; reactive red 4 (Ciba TMBrilliant Red 3B - A) Rhodamines and derivatives: 6-carboxy-X-rhodamine (ROX), 6-carboxyrhodamine (R6G), Lissamine rhodamine B sulfonic chloride rhodamine (Rhod), rhodamine B, rhodamine 123, rhodamine X isothiocyanate, sulfo-rhodamine B, sulfo-rhodamine 101, sulfonyl chloride derivative of sulfo-rhodamine 101 (Texas Red); N,N,N',N'-tetramethyl-6-carboxyrhodamine (TAMRA); tetramethylrhodamine; tetramethylrhodamine isothiocyanate (TRITC); riboflavin; roseolic acid; terbium chelated derivatives; Cy3; Cy5; Cy5.5; Cy7; IRD 700; IRD 800; La Jolta Blue; phthalocyanine; naphthalocyanine; any fluorescent marker available from Atto-Tec, such as Atto 390, Atto 425, Atto 465, Atto 488, Atto 495, Atto 520, Atto 532, Atto 550, Atto 565, Atto 590, Atto 594, Atto 610, Atto 611X, Atto 620, Atto 633, Atto 635, Atto 637, Atto 647, Atto 647N, Atto 655, Atto 680, Atto 700, Atto 725, Atto 740, etc.; any fluorescent label available from Dyomics, such as DY-630, DY-631, DY-632, DY-633, DY-634, DY-635, DY-636, Dy-647, Dy-648, DY-649, Dy-650, Dy-651, DY-652, etc.; any fluorescent label available from Pierce, such as DyLight 405, DyLight 488, DyLight 549, DyLight 633, DyLight 649, DyLight 680, DyLight 800, etc.; any fluorescent label available from AnaSpec, such as HiLyte Fluor TM 488 dye, HiLyte Fluor TM 555 dye, HiLyte Fluor TM 647 dye, HiLyte Fluor TM 680 dye, HiLyte Fluor TM 750 dye, HiLyte Plus TM 555 dye, HiLyte Plus TM 647 dye, HiLyte Plus TM750 dyes, etc.; any fluorescent label available from Denovo Biolables, such as Oyster 500, Oyster 550P, Oyster 550D, Oyster 556, Oyster 645, Oyster 650P, Oyster 650D, Oyster 656, etc.; 680, 700, 700DX, 800, 800RS, 800CW, etc.; any fluorescent label available from SETA Biomedicals, such as Seta K1-204, Seta K5-3212, Seta K8-1342, Seta K8-1352, Seta K8-1357, Seta K8-1407, Seta K8-1642, Seta K8-1644, Seta K8-1663, Seta K8-1664, Seta K8-1669, Seta K8-3002, Seta K4-1082, Seta K8-1669, Seta K7-545, Seta K7-547, Seta K7-549, Seta K8-1252, Seta K8-1261, Seta K8-1262, Seta K8-1320, Seta K8-1344, Seta K8-1367, Seta K8-1377, Seta K8-1382, Seta K8-1446, Seta K8-1667, Seta K8-1752, Seta K8-1762, Seta K8-1767, Seta K8-1777, Seta K8-1782, etc.

[0262] (iv) sample

[0263] The described BiZi-FIA device is designed for detecting and measuring one or more biomarkers, such as biomarkers present within a sample. An exemplary sample is a biological sample, such as a biological fluid. An exemplary biological fluid is a bodily fluid, such as a bodily fluid obtained from a sample.

[0264] The described BiZi-FIA device requires microliter amounts of sample. Thus, in some forms, the sample used according to the described device for BiZi-FIA has a volume of 1 pL, or 2, 3, 4, 5, 6, 7, 8, 9, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 150, 200, 300, 400, 500, 600, 700, 800, 900, or 999, or 1,000 pL, or greater than 1,000 pL. In exemplary forms, the sample is or is diluted to a volume of about 40 pL to about 60 pL of solution, inclusive. In some forms, the sample volume is 50 pL.

[0265] In some forms, the sample is an unfiltered body fluid, e.g., pus, blood, or tears from a subject. In some forms, the sample is a concentrated sample, e.g., concentrated to less than 100% of the initial (“net”) volume, such as 90%, 80%, 70%, 60%, 50%, 40%, 30%, 20%, or 10%, or less than 10% of the initial volume of the sample, such as a body fluid. In other forms, the sample is diluted, i.e., increased in volume relative to the initial (“net”) volume of the sample, such as a body fluid. For example, in some forms, the sample is diluted to greater than 100% of the initial (“net”) volume, such as 900%, 800%, 700%, 600%, 500%, 400%, 300%, 200%, or 150%, or less than 150% but greater than 100% of the initial volume of the sample, such as a body fluid.

[0266] Generally, the volume of pure biological sample, such as a body fluid, required for BiZi-FIA detection and / or measurement of target biomarkers by the described device is between about 0.01 pL and about 5.0 pL, inclusive, such as about 0.1375 pL per biomarker. As demonstrated in the Examples, in exemplary forms, a 2.2 pL volume of human tears is diluted to 16 parts to meet the requirements for 8 different biomarker detection (i.e., a volume of about 0.1375 pL per biomarker).

[0267] In some forms, the amount of target biomarker within the pure biological sample, such as a body fluid, is between about 1 IU / mL and about 10 U / mL, inclusive, such as about 1 U / mL. It has a detection sensitivity that extends to the attomolar level for model assays and low femtomolar levels for human tears. The described BiZi-FIA device can detect amounts of target biomarkers in a sample having a concentration of about 10 x 10 -18 M to about 10 x 10 5 M, inclusive.

[0268] (a) biological body fluid

[0269] In some forms, the one or more biomarkers are within a biological sample, e.g., a biological fluid or a sample comprising a biological fluid. Typically, the biological sample comprises a bodily fluid from a subject. Typically, the subject is a mammal. In some forms, the subject is a human. Non-limiting examples of bodily fluids include blood, urine, plasma, serum, tears, lymphatic fluid, bile, cerebrospinal fluid, interstitial fluid, aqueous or vitreous humor, colostrum, sputum, amniotic fluid, saliva, anal and vaginal secretions, sweat, semen, exudates, transudates, and synovial fluid. In some forms, the biological sample comprises tears, urine, or serum obtained from the subject. In other forms, the biological sample comprises blood, sputum, mucus, or a liquefied or lysed tissue, e.g., a lysate of bone marrow or peripheral blood cells.

[0270] (b) target biomarker

[0271] The described BiZi-FIA apparatus is designed for detecting and measuring one or more biomarkers, e.g., biomarkers present within a biological sample. Any biomarker known in the art can be detected according to the described BiZi-FIA methods. For example, the target biomarker can be or comprise a small molecule, a peptide, a carbohydrate, a lipid, a nucleic acid (e.g., DNA or RNA), a protein (e.g., an enzyme, an immunoglobulin, or a receptor), a synthetic polymer, a metal, a hormone, a drug, a bacterium, a virus, a protozoan, or a combination thereof. In some forms, the biomarker is a cytokine or an antigen, e.g., a cancer antigen or an antigen derived from a microorganism, e.g., a pathogen or an allergen. In some forms, the target biomarker is an immunoglobulin, e.g., an immunoglobulin specific for an antigen, e.g., an autoantigen or a foreign antigen. Exemplary biomarkers include TNF-a, LCN-1, NFL, tau-n, IgE, e.g., as an inBiZi-FIAtion of ocular inBiZi-FIAtives present in human tears. An exemplary pathogenic biomarker is a viral protein, e.g., SARS-COV2 N protein as an inBiZi-FIAtive to inhibit SARS-COV2 viral infection. Other exemplary biomarkers include tumor biomarkers, e.g., prostate-specific antigen (PSA), carcinoembryonic antigen (CEA), alpha-fetoprotein (AFP), cancer antigen 125 (CA-125), cancer antigen 19-9 (CA 19-9), human epidermal growth factor receptor 2 (HER2 / neu), cytokeratin 19 fragment (CYFRA 21-1), B-Raf proto-oncogene (BRAF V600 mutation), and epidermal growth factor receptor (EGFR mutation).

[0272] (v) buffers and wash reagents

[0273] In some forms, the methods for microfluidic device-based BiZi-FIA employ a buffer and a washing reagent. The buffer and washing reagent can be any solution used to remove or reduce the local concentration of another component, such as a contaminant.

[0274] Exemplary buffers and washing reagents include water, physiological salt solutions, such as PBS and DMEM.

[0275] (a) a buffer

[0276] In some forms, the methods for microfluidic device-based BiZi-FIA employ a buffer to dilute a sample, load microparticles conjugated with a capture agent having a target biomarker, and for actuating flow within the BiZi-FIA device. Typically, the methods include washing reagents and / or buffers known in the art to be physiological and / or not to disrupt the structure or amount of the target biomarker.

[0277] In some forms, the buffer includes one or more of EDTA, sodium, ammonium, chloride, iodide, phosphate, and TRIS buffer.

[0278] III. Methods of manufacture

[0279] Methods for manufacturing digital immunochromatography (BiZi-FIA) chips have been developed.

[0280] Typically, the methods include the following steps:

[0281] (a) photolithography to ablate a simple microchannel pattern on a silicon wafer;

[0282] (b) surface treatment of the microchannels;

[0283] (c) capture agents coupled to reporter molecules; and

[0284] (d) coupling of capture agents on beads.

[0285] A. Lithography

[0286] The methods include one or more photolithography steps. In exemplary forms, the PDMS and hardener are mixed in a 10: 1 weight ratio and the mixture is degassed in a vacuum to remove air bubbles.

[0287] In some forms, the PDMS mixture is poured over the ablated silicon wafer and cured, such as at 70 °C for 30 minutes. After curing, the PDMS layer is peeled off the silicon wafer, forming a PDMS male mold.

[0288] In some forms, the method treats the PDMS mold with an O2 plasma for 1 minute, then immerses it in a 0.1 M PEG solution (average molecular weight 6000 g / mol) to form the isolation membrane.

[0289] In some forms, the method repeats the PDMS casting and curing steps on a convex mold to produce a concave PDMS mold.

[0290] In some forms, the method bonds the PDMS mold to a blank PDMS substrate to form a microfluidic chip.

[0291] B. Surface Treatment

[0292] The method includes one or more surface treatment steps. In some forms, the method prepares a surface treatment reagent. The surface treatment method enhances the hydrophilicity of the PDMS surface in the microfluidic chip, thereby minimizing non-specific adsorption and improving chip performance for various applications.

[0293] For example, in some forms, the method prepares a 0.1 M solution of polyethylene glycol (PEG) with an average molecular weight of 6000 g / mol. The solution is used as the surface treatment reagent.

[0294] In some forms, the method includes setting up a surface treatment system. For example, in some forms, the method arranges a surface treatment reservoir connected to an inlet of a surface treatment channel in the microfluidic chip.

[0295] In some forms, the method includes one or more steps to ensure the system is sealed and leak-proof. For example, in some forms, the method includes connecting a precision pump to the surface treatment reservoir to control the flow of the PEG solution.

[0296] In some forms, the method initiates the surface treatment process. For example, in some forms, the method activates the precision pump to introduce a hydrophilic solution (such as a PEG solution) into the surface treatment channel. The flow rate should be maintained at a consistent and controlled speed to ensure uniform distribution of the reagent. In some forms, the PEG solution flows through the array capture channels simultaneously, which are parallel to the surface treatment channel. This ensures that the channels are uniformly exposed to the treatment reagent.

[0297] In some forms, the method maintains the flow rate and treatment time. For example, in some forms, the method maintains a constant flow rate of the PEG solution throughout the treatment process. A recommended flow rate parameter can be about 0.5 mL / min, but this can vary based on the specific design and size of the microfluidic chip.

[0298] The duration of the surface treatment process should be carefully timed. A typical treatment time can be 25 to 30 minutes. This duration ensures adequate surface modification without overexposure.

[0299] In some forms, the method completes and drains the reagent. For example, in some forms, after the treatment duration is complete, the method gradually reduces the flow rate and eventually stops the pump.

[0300] The method allows for complete drainage of the hydrophilic (i.e., PEG) solution from the outlet of the microfluidic chip. Ensures that there are no residual treatment reagents within the tubing.

[0301] In some forms, the method includes a post-treatment process. For example, in some forms, the method includes a step of rinsing the surface-treated tubing with deionized water to remove any unbound PEG molecules. In some forms, the microfluidic chip is dried under a gentle stream of nitrogen to remove any remaining moisture.

[0302] In some forms, the method includes verifying the surface treatment. In some forms, the method assesses the success of the surface treatment by measuring the contact angle of a water droplet on the treated surface. A significant decrease in the contact angle indicates successful hydrophilic modification.

[0303] Optionally, in some forms, the method includes additional analytical techniques, such as SEM (scanning electron microscopy) or FTIR (Fourier transform infrared spectroscopy) for further verification and analysis of the surface features. In some forms, the method includes measuring the contact angle of a 5 pL droplet of deionized water on differently treated PDMS substrates using a CCD camera. In some forms, the method assesses the hydrophilic properties of the treated surface by recording the contact angle of a plasma-PEG treated surface over a period of up to 420 hours. In some forms, the method uses SEM microscopy to examine the morphology of the PDMS before and after surface treatment.

[0304] In some forms, the method employs FTIR (Fourier transform infrared spectroscopy) to compare PDMS samples before and after treatment.

[0305] C. Capture agent coupled to a reporter molecule (label)

[0306] The method includes one or more steps of coupling the capture agent to a reporter molecule.

[0307] In some forms, the method includes preparing the capture agent, e.g., a detection antibody, by coupling to a reporter molecule, e.g., a streptavidin-functionalized quantum dot nanoparticle.

[0308] In some forms, the method includes one or more steps of conjugating the capture agent specific for the target biomarker to a reporter molecule according to any standardization protocol known in the art.

[0309] In some forms, the method includes one or more steps of conjugating an antibody to quantum dot nanoparticles. In other forms, the method prepares antibody-conjugated quantum dot nanoparticles that are initiated and ready for integration into further experimental applications, such as immunofluorescence assays or protein detection.

[0310] As demonstrated in the examples, in some forms, the method includes one or more steps to prepare a working solution of streptavidin quantum dot nanoparticles having an emission peak of 625 nm, according to the manufacturer's instructions (catalog number DNQ-N008, DiagNano). TM Preparation of CDBioparticles: The prepared bead solution (50 μL) was centrifuged at 5000 rpm for 2 minutes at room temperature to remove the supernatant. The beads were then subjected to a thorough washing process, in which they were gently vortexed and then centrifuged three times with 1 mL phosphate-buffered saline (PBS). Subsequently, 25 μg of antibody solution was introduced into the washed beads. The tube containing the mixture was gently aspirated to ensure homogeneous mixing. The tube was then incubated on an orbital shaker at room temperature with gentle vortexing for 1 hour, allowing sufficient time for the antibody to efficiently bind to the streptavidin-coated Qt nanoparticles. Once the incubation period was complete, the supernatant was removed after centrifugation. The beads were then washed three additional times with 1 mL PBS each time to remove any unbound antibody from the beads. After the final wash, the antibody-conjugated Qt nanoparticles were resuspended in 100 μL PBS for immediate use or resuspended in a suitable storage buffer for long-term storage.

[0311] D. Coupling traps on beads

[0312] In some forms, the method includes one or more steps of coupling a trapping agent (such as a tag-conjugated trapping agent) to a solid matrix. In some forms, the method includes one or more steps of coupling a tagging trapping agent to microbeads.

[0313] In some forms, the method includes one or more steps of conjugating a trapping agent specifically labeled for a target biomarker with a microparticle according to any standardized scheme known in the art.

[0314] As described in the examples, the antibody is covalently immobilized on... Methods for applying M-270 epoxy resin to surfaces. Strictly based on information from Thermo Fisher Scientific. The guidelines provided in the antibody-conjugation kit (2.8 μm diameter carboxylic acid and epoxy-linked superparamagnetic beads) guide the design and execution of the schemes described in the examples.

[0315] Initially, a series of buffers and solutions are prepared by antibody coupling kits, including CI for bead washing and preparation, C2 as an activating agent for coupling, LB as a low salt buffer for non-specific binding removal, HB as a high salt buffer for enhanced binding, and SB for long-term bead storage. These analytical grade reagents are confirmed to be compatible with protease and phosphatase inhibitors and stored at 2-25 °C.

[0316] For 5 mg bead preparation, weigh exactly 5 mg of beads at room temperature along with the beads M-270 epoxy resin to prevent condensation. The beads are then washed using CI solution to ensure their surface is ready for coupling. After magnetic separation, a volume of antibody is added and filled up to a remaining volume of 250 μΐ with CI. Another 250 μΐ of C2 activating agent solution is introduced, promoting the covalent attachment of the antibody to the bead surface. The mixture is then incubated overnight at 37 °C on a roller.

[0317] The next day, the tube is placed on a magnet and the supernatant is discarded. The beads are washed sequentially with HB and LB to remove non-specifically bound material and enhance specific binding. A short SB wash is performed, followed by a long SB wash, where the beads are incubated at room temperature for 15 minutes. Finally, the beads are resuspended in 500 μΐ of SB, ensuring their stability and longevity. In an exemplary form, the final bead concentration is 10 mg antibody-coupled beads / ml.

[0318] IV. Method of digital immunochromatography (BiZi-FIA)

[0319] A method of digital immunochromatography (BiZi-FIA) has been developed.

[0320] Typically, the method utilizes a BiZi-FIA chip device, which is loaded with functionalized beads as a solid support for specific capture molecules. These beads are guided through microfluidic channels, where filter-like flow controls ensure precise manipulation and directed motion of the beads. When the beads encounter a capture junction, they are immobilized, allowing for controlled interaction between the target analyte and the capture molecules.

[0321] The filter-like flow and capture junction mechanism provides several advantages over traditional immunochromatographic techniques. Controlled flow enables precise control of reaction times, improving the binding kinetics between the capture molecules and the target analyte. The capture junction provides a stable and confined environment for the immobilization of the beads, promoting enhanced sensitivity and specificity in the detection of the analyte.

[0322] Filter-like flow and capture junction technology and integration of bead-based assays into digital immuno-chromatography provides a versatile and robust platform. This method has great potential in various fields such as clinical diagnostics, environmental monitoring, and food safety due to its high sensitivity, rapid detection, and potential for cost-effective, high-throughput analysis. The digital immuno-chromatographic method provides sensitive and multiplexed analyte detection.

[0323] The described BiZi-FIA biochip simplifies the immunoassay process by integrating incubation, filtration, and washing steps within the chip. The easy-to-operate BiZi-FIA enables single-molecule protein detection with attomolar sensitivity, featuring enhanced signal-to-noise ratio, fast analysis time, reduced reagent usage, and reduced setup cost. The robustness of the assay method technique is demonstrated in the examples, which exemplify 94% capture efficiency of 4 x 1010beads within a compact area (30 mm 2 ) achieved in 180 seconds, which is significantly higher than two times the capture efficiency and array density of commercialized single-molecule array chips (e.g., Simoa chips). 5

[0324] If the background noise in the control region exceeds the error range of the standard curve, the test is considered to fail. If the background noise is within the acceptable range, the signal from the test region can be adjusted by subtracting the background noise from the control region, thereby generating an effective signal for concentration estimation based on the standard curve.

[0325] Generally, the method includes one or more steps of detecting and / or measuring the amount of a biomarker within a sample using BiZi-FIA. The assay method generally includes one or more of the following steps:

[0326] (a) providing

[0327] (i) a fluid sample comprising a target biomarker, and

[0328] (ii) a control sample comprising a known amount of the target biomarker;

[0329] wherein the fluid sample and the control sample are provided in separate microfluidic channels in a microfluidic device

[0330] described for BiZi-FIA;

[0331] (iii) a solid phase matrix comprising a plurality of microparticles,

[0332] wherein a microparticle of the plurality of microparticles comprises a plurality of capture agents conjugated thereto,

[0333] wherein a capture agent of the plurality of capture agents has specificity for the target biomarker; and ​

[0334] (iv) a labeled capture agent specific for the target biomarker,

[0335] wherein an equal amount of microparticles are provided in separate microfluidic channels in the microfluidic device;

[0336] (b) actuating movement of the microparticles within the microfluidic channels in the microfluidic device,

[0337] wherein the movement comprises filtering and washing the microparticles within the flow-capture junctions within the microfluidic channels in the microfluidic device; and

[0338] (c) forming an array comprising the microparticles within the capture of each microfluidic channel in the microfluidic device; and

[0339] (d) imaging the array of microparticles within the microfluidic device.

[0340] In some forms, the method further comprises one or more steps for:

[0341] (e) detecting and measuring the target biomarker conjugated or coupled to the microbeads within the array.

[0342] A. providing reagents

[0343] Generally, the assay method comprises the step of providing an effective amount of a fluid sample comprising a target biomarker on the microfluidic device described for BiZi-FIA.

[0344] In some forms, the fluid sample comprises a volume of between 10 μL and 100 μL. In some forms, the control sample comprises a volume of between 10 μL and 100 μL.

[0345] In some forms, prior to providing the test and control samples or microbeads on the device, the fluid sample and control sample are contacted with microbeads conjugated to a capture agent specific for the target biomolecule. For example, in some forms, prior to providing the reagents of the BiZi-FIA device, microbeads conjugated to a labeled capture agent specific for the target biomarker are incubated with the test or control sample to enable specific binding of the target biomarker to the capture agent on the surface of the microbeads.

[0346] In some forms, contacting the fluid sample and the control sample with the microbeads conjugated with labeled capture agents specific for the target biomolecule is effective to bind at least 50%, at least 60%, at least 70%, at least 80%, at least 90%, or at least 100% of the biomarker molecules within the sample to the capture agents immobilized on the microparticles for an amount of time. In some forms, the incubation time is from 1 second to 1 minute, or from 1 minute to about 100 minutes or 100 hours. In exemplary forms, the incubation time is about 30 minutes. For example, in some forms, after ensuring adequate mixing, the samples are incubated at room temperature for 30 minutes. The incubation period generally facilitates binding of the quantum dot-labeled capture agents (e.g., detection antibodies) to the antigens previously captured on the magnetic beads.

[0347] In exemplary forms, after incubation, further washing steps are performed according to the same protocol, i.e., to remove unbound quantum dot-antibody complexes. In exemplary forms, the beads are separated from the supernatant using a magnet and resuspended in 50 μΐ, of buffer.

[0348] In exemplary forms, the resuspended bead-protein-quantum dot complexes are then carefully introduced into the microfluidic BiZi-FIA device, e.g., using a micropipette.

[0349] Generally, an equal amount of microparticles is provided in a separate microfluidic channel in the microfluidic device.

[0350] B. Actuating movement of microparticles within a microfluidic channel in the microfluidic device

[0351] Generally, the assay method includes a step of actuating flow of the particles within the microfluidic channel in the device. The flow can be automated, e.g., by use of a pump, or it can be manually actuated, e.g., by a pipette. The flow is generally actuated by applying pressurized fluid to an inlet in the device. Excess fluid passes through the device via an outlet conduit in the capture structure.

[0352] The amount and pressure of the fluid, as well as the time, can vary depending on the requirements of the assay. In some forms, the method passes fluid through the device in an amount effective to filter and wash the microparticles within the device. In some forms, the method passes fluid through the device in an amount and for a time effective to concentrate the microparticles within the capture structure of the device to form an array. In some forms, the method passes fluid through the device to filter, concentrate, and / or wash the microparticles in the fluid for a time of from about 60 seconds to about 300 seconds, inclusive. In some forms, the method passes fluid through the device to filter, concentrate, and / or wash the microparticles in the fluid for a time of about 180 seconds.

[0353] C. Filtration and washing on the beads

[0354] In some forms, the method using the BiZi-FIA platform includes automated filtration and washing of the capture beads.

[0355] In some forms, the method includes one or more steps of washing the beads to effectively remove non-specific impurities while preserving the specific immune complexes on the beads. For example, in some forms, the method uses fluidic loading and separation of microbeads associated with a single enzyme molecule only. Parallel perfusion of multiple channels significantly improves efficiency. In some forms, upon entering the middle array region, the single molecule immune complexes captured by the beads interact with the contrasting surface properties of the array region. Hydrophilic and anti-static surfaces prevent non-specific adsorption of quantum dot (QD) microspheres, ensuring their removal from the chip by fluid dynamics. The method enables particles to flow through a hydrophobic polymer, such as polydimethylsiloxane (PDMS), in the flow separation zone to effectively eliminate non-specifically bound proteins and impurities, thereby significantly improving the signal-to-noise ratio (SNR) and detection sensitivity. Thus, the method employs on-chip separation whereby the interaction of the particles with the hydrophobic microcolumns binds and removes non-specifically adsorbed particles. The method employs collision-based filtration by passing the microbeads through hydrophilic and hydrophobic surfaces to reduce non-specific adsorption of particles to the beads.

[0356] D. Bead detection

[0357] The described method includes one or more steps for detecting biomarkers based on imaging microbeads that have been developed within a high-density microbead array. In some forms, the method employs fluorescence detection with the beads arranged in a detection chamber. In some forms, the method employs fluorescence detection if it is a negative control sample (0 fg / mL). In some forms, the method uses the BiZi-FIA platform to automate the filtration process to improve bead arrangement. In some forms, where the BiZi-FIA platform employs silica beads as the primary capture substrate, the silica beads have higher optical transparency and reduced tendency for non-specific binding compared to non-silica bead systems, with lower background noise. In some forms, the measurement of light intensity is performed at 611 nm by a fluorospectrophotometer. In some forms, the measurement includes microscopic imaging.

[0358] In exemplary forms, the method employs one or more algorithms to accurately measure the amount of biomarkers. An exemplary algorithm is an adaptive difference algorithm with one-step imaging for accurate bead detection.

[0359] 1. Adaptive difference algorithm

[0360] In some forms, the method for detecting and quantifying biomarkers includes an adaptive difference algorithm.

[0361] With BiZi-FIA's superior sample loading capacity, a self-adaptive differential algorithm was designed to compensate for background noise fluctuations. The algorithm operates by comparing signals from a control sample to signals from a fluid sample, effectively eliminating background noise signals present under the same environmental conditions. This method maintains assay accuracy, especially when detecting ultra-low analyte concentrations, and it exhibits significant resilience to environmental changes. BiZi-FIA eliminates the necessity of depositing a conductive metal film on the microchip surface, a requirement in traditional methods employing dielectrophoresis (DEP), electric field, or magnetic field techniques to increase bead loading rates. The simplification achieved with BiZi-FIA enables high-resolution, bead-based analyte quantification and is easy to operate.

[0362] In some forms, the number of beads used and the percentage of them to be analyzed are two important parameters for the sensitivity and dynamic range of the BiZi-FIA assay. In some forms, the number of beads used determines the f on (number of positive events relative to the total number of beads) and AEB molecules. For example, for 1,000,000, 500,000, and 100,000 beads used, the theoretical AEB is 0.0006, 0.0012, and 0.0060, respectively. Therefore, using fewer beads will result in a higher f on and AEB. In some forms, the percentage of beads analyzed results in the sensitivity of detection, as the more beads analyzed, the lower the measurement uncertainty.

[0363] In some forms, when all beads in the array are analyzed, the number of beads used does not affect the sensitivity of detection. In other forms, the more beads used, the shorter the incubation time required for a complete assay. In some forms, using a larger number of beads expands the dynamic range of quantitative detection, enabling the chip to be suitable for a larger range of analyte concentrations. Therefore, in some forms, using as many beads as possible is beneficial for improving sensitivity if all beads are analyzed. While analyzing so many beads can be advantageous, it can also result in more complex systems and instruments that are not suitable for traditional or rapid use. Therefore, by increasing the density of the bead array, reaction and imaging times can be reduced, detection sensitivity is enhanced with simplified system complexity, and rapid point-of-care diagnostics are achieved.

[0364] It is possible that in clinical testing, the accuracy of diagnosis can be significantly affected by environmental instabilities such as pH changes, temperature fluctuations, ion concentration changes, and interfacial effects. This is particularly problematic when traditional Poisson distribution algorithms are applied to the detection of extremely low concentrations. At such low levels, the inherent randomness and uncertainty, coupled with background noise, including interfering signals caused by non-specific adsorption, result in considerable detection errors that are not acceptable in a clinical context.

[0365] In some forms, the method enables comprehensive analysis of the entire sample by the principle of exhaustive testing. In some forms, the method employs a computational method that is insensitive to both systematic and random errors, called the "adaptive difference method," which utilizes control samples of known concentrations to facilitate rapid initial testing without the need to generate a standard curve, allowing for accurate differential calculation of sample concentrations. As the number of known concentration tests in the control sample increases, more data points are collected, enabling continuous refinement and enhancement of the model. The method includes an iterative process involving refitting the curve with new data points to generate an updated prediction function, leveraging incremental learning within the field of machine learning, improving the accuracy of the model's prediction of unknown data.

[0366] In some forms, the differential adaptive algorithm involves subtracting the noise signal from the control blank sample to obtain the concentration difference. The comprehensive washing method employs control samples of known concentrations, washed to the point where the control sample signal meets the error range of the standard curve value, ensuring the validity of the test area signal.

[0367] In some forms, the method includes an algorithm to reduce errors in spatially reflective symmetric microfluidic biochips, particularly with respect to non-specific adsorption issues at low concentrations. In some forms, the method includes a mathematical model based on the Poisson distribution for eliminating errors caused by non-specific adsorption in microfluidic chip assays for single-molecule protein detection. In some forms, the method includes the following steps:

[0368] (a) selecting the fluorescence signal from the fluid sample and the control sample to follow the Poisson distribution. The Poisson distribution is suitable for describing sparse events, such as signals from single-molecule protein detection;

[0369] (b) selecting the background noise due to non-specific adsorption to follow an independent Poisson distribution; and

[0370] (c) selecting a model to represent the Poisson distribution of signals from the fluid sample and the control sample.

[0371] A. Exemplary optimization parameters

[0372] In some forms, the method optimizes one or more of the parameters, including the numerical aperture (NA) of the objective lens, the exposure time, and the detector sensitivity, particularly focusing on the detection limit of the CMOS sensor used in commercial fluorescence microscopy.

[0373] In some forms, the method determines the photon emission rate (R abs ) of quantum dots (QDs) on beads by the photon absorption rate (R em ) and quantum yield (QY):

[0374] Rem = QY x R abs

[0375] In some forms, the method calculates the photon emission rate (R em ) from a single QD using the following equation:

[0376]

[0377] where:

[0378] σ abs is the absorption cross-section of the QD (1 x 10 -16 cm 2 , provided in a data sheet from the manufacturer DiagNano TM , typically measured experimentally for the absorption properties of the QD at a specific wavelength of 550 nm);

[0379] I0is the excitation light intensity (1 x 10 5 W / cm 2 ), equal to the ratio between the power of the laser beam (10 mW) and the area of the spot;

[0380] hv exc is the energy of a single photon at the excitation wavelength;

[0381] h is the Planck constant (6.626 x 10 -34 J s); and

[0382] v exc is the photon frequency (5.45 x 10 14 Hz), equal to the ratio between the speed of light and the excitation wavelength.

[0383] For silica beads that are highly transparent and exhibit minimal quenching, the calculated photon emission rate (R em,silica ) is about 2.35 x 10 7 photons / s.

[0384] eR em,magnatic = R abs x QY x QF x A exc

[0385] where:

[0386] QF is a quenching factor, representing the reduction in quantum yield due to energy transfer processes or other quenching mechanisms caused by the proximity of the magnetic material; and

[0387] A exc is the attenuation of the excitation light due to absorption / scattering by the magnetic beads.

[0388] Therefore, the rate of photon emission (R em,magnatic ) of the magnetic beads is reduced to about 5.89 x 10 6 photons / s, which is used to determine the number of detected photons (N detected ):

[0389] N detected = R em x A em x T x η detector x τ x f collection

[0390] where:

[0391] A em is the emission attenuation factor, which represents the reduction of the emitted fluorescence intensity due to the absorption and scattering of the emitted photons by the bead material before reaching the detector;

[0392] T is the total transmission efficiency of the optical system, taking into account the losses due to the optical components (50% or 0.5, based on typical losses in the optical components used (e.g. lenses, filters));

[0393] η detector is the quantum efficiency of the detector (CMOS sensor) at the emission wavelength of 611 nm (50% or 0.5, based on the specifications of the detector provided by the microscope manufacturer);

[0394] τ is the exposure time (1 second, selected based on the requirements of the experimental design and the need to balance signal acquisition and imaging speed); and

[0395] f collection is the fraction of emitted photons collected by the objective, which depends on the numerical aperture NA of the lens:

[0396]

[0397] The minimum number of detected photons (N detected ) required to achieve a signal-to-noise ratio (SNR) of 100 is about 10,025 photons. This is determined using the SNR definition formula:

[0398]

[0399] where:

[0400] σ read is the read noise of the detector in electronic rms. (σ read = 5 rms, obtained from the technical specifications of the microscope CMOS detector).

[0401] To achieve the required N detected , the minimum fraction of collected photons fcollection Must be satisfied:

[0402]

[0403] The minimum NA is then calculated using the following equation:

[0404]

[0405] The lower NA requirement for silica beads facilitates large field-of-view imaging, as low magnification objectives with wide fields-of-view can be used without compromising detection sensitivity. This advantage is critical for applications that require analysis of large numbers of beads or widely distributed targets. BiZi-FIA platforms employing silica beads as the primary capture substrate exhibit a significant improvement in signal-to-noise ratio (SNR) compared to traditional magnetic bead-based assays.

[0406] 2. BiZi-FIA adaptive differential noise correction for wide dynamic range fast precision detection

[0407] In some forms, the method implements an adaptive differential noise correction (ADNC) algorithm to enhance the precision and reliability of a bi-lateral zig-zag flow immunoassay (BiZi-FIA) system in detecting ultra-low concentrations of proteins.

[0408] In some forms, the method leverages the symmetric bi-lateral assay setup of BiZi-FIA, where test and control samples are introduced from opposite sides of the flow channel, ensuring similar environmental exposure of both samples to accurately distinguish true signals from background noise. The method for implementing ADNC involves several key steps, including:

[0409] Data acquisition,

[0410] Differential analysis,

[0411] Noise modeling, and

[0412] Iterative optimization with gradient descent.

[0413] The mathematical framework of the ADNC algorithm is based on the Poisson distribution, which is suitable for describing sparse events such as single-molecule protein detection signals. Fluorescence signals from the test sample (S t ) and control sample (S c ) are modeled as independent Poisson-distributed variables. The background technical noise (S0) is a component of both the test and control signals and is also assumed to be Poisson-distributed. Importantly, the control sample (S c ) contains background noise but can also include signal contributions from a standard of known concentration. To improve detection precision, the algorithm calculates the gradient of the signal difference between the test and control samples using the following equation:

[0414] f'(TC) = (S t - S C ) / (x - C)

[0415] where x and C are the concentrations of the test sample and control sample, respectively. This difference analysis accounts for non-specific adsorption and isolates the true signal.

[0416] Systematic experiments are performed across a range of known concentrations to train the ADNC algorithm through difference analysis. Data from the double determinations are collected to establish a relationship between the test and control regions. The test sample highlights experimental variations at specific concentrations, forming the basis for gradient analysis, which is generally induced by "pH": pH variation, "T": temperature fluctuation, "k off ": dissociation constant of the specific antibody, "γ": interfacial energy, and "I": ionic concentration variation. "N use ": number of microbeads used, "N anl ": number of microbeads analyzed.

[0417] During the training process, the algorithm learns to adjust the test readings based on the control response. The software has an initial setup, which involves inputting the concentration values of the quantitative sample (Q) and the control sample (C). The process begins with a double determination of the test sample relative to the quantitative sample to obtain the first test signal (S t1 ) and the quantitative signal (S Q ). This is followed by a double determination using the control sample (C) to obtain the second test signal (S t1 ) and the control signal (S C ). Finally, a double determination with a blank sample provides the third test signal (S t3 ) and the background signal (S0). After repeating the double determinations, the software records the signal gradients f'(CQ), f'(C0), and f'(Q0) for the specific biomarker as follows.

[0418]

[0419] During the initial setup, the software will iterate through a threshold range to ensure

[0420] f'(CQ) > f'(C0) > f'(Q0)

[0421] This can be understood in the context of the Mean Value Theorem for Lagrange's Differentials, which explains the potential link between the average rate of change of a function and its instantaneous rate at different points ( Figures 8A-8C ).

[0422] By calculating the average difference in signal strength between consecutive points in the field, the algorithm can fine-tune its correction factors to account for local variations caused by measurement errors. Based on the available concentrations (C, Q) and corresponding fluorescence detection signals, we establish a function for x in terms of δ.

[0423]

[0424] Next, validation is performed using a cross-validation technique to assess the accuracy of the model. The parameters are fine-tuned by gradient descent optimization to minimize the variance between concentration estimates in repeated tests. The loss function used in the optimization process is defined as:

[0425]

[0426] where x i represents individual concentration estimates, and is their mean. The correction factor δ is updated iteratively using the following equation:

[0427]

[0428] This iterative process ensures convergence to the optimal correction factor that minimizes the difference and gradually reduces the loss, helping it find the best model parameters δ1, δ2, δ3. The process involves calculating the partial derivatives of the loss function with respect to δ and iteratively updating the value of δ based on these derivatives until δ old refers to the current value of the parameter from the previous iteration. is the gradient of the loss function with respect to the parameter, indicating how the loss function changes with a small change in δ. α is the learning rate that determines the size of each step taken during the update. δ new is the updated parameter value used in the next iteration. Typically, “1” is used as the initial value for δ starting from an ideal state. Using the optimized correction factors (δ1, δ2, δ3), the program calculates the values of the unknowns x1, x2, x3. Finally, the program outputs the accurate concentration by taking the mean Figure 8B ). After three repeated bilateral assays, the standard curve function f(x) with the optimized signal gradient f'(x) is established for a specific biomarker, allowing subsequent samples tested in bilateral assays to be quickly calculated for concentration. The software automatically verifies the accuracy of the concentration output results. If it detects values that exceed the measurable concentration range, the system will output “invalid data” instead of generating erroneous data. By following these steps, the ADNC model can help understand and correct errors caused by environmental variables in single-molecule protein detection, thereby improving the accuracy and reliability of the detection.

[0429] The disclosed compositions and methods can be further understood by the following numbered paragraphs.

[0430] 1. A microfluidic chip comprising:

[0431] a microfluidic platform comprising one or more microfluidic flow paths, wherein at least one of the microfluidic flow paths comprises an inlet channel, a flow-trap junction (FTJ) structure, and an outlet channel, wherein the microfluidic flow path is configured for moving fluid from the inlet channel into the FTJ structure and from the FTJ structure into the outlet channel,

[0432] wherein the inlet channel is wider at a location where the fluid moves from the inlet channel into the FTJ structure than at a location where the fluid is introduced into the inlet channel, wherein the inlet channel comprises a plurality of hydrophobic micro-pillar structures,

[0433] wherein the FTJ structure comprises a flow layer and a trap layer, wherein the flow layer is in contact with, on top of, and overlapping the trap layer,

[0434] wherein the flow layer comprises a plurality of flow microfluidic channels, each flow microfluidic channel comprising a top, a sidewall, and an opening on a bottom, wherein the surface of the flow microfluidic channels is hydrophobic, wherein the flow microfluidic channels allow micron-scale particles and nanoscale objects to pass freely, wherein the fluid flows in the same direction in all of the flow microfluidic channels,

[0435] wherein the flow microfluidic channels are parallel to each other, wherein the trap layer comprises a plurality of trap microfluidic channels, each trap microfluidic channel comprising a bottom, a sidewall, and an opening on a top, wherein the trap microfluidic channels are parallel to each other, wherein the surface of the trap microfluidic channels is hydrophilic, wherein the fluid flows in the same direction in all of the trap microfluidic channels,

[0436] wherein the flow microfluidic channels are not parallel to the trap microfluidic channels, wherein the flow microfluidic channels and the trap microfluidic channels allow fluid to move from the flow microfluidic channels into the trap microfluidic channels via the openings on the bottom and the openings on the top, respectively,

[0437] wherein the trap microfluidic channels, the transition from the flow microfluidic channels to the trap microfluidic channels, or a combination of both the trap microfluidic channels and the transition from the flow microfluidic channels to the trap microfluidic channels are configured to allow the nanoscale objects to pass through the trap microfluidic channels, whereby the nanoscale objects that have passed flow into the outlet channel,

[0438] wherein the capture microfluidic channel, the transition from the flow microfluidic channel to the capture microfluidic channel, or a combination of the capture microfluidic channel and the transition from the flow microfluidic channel to the capture microfluidic channel is configured to capture the micrometer-scale particles in the capture microfluidic channel, whereby the captured micrometer-scale particles combine to form an array within the FTJ structure.

[0439] 2. The chip of paragraph 1, wherein the sidewalls of the capture microfluidic channel are straight and parallel to each other, wherein the height of the capture microfluidic channel is less than the diameter of the micrometer-scale particles.

[0440] 3. The chip of paragraph 2, wherein the height of the capture microfluidic channel is greater than the diameter or long dimension of the nanometer-scale objects.

[0441] 4. The chip of paragraph 1, wherein the sidewalls of the capture microfluidic channel are not straight, such that the width of the capture microfluidic channel varies in a regular pattern along its length, wherein the pattern of width variation forms constrictions in the width of the capture microfluidic channel, wherein the width of the constrictions is less than the diameter of the micrometer-scale particles, wherein the width of the constrictions is greater than the diameter or long dimension of the nanometer-scale objects.

[0442] 5. The chip of paragraph 4, wherein all or a subset of the constrictions overlap the flow layer between some or each of the openings on the floor of an adjacent flow microfluidic channel.

[0443] 6. The chip of paragraph 4 or 5, wherein all or a subset of the constrictions overlap the openings on the floor of some or each flow microfluidic channel.

[0444] 7. The chip of any of paragraphs 4-6, wherein a subset of the constrictions overlap the openings on the floor of some or each flow microfluidic channel and a subset of the constrictions overlap the flow layer between some or each of the openings on the floor of an adjacent flow microfluidic channel.

[0445] 8. The chip of any of paragraphs 4-6, wherein a subset of the constrictions overlap the openings on the floor of each flow microfluidic channel and a subset of the constrictions overlap the flow layer between each of the openings on the floor of an adjacent flow microfluidic channel.

[0446] 9. The chip of any of paragraphs 6-8, wherein the constriction overlapping the opening on the bottom of the flow microfluidic channel forms a small capture inlet on the downflow side of the opening and a large capture inlet on the downflow side of the opening for alternating capture microfluidic channels, wherein the small capture inlet is sized smaller than the diameter of the micron-scale particles, wherein the small capture inlet is sized larger than the diameter or long dimension of the nanoscale objects, wherein the large capture inlet is sized larger than the diameter of the micron-scale particles.

[0447] 10. The chip of any of paragraphs 1-3, wherein the flow microfluidic channel and the capture microfluidic channel are at a right angle to each other.

[0448] 11. The chip of any of paragraphs 1-3, wherein the flow microfluidic channel and the capture microfluidic channel are at an oblique angle to each other.

[0449] 12. The chip of any of paragraphs 1-3, wherein the flow microfluidic channel and the capture microfluidic channel are at an angle of 60° to 90°, 70° to 90°, 80° to 90°, 85° to 90°, 87° to 90°, 88° to 90°, or 89° to 90° to each other.

[0450] 13. The chip of any of paragraphs 1-12, wherein a sidewall of the capture microfluidic channel is angled toward the upflow end of the flow microfluidic channel.

[0451] 14. The chip of any of paragraphs 1-13, wherein the microfluidic flow path further comprises a sample inlet, wherein the microfluidic flow path is configured for moving fluid from the sample inlet into the inlet conduit.

[0452] 15. The chip of any of paragraphs 1-14, wherein the microfluidic flow path further comprises a plurality of outlet channels, wherein the microfluidic flow path is configured for moving fluid from the capture microfluidic channels into the outlet channels and from the outlet channels into the outlet conduit.

[0453] 16. The chip of paragraph 15, wherein each capture microfluidic channel is flowably connected to a different one of the outlet channels.

[0454] 17. The chip of any of paragraphs 1-14, wherein the flow layer of the FTJ structure further comprises a plurality of outlet channels, wherein the outlet channels are interspersed among and parallel to the flow microfluidic channels, wherein the outlet channels each comprise a top, sidewalls, and an opening on a bottom, wherein the outlet channels and the capture microfluidic channels allow fluid to move from the capture microfluidic channels into the outlet channels via the openings on the bottom and the openings on the top, respectively, wherein the microfluidic flow path is configured for fluid to move from the capture microfluidic channels into the outlet channels and from the outlet channels into the outlet conduit.

[0455] 18. The chip of paragraph 17, wherein the outlet channels alternate with the flow microfluidic channels in the flow layer of the FTJ structure.

[0456] 19. The chip of any of paragraphs 1-14, wherein the flow layer of the FTJ structure further comprises an outlet channel, wherein the outlet channel comprises a top, sidewalls, and an opening on a bottom, wherein the outlet channel overlaps with the downward flow end of the capture microfluidic channel, wherein the outlet channel and the capture microfluidic channel allow fluid to move from the capture microfluidic channels into the outlet channels via the openings on the bottom and the openings on the top, respectively, wherein the microfluidic flow path is configured for fluid to move from the capture microfluidic channels into the outlet channels and from the outlet channels into the outlet conduit.

[0457] 20. The chip of any of paragraphs 1-19, wherein the chip is transparent in one or more regions, wherein the chip is transparent in a region corresponding to the array.

[0458] 21. The chip of any of paragraphs 1-20, wherein the array comprises a surface area of 25 mm 2 or less.

[0459] 22. The chip of any of paragraphs 1-21, wherein the microfluidic platform comprises two microfluidic flow paths.

[0460] 23. The chip of paragraph 22, wherein the two microfluidic flow paths are flowably connected to a single fluid reservoir, wherein the fluid reservoir is flowably connected to respective inlet conduits of the two microfluidic flow paths.

[0461] 24. The chip of paragraph 22 or 23, wherein the two microfluidic flow paths are symmetrically disposed on the microfluidic platform.

[0462] 25. The chip of any of paragraphs 22-24, wherein the two microfluidic flow paths are symmetrically disposed on the chip.

[0463] 26. The chip of any of paragraphs 1-25, further comprising a plurality of microparticles.

[0464] 27. The chip of paragraph 26, wherein the microparticles comprise microbeads.

[0465] 28. The chip of paragraph 27, wherein the microbeads comprise magnetic microbeads or silica microbeads.

[0466] Optionally, wherein the silica microbeads comprise a diameter of about 2 pm to about 56 pm, inclusive, or about 2.97 pm to about 3.0 pm.

[0467] 29. The chip of any of paragraphs 26-28, wherein the microparticles further comprise a first capture agent specific for a target biomarker.

[0468] 30. The chip of paragraph 29, wherein the first capture agent is conjugated to the microparticle via streptavidin.

[0469] 31. The chip of paragraph 29 or 30, wherein one or more of the first capture agents bind to the target biomarker.

[0470] 32. The chip of any of paragraphs 26-31, wherein the microparticles have a diameter of between about 1 pm and about 5 pm, inclusive.

[0471] 33. The chip of any of paragraphs 29-32, wherein each of the microparticles comprises about 200,000 to about 400,000, inclusive, of the first capture agents.

[0472] 34. The chip of any of paragraphs 29-33, wherein the first capture agent is selected from the group consisting of a nucleic acid, a protein, a polypeptide, a lipid, a carbohydrate, and a small molecule.

[0473] Optionally, wherein the protein is an antibody.

[0474] 35. The chip of any of paragraphs 29-34, wherein the first capture agent comprises DNA or RNA or both.

[0475] 36. The chip of any of paragraphs 26-35, wherein the microparticles are present within the array.

[0476] 37. The chip of paragraph 36, wherein the microparticles are present in the array at a density of about 100 microparticles per pm 2 .

[0477] 38. The chip of any one of paragraphs 26 to 37, wherein the array comprises about 1 x 10 4 microparticles to about 1 x 10 6 microparticles, inclusive.

[0478] optionally about 4 x 10 5 microparticles.

[0479] 39. The chip of any one of paragraphs 1 to 38, wherein the array has an area of about 10 mm 2 to about 50 mm 2 , inclusive, optionally about 25 mm 2 .

[0480] 40. The chip of any one of paragraphs 1 to 39, further comprising a plurality of nanoscale objects.

[0481] 41. The chip of paragraph 40, wherein the nanoscale objects comprise a second capture agent specific for the target biomarker.

[0482] 42. The chip of paragraph 41, wherein one of the second capture agents binds to one or more target biomarkers bound to the first capture agent.

[0483] 43. The chip of paragraph 41 or 42, wherein the nanoscale objects further comprise a reporter molecule.

[0484] 44. The chip of paragraph 43, wherein the reporter molecule comprises a highly bright quantum dot nanoparticle.

[0485] 45. The chip of any one of paragraphs 1 to 44, wherein the microposts span a height of the inlet channel.

[0486] 46. The chip of any one of paragraphs 1 to 45, wherein the capture microfluidic channel comprises a hydrophilic polymer.

[0487] 47. The chip of paragraph 46, wherein the hydrophilic polymer comprises polyethylene glycol (PEG).

[0488] 48. A method for detecting a target biomarker in a fluid sample, the method comprising:

[0489] (a) introducing the fluid sample into one or more microfluidic flow paths of the microfluidic chip of any of paragraphs 1-25, wherein the fluid sample comprises a plurality of microparticles and a plurality of nanoscale objects, or is contacted with a plurality of microparticles and a plurality of nanoscale objects after its introduction, and

[0490] (b) performing digital chromatography on the chip,

[0491] wherein the digital chromatography identifies the presence of the target biomarker and / or the amount of the target biomarker in the fluid sample.

[0492] 49. The method of paragraph 48, wherein step (a) further comprises introducing a control sample comprising a known amount of the target biomarker into a different microfluidic flow path of the same chip.

[0493] 50. The method of any of paragraphs 48 or 49, wherein the performing of digital chromatography of step (b) comprises actuating fluid motion through the microfluidic flow paths in the microfluidic chip, wherein the motion filters and washes microparticles within the FTJ structures.

[0494] 51. The method of paragraph 50, wherein the filtering of microparticles in the FTJ structures captures microparticles within the FTJ structures and forms an array of microparticles within the FTJ structures.

[0495] 52. The method of paragraph 51, wherein step (b) further comprises imaging the array of microparticles within the microfluidic chip.

[0496] 53. The method of any of paragraphs 48-52, wherein the microparticles comprise microbeads.

[0497] 54. The method of paragraph 53, wherein the microbeads comprise magnetic microbeads.

[0498] 55. The method of any of paragraphs 48-54, wherein the microparticles further comprise a first capture agent specific for the target biomarker.

[0499] 56. The method of paragraph 55, wherein the first capture agent is coupled to the microparticles via streptavidin.

[0500] 57. The method of paragraph 55 or 56, wherein one or more of the first capture agents bind to the target biomarker.

[0501] 58. The method of any of paragraphs 48-57, wherein the nanoscale objects comprise a second capture agent specific for the target biomarker.

[0502] 59. The method of paragraph 58, wherein one of the second capture agents binds to one or more target biomarkers bound to the first capture agent.

[0503] 60. The method of paragraph 58 or 59, wherein the nanoscale object further comprises a reporter molecule.

[0504] 61. The method of paragraph 60, wherein the reporter molecule comprises a high- brightness quantum dot nanoparticle.

[0505] 62. The method of any one of paragraphs 57 to 61, wherein step (b) further comprises detecting and measuring the target biomarker bound to the first capture agent on the microscale particle within the array.

[0506] 63. The method of any one of paragraphs 55 to 62, further comprising, prior to step (a),

[0507] (i) incubating the fluid sample with the microscale particle for a period of time and the incubation is effective to bind the target biomarker to the first capture agent; and

[0508] (ii) optionally washing the microscale particle.

[0509] 64. The method of paragraph 63, further comprising, prior to step (a), contacting the microscale particle with the nanoscale object for a period of time and the contacting is effective to bind the target biomarker to the second capture agent.

[0510] 65. The method of any one of paragraphs 48 to 64, wherein step (a) and / or (b) comprises a total time of between about 10 seconds and 1000 seconds, inclusive, optionally about 180 seconds.

[0511] 66. The method of any one of paragraphs 48 to 65, wherein the method detects at least 90%, e.g., 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99%, or 100% of the biomarker within the fluid sample.

[0512] 67. The method of any one of paragraphs 48 to 66, wherein the fluid sample comprises the biomarker at a concentration of about 1 IU / mL.

[0513] 68. The method of any one of paragraphs 48 to 67, wherein the fluid sample comprises the biomarker at a concentration of about 10 -18 M to about 10 -5 M, inclusive.

[0514] 69. The method of any of paragraphs 48-68, wherein the fluid sample comprises a volume of between about 1.0 pL to about 100 pL, including endpoints, optionally about 50 pL.

[0515] 70. The method of any of paragraphs 48-69, wherein the sample comprises 1 to 100 different biomarkers, optionally 1 to 10 different classes of biomarkers.

[0516] 71. The method of any of paragraphs 48-70, wherein a biomarker comprises an immunoglobulin.

[0517] 72. The method of any of paragraphs 48-71, wherein a biomarker comprises IgE.

[0518] 73. The method of any of paragraphs 48-72, wherein a biomarker comprises an interferon.

[0519] 74. The method of any of paragraphs 48-73, wherein a biomarker comprises TNF-a.

[0520] 75. The method of any of paragraphs 48-74, wherein a biomarker comprises a tumor antigen.

[0521] 76. The method of paragraph 75, wherein the tumor antigen is selected from the group consisting of prostate specific antigen (PSA), carcinoembryonic antigen (CEA), alphafetoprotein (AFP), cancer antigen 125 (CA-125), cancer antigen 19-9 (CA19-9), human epidermal growth factor receptor 2 (HER2 / neu), cytokeratin 19 fragment (CYFRA21-1), B-Raf proto-oncogene, and epidermal growth factor receptor (EGFR).

[0522] 77. The method of any of paragraphs 48-73, wherein a biomarker comprises a biomarker derived from a pathogen.

[0523] 78. The method of paragraph 77, wherein the pathogen is selected from the group consisting of bacteria, fungi, viruses, and protozoa.

[0524] 79. The method of paragraph 78, wherein the virus is a coronavirus.

[0525] Optionally, wherein the coronavirus is a novel coronavirus.

[0526] 80. The method of any of paragraphs 48-79, wherein the target biomarker within the fluid sample is derived from a bodily fluid from a subject.

[0527] 81. The method as described in paragraph 80, wherein the body fluid is selected from the group consisting of: blood, sweat, semen, serum, bile, saliva, tears, pus, mucus, pleural fluid, vitreous fluid, cerebrospinal fluid, synovial fluid, amniotic fluid, and urine.

[0528] 82. The method as described in paragraph 81, wherein the bodily fluid is tears.

[0529] This disclosure will be further understood by referring to the following non-limiting embodiments.

[0530] Example

[0531] Example 1: Design and fabrication of BiZi-FIA microfluidic chip

[0532] A BiZi-FIA-based device was designed to separate and accurately detect single molecules of biomarkers with low sample volumes. Figures 1A-1E ).

[0533] method

[0534] Sylgard 184 silicone elastomer kit was purchased from Dow Corning, USA. Superparamagnetic beads covalently bonded with carboxylic acids and surface epoxy groups were purchased from Thermo Fisher Scientific, USA. M-270 was used without any further treatment. Artificial tears (A7720) were purchased from Solarbio, China.

[0535] Microfluidic devices for BiZi-FIA were designed and fabricated using conventional and soft lithography techniques. The fabrication process begins with the careful design of a flow / trapping layer mask, which is specifically tailored to define flow channels and trapping structures important for filter-like flow and trapping bonding. The mask is then materialized on an apt substrate using photolithography.

[0536] For the flow layer, MicroChem's SU-8 2025 photoresist was used, renowned for its ability to achieve a thickness of 5.5 μm. The substrate underwent spin coating at 4000 rpm for 30 seconds, followed by soft baking at 65°C for 5 minutes. The flow / trapping layer mask was then finely aligned and subjected to an intensity of 100 mJ / cm². 2 The mask was then exposed to UV light. Afterward, it was post-baked at 95°C for 5 minutes and developed with MicroChem SU-8 developer for 2–3 minutes. Washing was performed on the unexposed areas, ultimately revealing the envisioned flow channel pattern.

[0537] A mold for the trapping layer was prepared using MicroChem's SU-8 3005, which was specifically chosen because of its expertise in producing structures with a height of 3 μm.

[0538] A 45-degree angle lithography technique is used, where the flow / trapping layer mask is precisely aligned at this angle to ensure that UV light exposure is performed at the critical 45-degree angle. After soft baking at 65°C for 2 minutes, the UV light is applied at approximately 90 mJ / cm². 2 UV exposure was then performed. Subsequent post-exposure baking was carried out at 95°C for 3 minutes. The developed resist was then used as the base mold for the trapping layer.

[0539] The Sylgard 184 silicone elastomer kit (Dow Corning, USA) was used to prepare the trapping layer by mixing the base material and curing agent in a 10:1 ratio. After fine mixing and subsequent degassing, the mixture was then cast into a mold and cured at 65°C for 4–6 hours. After curing, the PDMS structure was carefully demolded, ensuring alignment with the incident angle of the photolithography to expose the complex trapping pattern.

[0540] To ensure robust and durable bonding, the trapping and flow layers were plasma-treated and precisely aligned prior to bonding. To enhance the biocompatibility of the trapping layer and mitigate nonspecific bonding, the surface was modified using Sigma-Aldrich PEG 2000. A PEG solution was strategically introduced through the outlet port of the trapping layer, while the inlet port of the flow layer was firmly sealed. This strategic approach ensured that the PEG solution was confined within the trapping layer. After an 8-hour incubation period, the equipment was flushed to remove any residual PEG.

[0541] Chip manufacturing

[0542] Photolithography is used to ablate simple microstructure patterns on silicon wafers.

[0543] PDMS and hardener were mixed at a weight ratio of 10:1, and the mixture was degassed in a vacuum to remove air bubbles.

[0544] The PDMS mixture was poured onto the ablated silicon wafer and cured at 70°C for 30 minutes.

[0545] After curing, the PDMS layer is peeled off from the silicon wafer to form a PDMS convex shape.

[0546] The PDMS mold was treated with O2 plasma for 1 minute and then immersed in 0.1M PEG solution (average molecular weight 6000 g / mol) to form a separatory membrane.

[0547] Repeat the PDMS pouring and curing steps on the convex mold to produce the concave PDMS mold.

[0548] Finally, the PDMS mold is bonded to a blank PDMS substrate to form the microfluidic chip.

[0549] Surface treatment

[0550] Preparation of surface treatment reagent: A 0.1 M solution of polyethylene glycol (PEG) with an average molecular weight of 6000 g / mol is prepared. This solution is used as the surface treatment reagent.

[0551] Setting up the surface treatment system: A surface treatment reservoir connected to the inlet of the surface treatment channel is arranged in the microfluidic chip. Ensure that the system is sealed and leak-proof.

[0552] Connecting a precision pump to the surface treatment reservoir to control the flow of the PEG solution.

[0553] Starting the surface treatment process: Start the precision pump to introduce the PEG solution into the surface treatment channel. The flow rate should be maintained at a consistent and controlled speed to ensure uniform distribution of the reagent.

[0554] As shown in Figures 1A-1E , Figures 2A-2I and Figures 3A-3I , the PEG solution flows through the array capture channels simultaneously, which are parallel to the surface treatment channel. This ensures that the channels are uniformly exposed to the treatment reagent.

[0555] As depicted in Figure 2E , the counter-oriented channels positioned at the end of each flow channel create a turbine valve structure, where the centrally positioned valve in the flow layer induces a controlled hydrodynamic resistance to direct fluid laterally into the capture network. This system facilitates selective bead capture through an adaptive zigzag path dynamically controlled by the capture occupancy state: unoccupied capturers exhibit minimal resistance, enabling beads to enter and settle, while occupied capturers generate an elevated resistance profile, redirecting fluid recirculation to the main channel and diverting subsequent beads to adjacent available sites. This self-regulating process ensures spatially ordered bead deposition, as the hydrodynamics autonomously adjust to real-time capture availability. Upon saturation of all capture sites, excess beads undergo controlled evacuation via the end flow conduit, preventing overcrowding while maintaining optimal particle density. This architecture operates with autonomous fluid regulation, leveraging occupancy-dependent resistance modulation, self-guided particle routing, and systematic overfill prevention to achieve non-mechanical flow control, self-limited deposition, and scalable array formation without reliance on external control systems or active monitoring components.

[0556] Maintaining flow rate and treatment time: Maintain a constant flow rate of the PEG solution throughout the treatment process. The recommended flow rate parameter can be approximately 0.5 mL / min, but this can vary based on the specific design and dimensions of the microfluidic chip.

[0557] The duration of the surface treatment process should be carefully timed. A typical treatment time can be 25 to 30 minutes. This duration ensures adequate surface modification without overexposure.

[0558] Reagent completion and drain: After the treatment duration is complete, gradually reduce the flow rate and finally stop the pump.

[0559] Allow the PEG solution to completely drain from the outlet port of the microfluidic chip. Ensure that there are no residual treatment reagents within the channel.

[0560] Post-treatment operation: Rinse the surface-treated channel with deionized water to remove any unbound PEG molecules.

[0561] Dry the microfluidic chip under a gentle stream of nitrogen gas to remove any remaining moisture.

[0562] Verification of surface treatment: Assess the success of the surface treatment by measuring the contact angle of a water droplet on the treated surface. A significant decrease in the contact angle indicates successful hydrophilic modification.

[0563] Optionally, use additional analytical techniques, such as SEM (scanning electron microscopy) or FTIR (Fourier-transform infrared spectroscopy), for further verification and analysis of surface features.

[0564] Measure the contact angle of a 5 μL droplet of deionized water on differently treated PDMS substrates using a CCD camera.

[0565] Evaluate the hydrophilic properties of the treated surface by recording the contact angle of a plasma-PEG treated surface over a period of up to 420 hours.

[0566] Examine the morphology of PDMS before and after surface treatment using a SEM microscope.

[0567] Compare PDMS samples before and after treatment using FTIR (Fourier-transform infrared spectroscopy).

[0568] This detailed surface treatment procedure aims to enhance the hydrophilicity of PDMS surfaces in microfluidic chips, thereby minimizing non-specific adsorption and improving chip performance for various applications.

[0569] Detection antibody coupled to super-bright fluorescent label

[0570] To prepare the detection antibody coupled to streptavidin-functionalized quantum dot nanoparticles, follow the standardized protocol reported in the literature.

[0571] Initially, prepare the streptavidin-functionalized quantum dot nanoparticles according to the manufacturer's instructions (Catalog No. DNQ-N008, DiagNano TM, CD Bioparticles) to prepare a working solution of streptavidin quantum dot nanoparticles with an emission peak of 625 nm. The prepared bead solution (50 pL) was centrifuged at 5000 rpm for 2 minutes at room temperature to remove the supernatant.

[0572] Thereafter, the beads underwent a thorough washing process in which they were gently vortexed and then centrifuged three times using 1 mL of phosphate buffered saline (PBS).

[0573] Subsequently, 25 pg of antibody solution was introduced to the washed beads. The tube containing the mixture was gently pipetted to ensure even mixing. The tube was then incubated for 1 hour at room temperature on an orbital shaker, allowing sufficient time for the antibody to effectively bind to the streptavidin-coated Qt nanoparticles. Once the incubation period was complete, the supernatant was removed after centrifugation.

[0574] Afterwards, the beads were subjected to three additional washes each using 1 mL of PBS to remove any unbound antibody from the beads. After the final wash, the antibody-coupled Qt nanoparticles were resuspended in 100 pL of PBS for immediate use or resuspended in a suitable storage buffer for long-term storage.

[0575] In summary, the antibody-coupled quantum dot nanoparticles were primed and ready to be integrated into further experimental applications, such as immuno-fluorescence assays or protein detection.

[0576] Coupling of capture antibody on beads

[0577] A method for covalently immobilizing an antibody on M-270 epoxy resin surface is set forth. It must be noted that the protocol described herein was designed and executed strictly according to the guidelines provided in the antibody coupling kit Dynabeads (3 pm diameter carboxyl and epoxy-linked superparamagnetic beads) from Thermo Fisher Scientific.

[0578] Initially, a series of buffers and solutions were prepared by the antibody coupling kit, including CI for bead washing and preparation, C2 as the activating agent for coupling, LB as a low salt buffer for non-specific binding removal, HB as a high salt buffer for enhanced binding, and SB for long-term bead storage. These analytical grade reagents were confirmed to be compatible with protease and phosphatase inhibitors and were stored at 2-25 °C.

[0579] For a 5 mg bead preparation, exactly 5 mg of M-270 epoxy resin to prevent condensation. The beads are then washed with CI solution to ensure their surface is ready for coupling. After magnetic separation, a volume of antibody is added and filled up to a remaining volume of 250 μΐ with CI. Another 250 μΐ of C2 activator solution is introduced, promoting the covalent attachment of the antibody to the surface of the beads. The mixture is then incubated overnight at 37°C on a roller.

[0580] The next day, the tubes are placed on a magnet and the supernatant is discarded. The beads are washed with HB and LB in succession to remove non-specifically bound material and to enhance specific binding. A short SB wash is performed, followed by a long SB wash, in which the beads are incubated for 15 minutes at room temperature. Finally, the beads are resuspended in 500 μΐ of SB, ensuring their stability and longevity. The final bead concentration is 10 mg antibody-coupled beads / ml.

[0581] Results

[0582] The designed one-way microcapillary flow allows for simultaneous transportation and immobilization of many microbeads in the chip. The innovation of this device is that it is portable and low cost, as it does not require connection with a syringe pump and other additional controllers, and is easy to mass-produce by nanoimprinting. Compared with single-pipe perfusion with negative injection pressure, parallel perfusion of multiple pipes is more efficient.

[0583] Further, the drain pipe is parallel to the extension direction of the array pipe. That is, the extension directions of the guide pipe and the capture pipe are consistent. The array chamber captures particles in the liquid, and the excess liquid flows into the drain pipe. The drain pipe includes a liquid reservoir tank on the left and right sides, which is configured to store the liquid after passing through the array chamber. The drain pipe has a row of 3D recessed cavities. When the cross section of the cavity is half-elliptical and inclined, shear force is more likely to shear the liquid, and the liquid enters the cavity. Compared with the vertical cavity, it is difficult to produce bubbles and dead zones in the inclined cavity. Figure 2F It is shown that the inclined pipe can better reduce the pinning effect than the vertical pipe, thereby achieving faster liquid delivery. In addition, it has the ability to utilize three-dimensional surface energy gradients and Laplace pressure differences to achieve the spontaneous flow of liquid, so that the liquid fills the liquid reservoir under the capillary force of the biomimetic pitcher plant structure without the need for a complex mechanical syringe pump device, which has the characteristics of simple operation and high repeatability.

[0584] Upon entering the middle array region, the single-molecule immunocomplexes captured by the magnetic beads are subjected to the contrasting properties of the array region surface. The hydrophilic and anti-static adsorption properties of this region, in contrast to the hydrophobic properties of the flow separation zone, effectively prevent the non-specific adsorption of quantum dot microspheres in the array region. As a result, they continue to be hydrodynamically expelled from the chip. The chip, which is specifically designed for this purpose, effectively removes the remaining non-specific impurities in the separation zone of its flow layer. The design of the chip takes advantage of the hydrophobic properties of polydimethylsiloxane (PDMS), ensuring the effective removal of non-specifically bound proteins and other impurities. This design significantly improves the signal-to-noise ratio and sensitivity of the detection.

[0585] Due to the combination of surface interactions (electrostatic, van der Waals, steric, hydrophobic, and hydration forces), most of the inoculated quantum dot nanospheres adhere to the microwell surface, thus not exhibiting Brownian motion.

[0586] Analysis verification of the assay

[0587] In the first step of incubation, different target proteins are detected one by one by changing different antibodies on the microbeads.

[0588] The COVID-19 N protein is selected to test the lower detection limit, and the performance of the digital ICA system is compared with other methods. At the same time, two eye biomarkers, TNF-a and LCN-1, are selected to evaluate the ability of digital ICA in ophthalmic applications.

[0589] To draw the standard curve of COVID-19 N protein, TNF-a and LCN-1 detection, a series of gradient dilution preparations are prepared for the biomarker samples, and quantum dots are labeled on the detection antibodies. Then the magnetic beads are combined with the antigen and quantum dot label to form a sandwich structure complex. In this step, each of the three different types of capture microbeads is placed in an EP tube on a magnetic stand to remove excess liquid, and the detection antibody solution is added to the tube. The microbead suspension with different standard concentrations and samples are mixed in separate tubes by shaking and incubated at room temperature for 30 minutes. In the washing step, the magnetic beads are removed and washed three times with PBS and Tween 20. Finally, the three magnetic beads are dispersed in 40 pL of PBS for detection.

[0590] Finally, the single-molecule signals are automatically analyzed using custom-made smart software. The algorithm of this software is that the horizontal coordinate corresponds to the left-to-right and top-to-bottom position of each pixel on the image. The vertical coordinate represents the gray scatter points. When the signal intensity exceeds the threshold value, the signal will be counted. Using 9 pictures, a total of 200,000 particles can be scanned in the entire array region, and more than 90% of the particles can be analyzed. Each concentration is repeated six times, and then the ratio between the number of single molecules and the total number of particles is calculated to provide the average molecules per bead (AMB).

[0591] The lowest limit of detection was determined in the gradient concentration data that satisfied the linear relationship of 90% or more of the average molecules per bead.

[0592] The results show that the detection limit of COVID-19 N protein can reach 15 attomolar using the avidin coating system. And compared with the traditional carboxyl coating system, the detection limit increases by 6 times. Among different proteins, the detection limit can be affected by the dissociation constant and molecular weight of different proteins. Generally, lower dissociation constant values lead to higher sensitivity. At the same time, the sensitivity of biomarker detection is directly related to the affinity of antibodies and aptamers.

[0593] To determine the lowest limit of detection (LOD) in the concentration gradient test, a specific method using antibody-coupled magnetic beads and quantum dots was used. For each concentration gradient tested, 2 μL of magnetic bead solution was pipetted from a stock solution with a final bead concentration of 10 mg antibody-coupled beads / mL. This aliquot containing approximately 340,000 beads was first mixed with 10 μL of antigen sample, then diluted in 10 mM PBS at pH 7.4 to achieve a specific concentration in the range of 0 fg / mL to 100 pg / mL. The samples with different concentrations were then incubated at room temperature for 30 minutes to ensure optimal binding between the antigen and the capture antibodies on the magnetic beads.

[0594] After that, a washing step was performed on the samples with 150 μL of wash buffer (10 mM PBS at pH 7.4 containing 0.01% Tween-20 and 0.01% BSA) to remove any unbound antigens. Subsequently, the magnetic beads were separated from the supernatant using a magnet.

[0595] Next, 2 μL of quantum dot-labeled detection antibody (concentration: 25 ug / 100 uL) was diluted in 100 μL of buffer solution (50 mM Tris, 0.1% BSA, and 0.05% Tween-20, pH 7.4) and then added to each sample. After ensuring thorough mixing, the samples were incubated at room temperature for 30 minutes. This incubation period facilitated the binding of the quantum dot-labeled detection antibody to the antigens previously captured on the magnetic beads.

[0596] After incubation, another washing step was performed following the same protocol as above to remove unbound quantum dot-antibody complexes. The magnetic beads were then separated from the supernatant using a magnet and resuspended in 50 μl of buffer. The resuspended bead-protein-quantum dot complex was then carefully introduced into the TF-JA (filter-like flow and capture junction array) chip using a micropipette.

[0597] All experiments were performed more than 3 times. Dry eye samples were measured in duplicate or triplicate at individual time points for longitudinal cytokine profiling tests.

[0598] The BiZi-FIA device was used to successfully detect coronavirus antigen N protein; and TNF-a and LCN-1.

[0599] Example 2: Confirmation of design parameters of flow-trapping junctions

[0600] To ensure effective bead capture by the double-sided zigzag flow, the electrical resistance in the trapping direction (Rt) should be smaller than the electrical resistance in the flow direction (Rf). By calculating and assigning these resistance ratios, the design of the flow and trapping channels can be guided to optimize the capture efficiency and flow performance. The electrical resistance in the flow direction is defined as R f (x,y), and the electrical resistance in the trapping direction is defined as R t (x,y), as shown in Figure 2B As the boundary conditions of the FT-JA coordinate system, the origin (0,0) represents the final FT junction closest to the outlet, where the minimum electrical resistance is denoted as R f (0,0) = AR fo and R t (0,0) = AR to These values are affected by the geometry of the flow and trapping units, respectively [23, 24]. Considering the parallel relationship between the flow and trapping directions at each junction, the electrical resistance R j (x,y) at one junction is expressed as:

[0601]

[0602] For positions with x > 1 and y > 0, the electrical resistance in the flow direction R f (x,y) is determined by the electrical resistance R j (x-1,y) of the previous junction along the x-axis (flow direction) and the electrical resistance AR f (x,y) of the current flow unit (which is determined by the geometry of the flow unit at (x,y)):

[0603]

[0604] Similarly, for positions with x > 0 and y > 1, the electrical resistance in the trapping direction R t (x,y) is determined by the electrical resistance R j (x,y-1) of the previous junction along the y-axis (trapping direction) and the electrical resistance AR t (x,y) of the current trapping unit (which is determined by the geometry of the trapping unit at (x,y)):

[0605]

[0606] Based on these recursive relations, the capture efficiency of FT-JAs in BiZi-FIA systems can be predicted by the resistance ratio R t (x,y) / R f (x,y) to R t (x,y) / R f (x,y) < 1, the corresponding junction effectively works to capture microbeads. The total resistance R total ( Figures 2A-2I ) of FT-JAs is calculated by equivalent circuit simulation.

[0607]

[0608] Simulation results ( Figure 2J ) show that by minimizing the resistance in the capture direction, a higher number of capture channels relative to flow channels is maintained to reduce R total in favor of microbead capture. Specifically, AR t (x,y) < AR f (x,y) scenario achieves the maximum capture efficiency. To facilitate this, we designed tilted capture channels that preferentially direct flow lines into the capture layer. In contrast, vertical capture channels increase the resistance and hinder capture efficiency. Therefore, the tilted capture channel design optimizes the capture efficiency within BiZi-FIA systems ( Figure 2F ).

[0611] To further optimize the FT-JA system, we investigated the impact of varying the flow channel width. The goal was to determine whether the flow channel width affects the fluid velocity and, consequently, the capture efficiency. The results show that while varying flow channel widths affect the fluid velocity at a specific pressure differential ( Figures 3A-3D ), the overall capture efficiency remains stable ( Figures 3E-3F ). Specifically, at lower fluid velocities, wider channels maintain a consistent capture efficiency of 90% (n = 3), allowing for the capture of microbeads in an orderly and uniformly spaced manner, resulting in an array with a density of 5.6 x 10^3 beads / mm 2 ( Figure 3E ). At higher fluid velocities, narrower channels also maintain a high capture efficiency of 90% (n = 3), leading to a denser complementary array with a density of 9.7 x 10^3 beads / mm 2 ( Figure 3G ). This stability is attributed to the optimized resistance ratio R t (x,y) / R f (x,y), which ensures effective microbead capture regardless of channel width and fluid velocity.

[0612] The FT-JA design ensures robust and efficient bead capture, whether the beads are highly confined (Ht < d, Figure 2G ) or width-confined (Wt < d, Figure 2C ). Experimental results Figure 3J ) show that maintaining 90% capture efficiency results in an average of 365,466 beads captured across multiple batches with a coefficient of variation (CV) of 5.9%. Within individual dual-sided arrays on a single chip, the average CV of bead capture is as low as 0.4%, highlighting the high consistency and reliability of the BiZi-FIA system.

[0613] The FT-JA array in the BiZi-FIA system utilizes hydrodynamics to achieve efficient bead capture and immobilization. Its filter-like flow and capture junction design ensures unidirectional flow, minimizes backflow, and improves capture efficiency. The tilted capture channels and parallel multi-passage design support the formation of dense bead arrays, maintaining high capture efficiency over varying channel widths and fluid velocities. This robust and reliable system is an ideal choice for high-throughput and ultra-sensitive bioanalytical applications, providing a portable and cost-effective solution for single-molecule detection. Performance evaluation of the BiZi-FIA system with channels of varying widths for denser bead capture is shown in Figures 3A-3K , showing simulated pressure gradients driving dual-sided zigzag flow through FT-JA, simulated 2D multi-zigzag and 3D single-zigzag flow paths, cross-sectional views of flow channels with different widths, and variance analysis of bead capture in dual-sided arrays, demonstrating high stability between batches.

[0614] Example 3: On-chip filtration and washing for magnetism-free immunoassay

[0615] A key feature of the BiZi-FIA platform is its automated filtration and washing mechanism, which effectively removes non-specific impurities while preserving specific immune complexes on beads. The BiZi-FIA system achieves large-scale bead arrays within the cross-grid of a dual-layer flow and capture junction array (FT-JA). The capture channels overlap with the flow channels, forming a grid-like array chamber. This filter-like bead separator system efficiently transports, immobilizes, and arranges beads into the desired array for bioanalytical applications. It facilitates loading and separation of beads associated with individual enzyme molecules using only hydrodynamic forces. In comparison to single-passage perfusion with negative injection pressure, our multi-channel parallel perfusion significantly improves efficiency.

[0616] Upon entering the middle array region, the single-molecule immuno-complexes captured by the beads interact with the contrasting surface properties of the array region. The hydrophilic and anti-static surfaces prevent non-specific adsorption of the quantum dot (QD) microspheres, ensuring their removal from the chip by hydrodynamics. Additionally, the hydrophobic nature of the polydimethylsiloxane (PDMS) in the flow separation zone effectively eliminates non-specifically bound proteins and impurities, significantly improving the signal-to-noise ratio (SNR) and detection sensitivity. Figures 4A-4C The process is shown to involve on-chip separation, where the micro-pillars filter out non-specifically adsorbed particles. The filtration module utilizes hydrophilic and hydrophobic surfaces to enhance collision-based filtration, significantly reducing non-specific adsorption. The enhanced specificity of the platform is evident in the fluorescence detection phase, where the beads arranged in the detection chamber show minimal non-specific signal, even in the negative control sample (0 fg / mL). Traditional methods that rely on magnetic setup suffer from higher noise levels due to incomplete removal of non-specific nanobeads. By automating the filtration process and improving bead arrangement, the BiZi-FIA platform ensures high-throughput and efficient detection of even ultra-low concentrations of target molecules. An exemplary process of the BiZi-FIA platform for single-molecule detection with an automated filtration and washing mechanism is depicted in FIG. 4, showing a schematic of reagent preparation and incubation in the BiZi-FIA workflow, traditional separation methods with magnetic devices, droplet casting for immuno-complex analysis, the BiZi-FIA filtration and washing mechanism, effective filtration using hydrophobic and hydrophilic surfaces, and fluorescence images of BiZi-FIA showing efficient removal of non-specific impurities and clear separation of signal in the negative control (0 fg / mL). Traditional magnetic washing retains non-specific nanobeads, increasing background noise, and fluorescence detection of target molecules (10, 20, and 40 fg / mL) shows distinct signal dots and uniform bead arrangement in the magnified view.

[0617] The BiZi-FIA platform employing silica beads as the primary capture substrate exhibits a significant improvement in signal-to-noise ratio (SNR) compared to traditional magnetic bead-based assays. This enhancement is driven by the higher optical transparency and reduced non-specific binding propensity of silica beads, which are key factors in reducing background noise. As shown in FIG. 5, the light transmittance of the silica beads was chosen to be enhanced, which significantly improves the detection sensitivity of QD-labeled antibodies. This is demonstrated in a comparison of fluorescence microscopy images, where silica beads exhibit a higher density of visible QD-labeled events than magnetic beads. This improvement is attributed to the minimized light attenuation and quenching typically associated with magnetic beads. Light intensity measurements by a fluorescence spectrophotometer at 611 nm (FIG. 6) confirm the higher signal-to-noise ratio (SNR) of the BiZi-FIA platform compared to the traditional magnetic bead-based assay. Figures 5A-5B Figure 5A ​) further confirmed the advantages of silica beads, where the fluorescence intensity was almost 2-3 times higher than that observed with magnetic beads. This enhancement is particularly beneficial for single molecule detection, where the ability to distinguish between true signal and background noise is crucial. Microscopy imaging also highlighted the excellent signal uniformity across individual silica beads, emphasizing the ability to maintain high SNR at different magnification levels Figure 5B ).

[0618] Example 4: Superior signal-to-noise ratio of silica beads for large area fast imaging by CMOS sensor

[0619] To quantitatively understand the superior performance of silica beads and compare the minimum requirements of the optical system capable of detecting single molecule fluorescence signal on different bead types, we analyzed various parameters that affect the signal detection capability. These parameters include the numerical aperture (NA) of the objective lens, the exposure time and the detector sensitivity, with particular focus on the detection limit of the CMOS sensor used in commercial fluorescence microscopes. The photon emission rate (R em ) of QDs on beads is determined by the photon absorption rate (R abs ) and the quantum yield (QY):

[0620] R em = QY x R abs

[0621] Both silica and magnetic beads have a diameter of ~3 pm and were labeled with QDs exhibiting a high quantum yield (QY) of 85% at excitation and emission wavelengths of 550 nm and 611 nm, respectively (data from the manufacturer DIAGNANO TM , typically measured using fluorescence spectroscopy under specific excitation conditions). The photon emission rate (R em ) from a single QD was calculated using the following equation:

[0622]

[0623] where:

[0624] σ abs is the absorption cross-section of QDs (1 x 10 -16 cm 2 , from the data sheet provided by the manufacturer DIAGNANO TM , typically measured experimentally by measuring the absorption properties of QDs at a specific wavelength of 550 nm). I0 is the excitation light intensity (1 x 10 5 W / cm 2 ), equal to the ratio between the power of the laser beam (10 mW) and the area of the spot;

[0625] h vexcis the single photon energy at the excitation wavelength;

[0626] h is Planck's constant (6.626 x 10 34 J s); and

[0627] v exc is the photon frequency (5.45 x 10 14 Hz), which is equal to the ratio between the speed of light and the excitation wavelength.

[0628] For the highly transparent and exhibiting minimal quenching silica beads, the calculated photon emission rate (R em,silica ) is about 2.35 x 10 7 photons / s. In contrast, the magnetic beads introduce significant light absorption, scattering and quenching due to their magnetic material (iron oxide). These effects reduce the effective quantum yield and excitation / emission efficiency by about 50%.

[0629] eR em,magnatic = R abs x QY x QF x A exc

[0630] where:

[0631] QF is the quenching factor, representing the reduction in quantum yield due to energy transfer processes or other quenching mechanisms caused by the proximity of the magnetic material; and

[0632] A exc is the attenuation of the excitation light due to absorption / scattering by the magnetic beads.

[0633] Thus, the photon emission rate (R em,magnatic ) of the magnetic beads is reduced to about 5.89 x 10 6 photons / s, which is used to determine the number of detected photons (N detected ):

[0634] N detected = R em x A em x T x η detector x τ x f collection

[0635] where:

[0636] A em is the emission attenuation factor, representing the reduction in emitted fluorescence intensity due to absorption and scattering of the emitted photons by the bead material before reaching the detector;

[0637] T is the total transmission efficiency of the optical system, accounting for losses due to optical components (50% or 0.5, based on typical losses in the optical components used (e.g. lenses, filters));

[0638] η detector is the quantum efficiency of the detector (CMOS sensor) at the emission wavelength of 611 nm (50% or 0.5, based on the specifications of the detector provided by the microscope manufacturer);

[0639] is the exposure time (1 second, chosen based on the requirements of the experimental design and the need to balance signal acquisition and imaging speed); and

[0640] f collection is the fraction of emitted photons collected by the objective, which depends on the numerical aperture of the lens, NA:

[0641]

[0642] To meet the application requirements for accurate quantification of single-molecule signals, we set a high SNR threshold of 100 to provide a high confidence in distinguishing signal from noise. The minimum number of detected photons (N detected ) required to achieve a signal-to-noise ratio (SNR) of 100 is approximately 10,025 photons. This is determined using the SNR definition formula:

[0643]

[0644] where:

[0645] σ read is the read noise of the detector in units of electronic rms. (σ read = 5 rms, obtained from the technical specifications of the microscope CMOS detector).

[0646] To achieve the required N detected , the minimum fraction of collected photons (f collection ) must satisfy:

[0647]

[0648] The minimum NA is then calculated using the following formula:

[0649]

[0650] Based on this relationship, Table 1 lists different objective magnifications, their typical numerical apertures (NA), and the corresponding calculated results for the signal-to-noise ratio (SNR) for both silica beads and magnetic beads. This table helps visualize how the SNR changes with different optical system configurations for each bead type.

[0651] Table 2: Quantification of SNR between silica beads and magnetic beads in different magnification objectives

[0652]

[0653] The magnification of the eyepiece (typically 10x) does not directly affect these values.

[0654] For silica beads, calculations show that an NA as low as 0.1 is sufficient for silica beads, which can be easily achieved with standard low magnification objectives (e.g., a 4x objective with NA ~ 0.1). For magnetic beads, the required f collection is higher, resulting in a minimum NA of ~ 0.25. This indicates that detecting single-molecule signals on magnetic beads requires a higher NA or additional optimization, such as increased exposure time or enhanced detector sensitivity. The lower NA requirement for silica beads facilitates large field-of-view imaging, as low magnification objectives with wide fields of view can be used without compromising detection sensitivity. This advantage is critical for applications that require the analysis of large numbers of beads or widely distributed targets. Compared to traditional magnetic bead-based assays, the BiZi-FIA platform employing silica beads as the primary capture substrate exhibits a significant improvement in signal-to-noise ratio (SNR). This enhancement is driven by the higher optical transparency and reduced non-specific binding propensity of silica beads, which are key factors in reducing background noise. As shown in Figures 5A-5B the light transmission of silica beads was chosen to enhance, which significantly improved the detection sensitivity of QD-labeled antibodies. This is demonstrated in a comparison of fluorescence microscopy images, where silica beads exhibit a higher density of visible QD-labeled events than magnetic beads. This improvement is attributed to the minimized light attenuation and quenching that is typically associated with magnetic beads. Light intensity measurements (Fig. 4D) Figure 5A ) taken at 611 nm by a fluorospectrophotometer further confirm the advantage of silica beads, where the fluorescence intensity is almost 2-3 times higher than that observed with magnetic beads. This enhancement is particularly beneficial for single-molecule detection, where the ability to distinguish true signals from background noise is critical. Microscopy imaging also highlights the excellent signal uniformity between individual silica beads, emphasizing the ability to maintain high SNR at different magnification levels Figure 5B ). Theoretical calculations confirm Figure 5BExperimental observations in the results section confirm that the superior fluorescence intensity of silica beads comes from their superior optical properties, i.e., high transparency and minimal quenching. These properties allow efficient excitation and emission of the fluorescence signal, enabling detection of single-molecule events even with low-NA objectives and standard CMOS detectors. In contrast, magnetic beads require more stringent optical conditions due to their intrinsic attenuation and quenching effects. The enhanced detection capability provided by silica beads not only improves the signal-to-noise ratio (SNR) but also expands the flexibility of the optical system design. Their use allows low magnification, wide-field objectives, which are advantageous for large field-of-view imaging applications. In comparing the signal intensity between magnetic beads and silica beads under brightfield microscopy and fluorescence microscopy images of the same field of view (4X objective) of magnetic beads and silica beads, the enhanced fluorescence of silica beads for wide-field detection was observed. The images show that the density of detectable QD-labeled events is significantly higher on silica beads. A comparison of the light intensity between magnetic beads and silica beads at a wavelength of 611 nm shows a significant increase in signal for silica beads. Microscopic imaging of individual beads at high magnification highlights better fluorescence signal uniformity and intensity on silica beads compared to magnetic beads, and a comparison of fluorescence intensity across various magnifications (4X, 10X, 20X, 40X) shows the superior performance of silica beads in enhancing signal detection.

[0655] Example 5: Silica bead usage, fraction of analysis, and SA-biotin reaction system for optimizing sensitivity

[0656] In addition to bead usage, the application of the streptavidin (SA)-biotin system to either the capture (Cap-biotin-SA, Figure 6A ) or detection (Det-biotin-SA, Figure 6B ) component further impacts assay performance. When the SA-biotin system is applied to the capture site (Cap-biotin-SA, Figure 6A ), the detection antibody is functionalized through an amine linkage on the QD nanoball. Conversely, when the SA-biotin system is used for the detection site (Det-biotin-SA, Figure 6B ), the capture antibody is functionalized through an amine linkage on the silica bead. Figures 6A-6BScanning electron microscope (SEM) images in FIG. 6B prove a consistently functionalized surface, ensuring uniform distribution of capture sites across all beads. Specifically, when the SA-biotin system was used for capture beads, measurements yielded an average diameter of 3.03 pm, ranging from 2.97-3.08 pm. In contrast, when amine linkages were used on capture beads, measurements yielded an average diameter of 3 pm, ranging from 2.94-3.04 pm. The use of SA-biotin for capture beads increases the number of available binding sites and reduces steric hindrance when using relatively few capture microbeads, improving detection sensitivity and reducing the limit of detection (LOD) Figure 6D ). However, as the number of capture beads increases and binding sites approach saturation, further additions of beads yield diminishing returns. When bead usage exceeds loading capacity, sensitivity can decrease because some beads cannot be analyzed Figure 6D .

[0657] When the number of microbeads exceeds 200,000, leading to saturation of capture sites, the transfer of the SA-biotin system to the detection site can enhance sensitivity by 30% compared to the SA-biotin-Cap system Figure 6C . This improvement in sensitivity and lower LOD values are primarily due to optimized binding interactions and more efficient signal generation from detection antibodies. The Det-biotin-SA system ensures that each immunocomplex is efficiently labeled by a single, highly fluorescent nanosphere, maximizing signal output per binding event and improving overall assay sensitivity.

[0658] The Det-biotin-SA system achieves a limit of detection (LOD) for IgE detection of below 13 aM at a high fraction of analyzed beads (e.g., 90%) using 400,000 beads Figure 6C . This performance exceeds that of the Cap-biotin-SA system, which reaches an LOD of about 22.5 aM with the same number of beads. While Cap-biotin-SA offers advantages at lower bead usage by mitigating steric limitations on capture sites, its benefits in enhancing detection sensitivity diminish once bead saturation is reached. In contrast, Det-biotin-SA becomes more effective as bead density increases, demonstrating superior signal generation under high-density conditions and outperforming Cap-biotin-SA Figure 6D). These effects are essentially driven by the interplay between the balance between the availability of capture sites and detection sites and their inherent association and dissociation kinetics. Increasing the number of capture sites improves sensitivity up to a point. This improvement only lasts until the association and dissociation kinetics of the detection sites collectively reach a saturation state. In other words, increasing the capture sites will no longer bring an increase in sensitivity when the reaction rate of the detection sites cannot be further improved. Beyond this saturation point, adding additional beads no longer improves and can even decrease the overall analysis efficiency.

[0659] The results show that the objective magnification significantly affects the limit of detection (LOD) and standard deviation (SD) in the BiZi-FIA platform. The impact of objective magnification on detection sensitivity was evaluated after a 30-minute incubation period with a high bead concentration of 400,000 beads, which ensures minimal gap and maintains a capture efficiency of over 90%.

[0660] As the fraction of beads analyzed increased from 1% to 90% by reducing the objective magnification from 40x to 4x, the LOD significantly decreased, reaching its lowest value when approximately 90% of the beads were analyzed. In contrast, higher objective magnifications (e.g., 40x and 20x) resulted in significantly higher LODs. This increase is attributed to the smaller field of view at higher magnifications, which limits the number of beads analyzed and decreases the statistical robustness of the detection.

[0661] Additionally, the coefficient of variation (CV) significantly increased at higher objective magnifications, reaching up to 70% at a 40x magnification of the objective. This indicates greater variability and reduced reliability of the measurements using high magnification objectives. Similarly, the SD of the detection measurements increased, reflecting greater dispersion due to the limited sample size. Objectives with lower magnifications allowed a larger number of beads to be analyzed within a single field of view, thus significantly reducing the LOD, CV, and SD. By increasing the fraction of beads analyzed, the detection sensitivity is enhanced as the measurement uncertainty decreases. The data show that analyzing approximately 60% of the beads can enable reliable detection with a CV of less than 5%. This indicates that scanning a single image at 4x or 5x objective magnification over the assay area is sufficient for reliable quantification using the BiZi-FIA system. Figure 6C

[0662] A strategy application of the SA-biotin system was performed to analyze the BiZi-FIA platform sensitivity and to evaluate the array density under different conditions. A schematic of the capture antibody labeling method using the SA-biotin system is depicted in Figure 6A and SEM images of SA-biotin functionalized silica beads are illustrated in Figure 6A .A schematic of the reverse setup of the detection antibody labeling method using the SA-biotin system is depicted in Figure 6B and Figure 6BSEM images illustrating amino-functionalized silica beads. The variation of LOD with different fractions of beads analyzed shows the best performance with higher fractions and lower objective magnification (400,000 beads were used in the conditions Figure 6D The limit of detection (LOD) in attomoles (aM) was determined in relation to the number of beads used to demonstrate increased sensitivity with higher bead density. As the number of beads used increased above the capture capacity, a certain number of beads would be lost, which resulted in a decrease in the fraction of microbeads analyzed. Microscopy images showing array density with increasing bead number at 20x objective magnification showed the uniformity and scalability of bead distribution in high density arrays.

[0663] Optimized detection conditions for the Det-biotin-SA system provided stable and ultra-sensitive detection using 400,000 silica beads at 4x objective magnification. This optimization was necessary for accurate quantification of three target proteins, namely immunoglobulin E (IgE), tumor necrosis factor-alpha (TNF-alpha), and neurofilament light chain (NFL). These proteins are key biomarkers for allergic reactions, immune defense in the eye, and inflammatory responses, respectively. Specifically, IgE is a key immunoglobulin involved in allergic reactions, mediates allergic reactions primarily through binding allergens, and triggers the release of histamine and other inflammatory mediators by mast cells and basophils. TNF-alpha has anti-bacterial and anti-inflammatory properties and plays a role in immune defense in the eye, making it particularly valuable for diagnosing dry eye syndrome

[28] . NFL is used as an indicator of the degree of activity of inflammatory responses, particularly the activation and migration of neutrophils. The proteins in human form were spiked into synthetic tear solution to represent the final concentration of clinical test samples. Since the LOD is determined by extrapolating the concentration above background plus 3 SD of the background, the LOD of different runs depends on the CV of the background. After 30 minutes of incubation, the optimized detection conditions significantly improved the limit of detection (LOD) and the reliability of the measurements. Specifically, the LOD of IgE was reduced from 0.21 fM (40 fg / mL) Figures 7D-7F ) using magnetic beads to 0.013 fM (2.5 fg / mL) Figures 7A-7C ) using silica beads. Similarly, for TNF-alpha, the LOD Figure 7B ) using silica beads was reduced to 0.05 fM, significantly lower than the 0.57 fM Figure 7E ) achievable with magnetic beads in traditional digital immunoassays. For NFL, the sensitivity improvement reduced the LOD from 16.27 fM Figure 7F ) to 0.81 fM Figure 7C ).

[0664] In summary, the BiZi-FIA system overcomes the limitations of magnetic bead-based immunoassays by introducing a streamlined process for arranging silica beads into a dense, uniform array that maximizes detection efficiency. The silica bead-based BiZi-FIA platform provides a robust and high-sensitivity solution for single-molecule detection with superior optical performance and significantly enhanced signal-to-noise ratio compared to traditional magnetic bead-based immunoassay systems. This advancement holds particular promise for clinical diagnostics, where early and accurate detection of biomarkers is critical to improving patient outcomes.

[0665] A comparison of the sensitivity of silica beads based on BiZi-FIA chip washing and magnetic beads based on traditional magnetic washing was determined. BiZi-FIA and traditional digital immunoassay calibration curves were calculated for IgE, TNF-a, NFL, as shown in Figures 7A-7F Figures 7G-7I A comparison of the signal-to-background ratio between silica beads and traditional magnetic beads within the calibration curve range for both BiZi-FIA and digital immunoassay is depicted in

[0666] Example 6: Computational method for adaptive differential noise correction of BiZi-FIA for fast and accurate detection over a wide dynamic range

[0667] To improve the precision and reliability of the bidirectional zigzag flow immunoassay (BiZi-FIA) system for detecting ultra-low concentrations of proteins, an adaptive differential noise correction (ADNC) algorithm was proposed. The ADNC algorithm takes advantage of the BiZi-FIA's symmetric dual-side assay setup, where the test and control samples are introduced from opposite sides of the flow channel, ensuring similar environmental exposure for both samples. This symmetry is critical in accurately distinguishing between true signals and background noise. The implementation of ADNC involves several key steps, including data acquisition, differential analysis, noise modeling, and iterative optimization using gradient descent. The mathematical framework of the ADNC algorithm is based on the Poisson distribution, which is suitable for describing sparse events such as single-molecule protein detection signals. The fluorescence signals from the test sample (S t ) and the control sample (S c ) are modeled as independent Poisson-distributed variables. The background technical noise (S0) is a component of both the test and control signals and is also assumed to be Poisson-distributed. Importantly, the control sample (S c ) contains background noise but can also include a signal contribution from a standard of known concentration.

[0668] To improve detection precision, the algorithm calculates the gradient of the signal difference between the test and control samples using the following equation:

[0669] f'(TC) = (S t -S​C ) / (x-C)

[0670] where x and C are the concentrations of the test sample and control sample, respectively. This difference analysis accounts for non-specific adsorption and isolates the true signal. Systematic experiments are performed across various known concentrations to train the ADNC algorithm by difference analysis. Data from the double-sided assays are collected to establish the relationship between the test and control regions. The test sample highlights the experimental variation at a particular concentration, forming the basis for gradient analysis, which is typically induced by "pH": pH variation, "T": temperature fluctuation, "k off ": dissociation constant of the specific antibody, "y": interfacial energy, and "I": ion concentration variation. "N use ": number of microbeads used, "N anl ": number of microbeads analyzed, as Figures 8A-8C indicated.

[0671] During the training process, the algorithm learns to adjust the test readings based on the control response. The software has an initial setup, which involves inputting the concentration values of the quantitative sample (Q) and the control sample (C). The process begins with a double-sided assay of the test sample relative to the quantitative sample to obtain the first test signal (S t1 ) and the quantitative signal (S Q ). This is followed by a double-sided assay using the control sample (C) to obtain the second test signal (S t2 ) and the control signal (S C ). Finally, a double-sided assay with a blank sample provides the third test signal (S t3 ) and the background signal (S0). After repeating the double-sided assays, the software records the signal gradients f'(CQ), f'(C0), and f'(Q0) for a particular biomarker as follows:

[0672]

[0673] During the initial setup, the software will iterate through a threshold range to ensure

[0674] f'(CQ) > f'(C0) > f'(Q0)

[0675] This can be understood in the context of the mean value theorem for Lagrange's differential sense

[30] , which explains the potential link between the average rate of change of a function and its instantaneous rate at different points (Figure 8).

[0676] By calculating the average difference in signal intensity between consecutive points in the assay field, the algorithm can fine-tune its correction factor to address local variations caused by measurement errors. Based on the available concentrations (C, Q) and the corresponding fluorescence detection signals, we establish a function for x in terms of δ.

[0677]

[0678] Next, validation is performed using a cross-validation technique to assess the accuracy of the model. The parameters are fine-tuned by gradient descent optimization to minimize the variance between concentration estimates in repeated tests. The loss function used in the optimization process is defined as:

[0679]

[0680] where x i represents individual concentration estimates, and is their mean. The correction factor δ is updated iteratively using the following equation:

[0681]

[0682] This iterative process ensures convergence to the optimal correction factor that minimizes the difference and gradually reduces the loss, helping it find the best model parameters δ1, δ2, δ3. The process involves calculating the partial derivatives of the loss function with respect to δ and iteratively updating the value of δ based on these derivatives until δ old is the current value of the parameter from the previous iteration. is the gradient of the loss function with respect to the parameter, indicating how the loss function changes with a small change in δ. α is the learning rate that determines the size of each step taken during the update. δ new is the updated parameter value used in the next iteration. Typically, “1” is used as the initial value of δ starting from an ideal state. Using the optimized correction factor (δ1, δ2, δ3), the program calculates the values of the unknowns x1, x2, x3. Finally, the program outputs the accurate concentration ( x ) by taking the mean Figure 8B After three repeated bilateral assays, the standard curve function f(x) with the optimized signal gradient f'(x) is established for a specific biomarker, allowing subsequent samples from bilateral assays to be tested for concentration calculation quickly. The software automatically verifies the accuracy of the concentration output results, as Figure 8B shown. If it detects values that exceed the measurable concentration range, the system will output “invalid data” instead of generating erroneous data. By following these steps, the ADNC model aids in understanding and correcting errors caused by environmental variables for single-molecule protein detection, thereby improving the accuracy and reliability of the detection.

[0683] In Figures 8A-8C ​The text describes the Adaptive Differential Algorithm (ADA) interface of the ADNC software used for concentration calculation. The interface displays fluorescence images of blank, quantified, control, and test samples. The ADNC algorithm automatically processes bright spot counts and signals to calculate the concentration gradient (f'(C0), f'(C0), f'(Q0)). Standard curves are plotted for known points and fitted signals, enabling accurate concentration calculations for ultra-low protein detection. Figure 8A The difference median theorem for signal interpretation in adaptive correction is depicted. Fluorescence intensity versus concentration curves illustrate the relationship between S_0 (background noise), S_Q (quantitative sample signal), S_C (control sample signal), and S_t (test sample signal). The gradient derivatives (f'(CQ), f'(C0), f'(Q0)) derived from concentration changes are crucial for adaptive correction. Shaded areas highlight the differences between fluorescence units used for precise gradient optimization. Figure 8C Examples of fluorescence signal distributions for different sample types used to train ADNC are depicted. Red fluorescent dots represent the raw emission signals captured from the sample, while green circles represent signals that exceed a specific threshold and are subsequently recognized as valid events by the software. The blank sample (S0), quantitative sample (S_Q), control sample (S_C), and test samples (S_t1, S_t2, S_t3) show varying signal distributions. Figure 8B A flowchart outlining the key steps of the ADNC algorithm is presented, including input setup, signal switching, and gradient threshold validation. A 3D loss function surface plot (L(x)) illustrates the iterative gradient descent process used to minimize the differences in unknown test sample concentrations (x1, x2, x3), as shown below. Figure 8B As shown. This algorithm ensures optimal calibration for robust signal correction and accurate concentration prediction.

[0684] Example 7: Detection of Biomarkers: Clinical Trial of Combined Analysis of Multiple Biomarkers

[0685] To evaluate the quantitative accuracy and reproducibility of the BiZi-FIA-ADNC system, calibration curves for the biomarkers TNF-α and NFL were established using artificial tear samples doped with different concentrations of these proteins. Figure 9A , Figure 9C As shown, the Adaptive Differential Noise Correction (ADNC) software generates standardized concentration-response curves using triplicate measurements of three control samples (data points) and test samples. Triplicated measurements of TNF-α and NFL (n=3) produce consistent strong linearity, with coefficients of determination (R²) exceeding 0.997. Figure 9E , Figure 9G). While the single-run measurement of the dynamically introduced test sample (n=l) was in near perfect alignment with the calibration baseline (reference line), R2 values of 0.995 were achieved for TNF-a in the range of 0.01 fM - 1000 fM and NFL in the range of 0.1 fM - 10000 fM Figure 9F , Figure 9H ) The measured concentrations showed quantitative recoveries of 92-107% in the clinically relevant range. This demonstrated robustness to matrix effects in complex biological fluids. The effectiveness of the ADNC algorithm for IgE concentration detection was then further validated by comparing it to the traditional Poisson-based signal processing method. The ADNC algorithm outperformed the traditional Poisson-based signal processing in key aspects. First, the ADNC extended the linear dynamic range of immunoglobulin E (IgE) detection to 0.01 - 10,000 fM, which is two orders of magnitude higher than the 0.024 - 526 fM range achievable with the Poisson method Figure 91 ) This comparison highlights the advantage of the ADNC in achieving an extended detection range, particularly at low concentrations below 1 fM and high concentrations above 600 fM, where the traditional Poisson-based method starts to lose linearity. Notably, the inset emphasizes the agreement of the results between the ADNC and the Poisson-based signal processing method in the specific range of 0.024 - 526 fM. Second, the algorithm provided sufficient data for signal analysis by capturing 5.5 x 5.5 mm 2 FOV 2 The full field-of-view dark-field analysis of the FOV eliminates the dependency on bright-field imaging, in contrast to the confined imaging area required for traditional microbead counting at 10x magnification. The data highlight the clear advantage of the ADNC algorithm over the traditional Poisson-based signal processing method in terms of signal analysis workflow and efficiency. The control area reflects the degree of background noise and true signal, while the test area provides a measurement of the test signal and noise. This capability of the ADNC algorithm provides sufficient data for robust noise correction and concentration calculation, significantly improving the accuracy and efficiency of biomarker detection. This not only speeds up the signal imaging process but also provides sufficient data for signal analysis, thereby improving the overall robustness and reliability of the system, broadening the dynamic range of detection. By utilizing the ADNC algorithm, the BiZi-FIA system eliminates the dependency on limited field bright-field imaging and expands its capability for faster and wider dynamic range detection. Finally, the ADNC achieves minimal bias (<5% signal variance) between the calibration and the fast test phases, demonstrating a robust linear relationship between the fluorescent signal intensity and the biomarker concentration, highlighting the sensitivity and precision of the system in detecting ultra-low protein concentrations.

[0686] Additionally, multiplex biomarker quantification was validated using 2.2 pL tear samples from two prospectively recruited cohorts: a control group of five asymptomatic individuals (Ocular Surface Disease Index [OSDI] < 12, tear film break-up time [TFBUT] > 10 s) and a disease group comprising five patients with clinically diagnosed dry eye (OSDI > 33, TFBUT < 5 s) and moderate to severe allergic conjunctivitis (Clement II-III grade). The system resolved biomarker concentrations spanning five orders of magnitude, with IgE detected from 14 fM to 15,388 fM, TNF-a from 53 fM to 486,945 fM, and NFL from 24 fM to 1,184 fM. This performance highlights the ability to quantify biomarkers across different physiological ranges within a complex biological matrix.

[0687] The BiZi-FIA method was compared to LFIA (lateral flow immunoassay) for IgE detection, where BiZi-FIA consistently outperformed LFIA in all tested cases. As shown in the manufacturer’s (Seinda Biome Corporation) instructions, the platform showed a 1,000-fold increase in sensitivity over LFIA 1 IU / mL threshold, detecting 13 aM of IgE. Furthermore, BiZi-FIA achieved 32-fold detection within 40 minutes, while LFIA was limited to single-analyte analysis. Precision metrics further distinguished the two methods: BiZi-FIA exhibited a maximum relative error of 7.8% across the dilution series, while LFIA produced non-quantitative outputs below its detection threshold (Table 3). The results demonstrate that BiZi-FIA achieves superior sensitivity and a wider dynamic range, particularly for low-concentration samples. In contrast, the BiZi-FIA system continued to reliably detect these low concentrations of IgE, highlighting its advantage in detecting low-abundance biomarkers.

[0688] Furthermore, the ability to maintain this strong linear relationship in triplicate and single-run configurations highlights the robustness of the system, making it highly suitable for rapid and multiplexed biomarker detection in clinical and research applications. These results strongly support the potential of the system for detecting and quantifying proteins in complex biological matrices with excellent accuracy and reproducibility.

[0689] Both biomarkers exhibited excellent linearity, with the fitted curves closely aligned with the experimental data points, as shown in Figures 9A-9H These findings emphasize the BiZi-FIA platform’s ability for precise detection. The ability to consistently produce reliable measurements across multiple replicates and under different conditions demonstrates its suitability for clinical and research applications that require ultra-sensitive and precise protein quantification.

[0690] The relative error (R-error) and dilution relative error (D-error) were calculated to assess the precision of both platforms. The measurement R-error was defined as the percentage difference between the BiZi-FIA measured value and the LFIA (I-IMMUNDX TM ) measured value. The dilution R-error refers to the error introduced during the sample dilution process and was similarly calculated:

[0691] Relative error = (|measured value - expected value|) / (expected value) x 100%

[0692] In addition, the relative error increased with decreasing sample concentration under the same dilution condition. This is mainly attributed to the sampling variability and Poisson noise introduced during the dilution process, which leads to greater measurement uncertainty at lower concentrations. As the concentration decreases, the proportional effect of even small changes in the sample or measurement process becomes more significant, contributing to the increase in relative error. However, the total error was less than 8% despite a 32-fold dilution of the 2.2 μL tear sample.

[0693] Figure 9J The results shown in Table 3 reveal a wide dynamic range of IgE, with concentrations spanning several orders of magnitude, while TNF-a and NFL showed relatively stable levels in the cases. The ability of this assay to detect proteins with both highly variable and consistent baseline concentrations highlights the robustness and adaptability of the system for diverse biomarker analysis.

[0694] These findings highlight the technical advantages of BiZi-FIA, including its broad dynamic range, sensitivity, and multiplexing capability. These results are particularly beneficial for biomarker analysis in limited biological samples. However, these results are based on controlled experimental conditions and do not imply clinical conclusions. Further studies, including rigorously designed clinical trials, are needed to further assess the diagnostic potential and clinical utility.

[0695] Table 3: Raw data of IgE concentration measured by LFIA and BiZi-FIA, and their respective relative error rates. Also included are the raw data and 32-fold dilution data of IgE concentration measured by BiZi-FIA, and their relative error rates.

[0696]

[0697]

[0698] SUMMARY

[0699] In summary, the BiZi-FIA system has been developed to provide high-density, high-throughput super-bright single molecule detection.

[0700] In an exemplary method, 40 pL solution with 450,000 microbeads labeled with QDs was randomly placed into the inlet channel and transported to the array chamber, where the microbeads were height-restricted captured by the array chamber with 80% coverage and ~90% high capture efficiency of microbeads, which significantly improved accuracy and reduced imaging time. Single-molecule resolution detection of TNFa, LCN-1, COVID-19 N protein has good reliability.

[0701] Compared with traditional digital ELISA, digital IAC can achieve detection limit in attomolar range, which is about 8 times higher than commercial Simoas and Quanterix. A single-layer BiZi-FIA chip was developed for ultra-fast capture of microbead array, which can improve efficiency and reduce congestion. The process is simple and controllable.

[0702] The described BiZi-FIA chip is manufactured using standard lithography and soft lithography methods for samples as low as 0.22 uL, with single-molecule level detection sensitivity in only 120 seconds. The linearity of single-molecule detection can reach 98%. The required sample volume is 50 pL. In this experiment, standard ELISA techniques were extended to detect ultra-low specific target biomarkers using BiZi-FIA chips for digital immunoassay detection. Due to the highly independent operating mode of the networked capture units, the device can be easily scaled up and formed into more complex patterns while maintaining its effective microbead capture performance.

[0703] In summary, the BiZi-FIA system provides high-density, high-throughput super-bright single-molecule detection. 40 pL solution with 200,000 microbeads labeled with QDs was randomly placed into the inlet channel and transported to the array chamber, where the microbeads were height-restricted captured by the array chamber with 75% coverage and ~90% high capture efficiency of microbeads, which significantly improved accuracy and reduced imaging time.

[0704] Single-molecule resolution detection of TNF-a and LCN-1 has good reliability. Compared with traditional digital ELISA, BIZI-FIA can achieve detection limit in attomolar range, which is about 8 times higher than commercial Simoas and Quanterix.

[0705] Multiple biomarkers with ultra-low concentration in small volume samples can be detected within a single biological sample (e.g., human tears) with small volume by multi-layer BiZi-FIA. A bead-based and wall-based BiZi-FIA system was developed for comparison of sensitivity and reliability performance, which can effectively avoid the shielding of traditional magnetic particles to fluorescent signal.

[0706] It is to be understood that the disclosed methods and compositions are not limited to the particular methodology, protocols, and reagents described, as these can vary. It is also to be understood that the terminology used herein is for the purpose of describing particular embodiments only, and is not intended to limit the scope of the present methods which will be limited only by the appended claims.

[0707] Throughout the description and claims of this specification, the words "comprise" and the terms "including" and "having" and the like are used in their inclusive sense, and are not used in the sense of the problems. limited to only the recited items.

[0708] "Optional" or "optionally" mean that the subsequently described event or circumstance can or cannot occur, and that the description includes instances where the event, circumstance or material occurs and instances where it does not.

[0709] Ranges can be expressed herein as from "about" one particular value, and / or to "about" another particular value. When such a range is expressed, it is to be understood that another particular value, from the one particular value and / or to the other particular value, is also specifically contemplated and is expressly disclosed. Similarly, when values are expressed as approximations, by use of the antecedent "about," it will be understood that the particular value forms another specific embodiment, unless the context clearly indicates otherwise. It will further be understood that the endpoints of both the

[0710] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which the disclosed methods and compositions belong. Although any methods and materials similar or equivalent to those described herein can be used in the practice or testing of the present methods and compositions, particular methods, devices, and materials are described. Publications cited herein are explicitly incorporated by reference for all that they report, and any described methodology is most certainly used in accordance to the experimental procedures set forth herein. Nothing herein is to be construed as an admission that the inventors are not entitled to antedate any publication cited herein by virtue of prior application. No admission is made that any reference constitutes prior art. The discussion of references herein is intended merely to summarize the assertions made by their authors and no admission is made that any reference is correct, relevant, or that its referenced documents are indispensable. It will be clearly understood that, although a number of publications are referred to herein, such reference does not constitute an admission that any of these documents forms part of the common general knowledge in the art.

[0711] While descriptions of materials, compositions, components, steps, techniques, and the like can include many options and alternatives, this should not be construed as, and is not, an admission that such options and alternatives are equivalent to each other, or particularly to obvious alternatives. Thus, for example, a list of different compositions and methods of using them is not an indication that the listed compositions and methods are obvious to each other, nor is it an admission of equivalence or obviousness.

[0712] One skilled in the art will recognize or be able to ascertain using no more than routine experimentation, many equivalents to the specific embodiments of the methods and compositions described herein. Such equivalents are intended to be encompassed by the following claims.

[0713] References

[0714] 1. Aydin, S., A short history, principles, and types of ELISA, and our laboratory experience with peptide / protein analyses using ELISA. Peptides, 2015. 72: p. 4-15.

[0715] 2. Wilson, D.H., et al., The Simoa HD-1 analyzer: a novel fully automated digital immunoassay analyzer with single-molecule sensitivity and multiplexing. Journal of laboratory automation, 2016. 21(4): p. 533-547.

[0716] 3. Dong, R., N. Yi, and D. Jiang, Advances in single molecule arrays (SIMOA) for ultra-sensitive detection of biomolecules. Talanta, 2024. 270: p. 125529.

[0717] 4. Perez-Ruiz, E., et al., Digital ELISA for the quantification of attomolar concentrations of Alzheimer's disease biomarker protein Tau in biological samples. Analytica chimica acta, 2018. 1015: p. 74-81.

[0718] 5. Stephens, A.D., et al., Miniaturized microarray-format digital ELISA enabled by lithographic protein patterning. Biosensors and Bioelectronics, 2023. 237: p. 115536.

[0719] 6. O'Connell, G.C., et al., Use of high-sensitivity digital ELISA improves the diagnostic performance of circulating brain-specific proteins for detection of traumatic brain injury during triage. Neurological research, 2020. 42(4): p. 346-353.

[0720] 7. Song, Y., et al., Rapid single-molecule digital detection of protein biomarkers for continuous monitoring of systemic immune disorders. Blood, The Journal of the American Society of Hematology, 2021. 137(12): p. 1591-1602.

[0721] 8. Song, L., et al., A digital enzyme-linked immunosorbent assay for ultr asensitive measurement of amyloid-β1-42 peptide in human plasma with utility for studies of Alzheimer’s disease therapeutics. Alzheimer’s research & therapy, 2016. 8: p. 1-15.

[0722] 9. Hsu, W., et al., Bead number effect in a magnetic-beads-based digital microfluidic immunoassay. Biosensors, 2022. 12(5): p. 340.

[0723] 10. Alnaimat, F., et al., Microfluidics Based Magnetophoresis: A Review. Chem Rec, 2018. 18(11): p. 1596-1612.

[0724] 11. Cohen, L., et al., Single molecule protein detection with attomolar sensitivity using droplet digital enzyme-linked immunosorbent assay. ACS nano, 2020. 14(8): p. 9491-9501.

[0725] 12. Anna, S.L., N. Bontoux, and H.A. Stone, Formation of dispersions using "flow focusing" in microchannels. Applied Physics Letters, 2003. 82(3): p. 364-366.

[0726] 13. Kan, C.W., et al., Digital enzyme-linked immunosorbent assays with sub-attomolar detection limits based on low numbers of capture beads combined with high efficiency bead analysis. Lab on a Chip, 2020. 20(12): p. 2122-2135.

[0727] 14. Zhang, Y. and H. Noji, Digital bioassays: theory, applications, and perspectives. Analytical chemistry, 2017. 89(1): p. 92-101.

[0728] 15. Yue, X., et al., Breaking through the Poisson Distribution: A compact high-efficiency droplet microfluidic system for single-bead encapsulation and digital immunoassay detection. Biosensors and Bioelectronics, 2022. 211: p. 114384.

[0729] 16. Akama, K., K. Shirai, and S. Suzuki, Highly sensitive multiplex protein detection by droplet-free digital ELISA. Electronics and Communications in Japan, 2019. 102(2): p. 43-47.

[0730] 17. Wu, C., P. M. Garden, and D. R. Walt, Ultrasensitive detection of attomolar protein concentrations by dropcast single molecule assays. Journal of the American Chemical Society, 2020. 142(28): p. 12314-12323.

[0731] 18. Wu, C., A. M. Maley, and D. R. Walt, Single-molecule measurements in microwells for clinical applications. Critical Reviews in Clinical Laboratory Sciences, 2020. 57(4): p. 270-290.

[0732] 19. Ng, A. H., et al., Digital microfluidic magnetic separation for particle-based immunoassays. Analytical chemistry, 2012. 84(20): p. 8805-8812.

[0733] 20. Doonan, S., Microfluidic Technologies for Bioanalytical Chemistry: Advancing Epigenetic Profiling via Chromatin Immunoprecipitation in Droplets. 2019.

[0734] 21. Rissin, D. M., et al., Multiplexed single molecule immunoassays. Lab on a Chip, 2013. 13(15): p. 2902-2911.

[0735] 22. Rissin, D. M., et al., Single-molecule enzyme-linked immunosorbent assay detects serum proteins at subfemtomolar concentrations. Nature biotechnology, 2010. 28(6): p. 595-599.

[0736] 23. Mohammed, H. A., The effect of different inlet geometries on laminar flow combined convection heat transfer inside a horizontal circular pipe. Applied Thermal Engineering, 2009. 29(2-3): p. 581-590.

[0737] 24. Yang, S., et al., A fractal analysis of laminar flow resistance in roughened microchannels. International Journal of Heat and Mass Transfer, 2014. 77: p. 208-217.

[0738] 25. Rumi, M. and J. W. Perry, Two-photon absorption: an overview of measurements and principles. Advances in Optics and Photonics, 2010. 2(4): p. 451-518.

[0739] 26. Aleem, S. H. E. A., A. F. Zobaa, and M. M. A. Aziz, Optimal C-type passive filter based on minimization of the voltage harmonic distortion for nonlinear loads. IEEE Transactions on Industrial Electronics, 2011. 59(1): p. 281-289.

[0740] 27. Komiyama, S., Single-photon detectors in the terahertz range. IEEE Journal of selected topics in quantum electronics, 2010. 17(1): p. 54-66.

[0741] 28. LECHNER, M., P. WOJNAR, and B. REDL, Human tear lipocalin acts as an oxidative-stress-induced scavenger of potentially harmful lipid peroxidation products in a cell culture system. Biochemical Journal, 2001. 356(1): p. 129-135.

[0742] 29. Lin, C.-W., et al., Elevated a-synuclein and NfL levels in tear fluids and decreased retinal microvascular densities in patients with Parkinson’s disease. Geroscience, 2022. 44(3): p. 1551-1562.

[0743] 30. Agrawal, O.P., Formulation of Euler-Lagrange equations for fractional variational problems. Journal of Mathematical Analysis and Applications, 2002. 272(1): p. 368-379.

Claims

1. A microfluidic chip comprising: a microfluidic platform comprising one or more microfluidic flow paths, wherein at least one of the microfluidic flow paths comprises an inlet channel, a flow-trap junction (FTJ) structure, and an outlet channel, wherein the at least one microfluidic flow path is configured for moving fluid from the inlet channel into the FTJ structure and from the FTJ structure into the outlet channel, wherein the inlet channel is wider at a location where the fluid moves from the inlet channel into the FTJ structure than at a location where the fluid is introduced into the inlet channel, wherein the inlet channel comprises a plurality of hydrophobic micro-pillar structures, wherein the FTJ structure comprises a flow layer and a trapping layer, wherein the flow layer is in contact with, on top of, and overlapping the trapping layer, wherein the flow layer comprises a plurality of flow microfluidic channels, each flow microfluidic channel comprising a top, a sidewall, and an opening on a bottom, wherein surfaces of the flow microfluidic channels are hydrophobic, wherein the flow microfluidic channels allow micron-scale particles and nanoscale objects to pass freely, wherein the fluid flows in the same direction in all of the flow microfluidic channels, wherein the flow microfluidic channels are parallel to each other, wherein the trapping layer comprises a plurality of trapping microfluidic channels, each trapping microfluidic channel comprising a bottom, a sidewall, and an opening on a top, wherein the trapping microfluidic channels are parallel to each other, wherein surfaces of the trapping microfluidic channels are hydrophilic, wherein the fluid flows in the same direction in all of the trapping microfluidic channels, wherein the flow microfluidic channels are not parallel to the trapping microfluidic channels, wherein the flow microfluidic channels and the trapping microfluidic channels allow fluid to move from the flow microfluidic channels into the trapping microfluidic channels via the openings on the bottoms and the openings on the tops, respectively, wherein the trapping microfluidic channels, the transition from the flow microfluidic channels to the trapping microfluidic channels, or a combination of the trapping microfluidic channels and the transition from the flow microfluidic channels to the trapping microfluidic channels are configured to allow the nanoscale objects to pass through the trapping microfluidic channels, whereby the passed nanoscale objects flow into the outlet channel, wherein the trapping microfluidic channels, the transition from the flow microfluidic channels to the trapping microfluidic channels, or a combination of the trapping microfluidic channels and the transition from the flow microfluidic channels to the trapping microfluidic channels are configured to trap the micron-scale particles in the trapping microfluidic channels, whereby the trapped micron-scale particles combine to form an array within the FTJ structure.

2. The chip of claim 1, wherein, each flow microfluidic channel comprises a terminal region proximate to the outlet channel, wherein the terminal region comprises a bend in the flow microfluidic channel, wherein the bend directs fluid flow out of the microfluidic channel in a direction opposite to an oriented fluid flow within the outlet channel; and wherein fluid flow in opposite directions provides resistance in the flow microfluidic channel and drives fluid lateral motion through the FTJ.

3. The chip of claim 1, wherein, The side walls of the capture microfluidic channel have uniform widths and are parallel to each other, wherein the height of the capture microfluidic channel is less than the diameter of the micron-scale particles.

4. The chip of claim 3, wherein, The height of the capture microfluidic channel is greater than the diameter or long dimension of the nanoscale objects.

5. The chip of claim 1, wherein, The side walls of the capture microfluidic channel do not have uniform widths, such that the width of the capture microfluidic channel varies in a regular pattern along its length, wherein the pattern of width variation forms a constriction in the width of the capture microfluidic channel, wherein the width of the constriction is less than the diameter of the micron-scale particles, wherein the width of the constriction is greater than the diameter or long dimension of the nanoscale objects.

6. The chip of claim 5, wherein, All or a subset of the constrictions overlap the flow layer between some or each of the openings on the bottom of an adjacent flow microfluidic channel.

7. The chip of claim 5, wherein, All or a subset of the constrictions overlap the openings on the bottom of some or each of the flow microfluidic channels.

8. The chip of claim 5, wherein, A subset of the constrictions overlap the openings on the bottom of some or each of the flow microfluidic channels and a subset of the constrictions overlap the flow layer between some or each of the openings on the bottom of an adjacent flow microfluidic channel.

9. The chip of claim 5, wherein, A subset of the constrictions overlap the openings on the bottom of each of the flow microfluidic channels and a subset of the constrictions overlap the flow layer between each of the openings on the bottom of an adjacent flow microfluidic channel.

10. The chip of claim 7, wherein, The constrictions overlapping the openings on the bottom of the flow microfluidic channels form small capture inlets on the down-flow side of the openings and large capture inlets on the down-flow side of the openings for alternating capture microfluidic channels, wherein the small capture inlets are smaller in size than the diameter of the micron-scale particles, wherein the small capture inlets are larger in size than the diameter or long dimension of the nanoscale objects, and wherein the large capture inlets are larger in size than the diameter of the micron-scale particles.

11. The chip of claim 1, wherein, The flow microfluidic channels and the capture microfluidic channels are at a right angle to each other.

12. The chip of claim 1, wherein, The flow microfluidic channels and the capture microfluidic channels are at an oblique angle to each other.

13. The chip of claim 1, wherein, The flow microfluidic channels and the capture microfluidic channels are at an angle of 60° to 90°, 70° to 90°, 80° to 90°, 85° to 90°, 87° to 90°, 88° to 90°, or 89° to 90° to each other.

14. The chip of claim 1, wherein, The side walls of the capture microfluidic channel are angled toward the up-flow end of the flow microfluidic channel.

15. The chip of claim 1, wherein, The microfluidic flow path further comprises a sample inlet, wherein the microfluidic flow path is configured for moving fluid from the sample inlet into the inlet conduit.

16. The chip of claim 1, wherein, The microfluidic flow path further comprises a plurality of outlet channels, wherein the microfluidic flow path is configured for movement of fluid from the capture microfluidic channels into the outlet channels and from the outlet channels into the outlet conduit.

17. The chip of claim 16, wherein, Each capture microfluidic channel is flowably connected to a different one of the outlet channels.

18. The chip of claim 1, wherein, The flow layer of the FTJ structure further comprises a plurality of outlet channels interspersed among and parallel to the flow microfluidic channels, wherein each of the outlet channels comprises a top, sidewalls, and an opening on a bottom, wherein the outlet channels and the capture microfluidic channels allow movement of fluid from the capture microfluidic channels into the outlet channels via the openings on the bottoms and the tops, respectively, wherein the microfluidic flow path is configured for movement of fluid from the capture microfluidic channels into the outlet channels and from the outlet channels into the outlet conduit.

19. The chip of claim 18, wherein, The outlet channels alternate with the flow microfluidic channels in the flow layer of the FTJ structure.

20. The chip of claim 1, wherein, The flow layer of the FTJ structure further comprises an outlet channel, wherein the outlet channel comprises a top, sidewalls, and an opening on a bottom, wherein the outlet channel overlaps with a downward flow end of the capture microfluidic channel, wherein the outlet channel and the capture microfluidic channel allow movement of fluid from the capture microfluidic channel into the outlet channel via the opening on the bottom and the top, respectively, wherein the microfluidic flow path is configured for movement of fluid from the capture microfluidic channels into the outlet channels and from the outlet channels into the outlet conduit.

21. A method for detecting a target biomarker in a fluid sample, the method comprising: (a) introducing the fluid sample into one or more of the microfluidic flow paths of the microfluidic chip of claim 1, wherein the fluid sample comprises a plurality of micron-scale particles and a plurality of nanoscale objects, or is contacted with a plurality of micron-scale particles and a plurality of nanoscale objects after its introduction, and (b) performing a digital chromatography on the chip, wherein the digital chromatography identifies the presence of and / or the amount of the target biomarker in the fluid sample.

22. The method of claim 21, wherein, Step (a) further comprises introducing a control sample comprising a known amount of the target biomarker into a different microfluidic flow path of the same chip.

23. The method of claim 22, wherein, The performance of the digital chromatography of step (b) comprises actuating movement of fluid through the microfluidic flow paths in the microfluidic chip, wherein the movement filters and washes the micron-scale particles within the FTJ structure.

24. The method of claim 23, wherein, The filtering of the micron-scale particles in the FTJ structure traps the micron-scale particles within the FTJ structure and forms an array of the micron-scale particles.

25. The method of claim 24, wherein, Step (b) further comprises imaging the array of micron-scale particles within the microfluidic chip.

26. The method of claim 21, wherein, The micron-scale particles comprise microbeads.

27. The method of claim 26, wherein, The microbeads comprise magnetic microbeads.

28. The method of claim 21, wherein, The microparticulate further comprises a first capture agent specific for a target biomarker.

29. The method of claim 28, wherein, Step (b) further comprises detecting and measuring the target biomarker bound to the first capture agent on the microparticulate within the array.

30. The method of claim 28, further comprising, prior to step (a), (i) incubating the fluid sample with the microparticulate for a period of time and in an amount effective to bind the target biomarker to the first capture agent; and (ii) optionally washing the microparticulate.

31. The method of claim 30, further comprising, prior to step (a), contacting the microparticulate with the nanoscale object for a period of time and in an amount effective to bind the target biomarker to the second capture agent.

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