Multi-channel microfluidic biochemical detection system based on graphene composite membrane

Abstract

The invention relates to the technical field of micro-fluidic chips and biochemical sensing, and discloses a multi-channel micro-fluidic biochemical detection system based on a graphene composite membrane, the system comprises: at least one micro-fluidic chip unit having a plurality of independent micro-channels and functionalized graphene composite membrane sensors integrated in each channel detection area; the graphene composite membrane is of a three-dimensional porous structure and can be subjected to differential modification to realize multi-index parallel detection; the chip unit adopts a modular design, so that the number of channels is convenient to expand; the system further comprises a module used for collecting and processing multiple paths of signals and a control module integrated with an artificial intelligence algorithm, the flow velocity and detection parameters can be dynamically adjusted according to real-time data, and result verification can be carried out by fusing optical detection. The method realizes high-throughput, high-sensitivity, high-accuracy and high-flexibility multi-index biochemical detection, and is suitable for the fields of clinical diagnosis, environmental monitoring and the like.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of microfluidic chips and biochemical sensing technology, and more particularly to a multi-channel microfluidic biochemical detection system based on graphene composite film. BACKGROUND

[0002] Microfluidic technology has been widely used in biochemical analysis, disease diagnosis and environmental monitoring due to its advantages of low sample consumption, fast analysis speed and easy integration. Multi-channel microfluidic systems can process multiple samples or detect multiple indicators simultaneously, significantly improving the detection throughput. However, existing technologies still face the following challenges: The contradiction between detection sensitivity and throughput: increasing the number of channels often leads to a decrease in the size of individual detection units, limiting the area and performance of the sensing interface, thereby affecting the sensitivity.

[0003] Cross-channel interference: when multiple channels are detected in parallel, the cross-talk of fluids, signals or heat can affect the accuracy of the detection results.

[0004] Insufficient system integration and flexibility: most multi-channel systems have fixed functions and are difficult to reconfigure or expand according to different detection requirements.

[0005] Sensing interface stability and reusability: many biological sensor devices are difficult to integrate stably in microfluidic channels and are usually disposable, which is costly.

[0006] Graphene and its composites are considered ideal sensing materials due to their excellent electrical, mechanical and chemical properties. However, their integration in microfluidic systems is mostly limited to single-channel or simple structures, failing to fully exploit their potential in high-throughput, multi-parameter detection.

[0007] Therefore, the present application proposes a multi-channel microfluidic biochemical detection system based on graphene composite film to solve the above problems. SUMMARY

[0008] In order to overcome the above-mentioned defects of the prior art, the present application provides a multi-channel microfluidic biochemical detection system based on graphene composite film to solve the problems existing in the background art.

[0009] The present application provides the following technical solution: a multi-channel microfluidic biochemical detection system based on graphene composite film, comprising: At least one microfluidic chip unit, which contains: a substrate layer; a multi-channel microfluidic network formed on the substrate layer, the network containing at least three independent microfluidic channels; a sample introduction interface connected to the inlet of each microfluidic channel; a waste liquid collection interface connected to each microfluidic channel outlet; a graphene composite film sensor integrated in each microfluidic channel specific detection area, which is composed of a support layer and a functionalized graphene composite film; a signal acquisition and processing module for real-time monitoring and processing of electrochemical or electrical impedance signals output by each channel sensor; a system control module for coordinating the timing of sample injection, fluid control and data acquisition.

[0010] As a further scheme of the present application: the microfluidic chip unit adopts a modular and detachable design, and each unit is connected in parallel or series through a standardized interface, realizing the expansion of the number of detection channels.

[0011] As a further scheme of the present application: the graphene composite film is a three-dimensional porous structure composed of graphene oxide and functional nanomaterials, including but not limited to at least one of metal nanoparticles, quantum dots, magnetic nanoparticles or molecularly imprinted polymers.

[0012] As a further scheme of the present application: the thickness of the graphene composite film is 10-500 nanometers, the porosity is 30%-80%, and the graphene composite films of different channels are differentially functionalized to make them respectively specifically respond to different types of target analytes.

[0013] As a further scheme of the present application: each microfluidic channel is provided with: a micro-mixing structure for promoting the mixing of samples and reagents; a temperature control area for maintaining or adjusting the reaction temperature; at least one bypass sampling port for extracting a small amount of sample for auxiliary analysis during the detection process.

[0014] As a further scheme of the present application: the signal acquisition and processing module adopts multiplexing technology to simultaneously or time-divisionally acquire signals of each channel, and is internally provided with a self-calibration algorithm, which can automatically compensate for baseline drift caused by film performance decay or environmental interference.

[0015] As a further scheme of the present application: it further comprises: an optical detection module comprising a movable confocal optical scanning head, which can perform fluorescence or Raman spectrum scanning on the surface of the graphene composite film of each channel; The surface of the graphene composite film is modified with fluorescently labeled probes or Raman-enhanced substrates corresponding to the target analyte.

[0016] As a further scheme of the present application: the system control module integrates an artificial intelligence algorithm, which can dynamically adjust: the sample flow rate of each channel; detecting parameters; and automatically selecting a preset subsequent analysis procedure according to the preliminary result.

[0017] A preparation method of a microfluidic chip unit for the system of any of the above, characterized in that it comprises the steps of: forming a microfluid channel network mold on the substrate layer by photolithography or laser etching; obtaining the microfluid channel layer by PDMS casting replication; generating a graphene composite film in situ at the microfluid channel detection area by vacuum-assisted self-assembly technology; directly transporting the functional reagent to the film surface for modification by microfluidic method; irreversible sealing of the microfluid channel layer and the substrate containing the electrode.

[0018] A method for multi-index parallel biochemical detection using the system of any of the above, characterized in that it comprises the steps of: injecting different samples to be tested or the same sample into different channels respectively; controlling the sample to flow through the functionalized graphene composite film sensor; the target analyte binds with the recognition element on the film, causing changes in the electrical or optical properties of the film; the signal acquisition module records the response signals of each channel; the processing module converts the signals into concentration information and outputs a multi-index detection report.

[0019] Technical effects and advantages of the present application: The present application integrates a functionalized graphene composite film with a three-dimensional porous structure in each independent microfluid channel detection area, providing a large effective specific surface area and abundant active sites in a limited microscale space, significantly enhancing the capture ability and signal response strength of the sensor for target objects, thereby ensuring high-throughput parallel detection while achieving detection sensitivity comparable to or even better than that of single-channel dedicated equipment (up to pg / mL level), solving the problem of decreased sensitivity caused by miniaturization of the unit in traditional multi-channel systems.

[0020] The modular chip unit and standardized interface design used in the present application allow users to flexibly parallel or series connect multiple basic units like building blocks according to the actual detection throughput requirements, realizing on-demand expansion from several channels to tens or even hundreds of channels. This design not only meets the high-throughput requirements of large-scale screening, but also is suitable for daily small-scale detection, avoiding the waste of idle high-performance dedicated equipment. At the same time, the damage of a single module does not require replacing the entire system, significantly reducing maintenance costs.

[0021] The present application fundamentally avoids fluid cross-contamination by being physically independent of each other and setting up isolation channels. In the signal level, time-division multiplexing acquisition and active shielding technology are adopted, and software algorithm is combined to automatically compensate the baseline drift, effectively suppressing the electrical signal crosstalk and environmental interference. The comprehensive design of "physical isolation + signal anti-interference" ensures the independence and accuracy of the detection results of each channel.

[0022] The integrated artificial intelligence algorithm of the present application can perform online analysis on the real-time collected signals and dynamically feedback control the detection process. For example, the flow rate is automatically adjusted according to the signal response speed to optimize the reaction time, or different preset subsequent analysis programs (such as dilution retesting or optical confirmation) are triggered according to the preliminary results. This enables the system to have preliminary autonomous decision-making and optimization capabilities, reduces human intervention, and improves detection efficiency and result reliability.

[0023] In the present application, the system not only relies on the excellent electrochemical properties of the graphene composite film for high-sensitivity detection, but also can use the fluorescence or Raman enhancement effect of the same sensing interface for optical verification through the optional optical scanning module. The potential integration of electrical and optical detection modes provides mutually corroborative data, especially suitable for complex samples or scenarios with extremely high result confirmation requirements, reducing the risk of false positives / negatives.

[0024] The vacuum-assisted self-assembly and in-situ film formation technology used in the present application allows direct preparation of high-performance graphene composite film sensors in the already formed microfluidic channels, avoiding complex transfer and bonding processes and reducing integration difficulty and failure rate. The prepared three-dimensional porous structure has good stability and firm functional modification, providing a guarantee for the long-term stable operation of the sensor in the microfluidic environment. BRIEF DESCRIPTION OF DRAWINGS

[0025] The present application will be further described below with reference to the accompanying drawings.

[0026] Figure 1 is a system block diagram of a multi-channel microfluidic biochemical detection system based on a graphene composite film according to the present application; Figure 2 is a preparation method flowchart of a microfluidic chip unit according to the present application; Figure 3 is a method flowchart of a multi-index parallel biochemical detection method according to the present application. DETAILED DESCRIPTION

[0027] The technical solutions of the present application will be described in detail below with reference to the accompanying drawings and specific embodiments. The embodiments are only used to explain the present application and do not limit the protection scope of the present application.

[0028] Please refer to Figure 1As shown, a multi-channel microfluidic biochemical detection system based on graphene composite film includes: At least one microfluidic chip unit, which contains: A substrate layer; A multi-channel microfluidic network formed on the substrate layer, which contains at least three independent microfluidic channels; A sample introduction interface connected to the inlet of each microfluidic channel; A waste liquid collection interface connected to the outlet of each microfluidic channel; A graphene composite film sensor integrated in the specific detection area of each microfluidic channel, which is composed of a support layer and a functionalized graphene composite film; A signal acquisition and processing module for real-time monitoring and processing of electrochemical or electrical impedance signals output by each channel sensor; A system control module for coordinating the timing of sample injection, fluid control and data acquisition.

[0029] In one embodiment: Microfluidic chip unit preparation: Select a polymethyl methacrylate (PMMA) plate with a thickness of 1 mm as the substrate layer. Using computer numerical control (CNC) precision milling technology, three parallel microfluidic channels with a width of 200 μm, a depth of 100 μm and a length of 30 mm are machined on the PMMA plate. The standard luer connector is embedded in the microfluidic channel inlet and outlet as the interface by hot pressing method.

[0030] Graphene composite film sensor integration: Using screen printing technology, a carbon paste interdigital electrode is printed in the middle region of each microfluidic channel as a support layer. Mix graphene oxide (GO) aqueous dispersion (concentration 2 mg / mL) with chloroauric acid (HAuCl4) solution, and generate gold nanoparticle-reduced graphene oxide (Au-rGO) composite film by in-situ reduction and compounding on the surface of the interdigital electrode through electrochemical deposition method (voltage -1.2 V, time 300 s), which is the functionalized graphene composite film and directly constitutes the sensor sensitive unit.

[0031] Signal acquisition and processing module building: A commercially available multi-channel electrochemical workstation (e.g., CHI1000C series) is used as the core. The working electrode lead, counter electrode lead and reference electrode lead of the workstation are connected to the corresponding interdigital electrode, common counter electrode and Ag / AgCl reference electrode of each channel of the chip, respectively. The built-in software of the workstation is set to chronoamperometry or electrochemical impedance spectroscopy (EIS) for signal monitoring and recording.

[0032] System control module construction: a personal computer (PC) is used to run a control program written in LabVIEW. The program controls a multi-channel syringe pump (e.g., Harvard Apparatus PHD Ultra) through serial communication to inject samples from the inlet interface into each microfluidic channel at a set flow rate (e.g., 10 μL / min) and time sequence. At the same time, the program triggers and synchronizes the data acquisition process by calling the application programming interface (API) of the electrochemical workstation.

[0033] As a further aspect of the application: the microfluidic chip unit adopts a modular and detachable design, and each unit is connected in parallel or series through a standardized interface to achieve the expansion of the number of detection channels.

[0034] In one specific embodiment: The specific way to realize the modular and detachable design is as follows: Standardized interface design: each chip unit is designed as a rectangle (e.g., 25 mm x 75 mm) with consistent dimensions. Mechanical docking grooves and positioning pins are designed on the two side edges of the unit. On the short edge end face of the unit, fluid connection ports (inlet and outlet) made of elastic silicone material are integrated, with an inner diameter matching the microfluidic channel. At the same time, a row of metal spring needle electrical connectors is arranged at the bottom of the unit for signal output of the interdigital electrode.

[0035] Modular connection implementation: when expansion is needed, two or more chip units are placed side by side on a general-purpose bearing base. By inserting the positioning pins of the first unit into the docking grooves of the second unit, physical alignment and fixation are achieved. At this time, the fluid outlet port of the first unit is tightly fitted with the fluid inlet port of the second unit under pressure, forming a sealed fluid channel in series. When the spring needle electrical connectors at the bottom of all units are inserted into the bearing base, they are in conduction with the corresponding printed circuit board (PCB) contacts inside the base, thereby connecting the electrical signals of all units to a unified interface. The bearing base itself provides quick plug-in interfaces with external syringe pumps and electrochemical workstations.

[0036] Expansion example: when six-channel detection is needed, one skilled in the art can choose to use a single chip unit with six built-in channels, or connect two three-channel modular units in parallel on the bearing base through the above-mentioned method (the inlets are connected to two syringe pump channels respectively, and the outlets are connected in series), which also achieves six independent channel detection.

[0037] As a further aspect of the application: the graphene composite film is a three-dimensional porous structure composed of graphene oxide and functional nanomaterials, including but not limited to at least one of metal nanoparticles, quantum dots, magnetic nanoparticles, or molecularly imprinted polymers.

[0038] In one specific embodiment, the specific preparation steps for achieving the three-dimensional porous graphene composite membrane are as follows: Preparation of composite dispersion: to prepare gold nanoparticles ( Taking the composite membrane as an example, firstly, a graphene oxide (GO) aqueous dispersion (1 mg / mL) was prepared using the modified Hummers method. Then, 10 mL of the above GO dispersion was mixed with 1 mL of 10 mM chloroauric acid (CHA). The solutions were mixed and magnetically stirred for 30 minutes. Then, 2 mL of 0.1 M ascorbic acid solution was slowly added as a reducing agent while stirring, and stirring was continued for 1 hour to obtain a homogeneous solution. Precursor composite dispersion. The reduction process can be carried out at room temperature under mild and controllable conditions.

[0039] Three-dimensional porous structure formation: The above composite dispersion is injected into the detection area of ​​the microchannel (above the interdigitated electrodes). Film formation using freeze-drying technology: The entire chip is placed in an ultra-low temperature freezer at -80°C for 4 hours to completely solidify the dispersion; then it is transferred to a freeze dryer and dried for 24 hours under a vacuum of less than 10 Pa. After the ice crystals sublimate, a rich three-dimensional porous structure is left behind. Composite foam membrane. Porosity can be controlled by adjusting conventional parameters such as dispersion concentration and freezing rate.

[0040] Other functional nanomaterial composites: Other nanomaterials can be replaced or added depending on the detection target. For example, if composites are to be used... To enhance optical properties, quantum dots can be simply mixed with a carboxylated quantum dot aqueous solution and a GO dispersion via ultrasonication, followed by film formation using the same freeze-drying method. To introduce magnetism, pre-synthesized Fe3O4 nanoparticles can be mixed with a GO dispersion. The mixing of these nanomaterials with GO involves either simple physical blending or assembly based on electrostatic interactions.

[0041] As a further aspect of the present invention: the thickness of the graphene composite membrane is 10-500 nanometers, the porosity is 30%-80%, and the graphene composite membranes with different channels are modified with differentiated functions to make them specifically respond to different types of target analytes.

[0042] The specific methods for achieving membrane parameter control and differentiated functional modification are as follows: Thickness control: The thickness of the membrane is mainly achieved by controlling the concentration and volume of the deposition or film-forming precursor solution. For example, in the freeze-drying method, a micro-injection pump is used to deliver 5 μL, 10 μL, or 20 μL of the precursor solution. A composite dispersion (concentration 1 mg / mL) was precisely injected into detection cells with different microchannels. After freeze-drying, the resulting film thickness was approximately proportional to the injected volume and could be precisely controlled within the range of 50-200 nm. This is a conventional technique for controlling coating thickness by controlling the amount of material.

[0043] Porosity control: Porosity is mainly achieved by adjusting the freezing rate. The chip is placed in a programmed cooling system and cooled to -80°C at different rates (such as 1°C / min or 10°C / min). Slower freezing rates produce larger ice crystals, resulting in a film with larger pore size and higher porosity (up to 70% or more) after drying; rapid freezing produces a denser film with lower porosity (about 40%).

[0044] Differential functional modification: Taking the modification of different antibodies as an example. A multi-channel gate valve is connected to the common inlet of the chip. First, an antibody solution targeting alpha-fetoprotein (AFP) (10 μg / mL in PBS) is selected through the gate valve and pumped into channel one only. Incubation at room temperature for 2 hours allows the antibody to be immobilized on the graphene composite membrane of channel one through physical adsorption or amino coupling. After rinsing with PBS buffer, the gate valve is switched, and an antibody solution targeting carcinoembryonic antigen (CEA) is pumped into channel one, and the same operation is performed. This process is repeated to complete the specific modification of all channels.

[0045] As a further aspect of the present invention: each microchannel is provided with: Micromixing structures are used to facilitate the mixing of samples and reagents; Temperature control zone, used to maintain or regulate reaction temperature; At least one bypass sampling port is provided for extracting trace samples for auxiliary analysis during the detection process.

[0046] The specific implementation methods for achieving micro-mixing, temperature control, and sampling are as follows: Micromixing structure: In a microchannel, a zigzag or chaotic mixing structure is designed upstream of the detection region. For example, a continuous zigzag channel with a width of 100 μm and a period of 500 μm is formed on a mold using photolithography. When fluid flows through it, secondary flow and chaotic convection are generated due to the periodic change in the channel direction, thereby achieving thorough mixing of the sample and injected reagent within a short distance (~5 mm). The design parameters (angle, period) of this mixing structure can be optimized by referring to published microfluidic mixer literature.

[0047] Temperature control region: A thin-film micro Pt100 temperature sensor and a thin-film micro heater (made of sputtered Pt film) are bonded to the bottom of the chip substrate (e.g. glass) at the position corresponding to the microfluidic detection region. The temperature sensor and heater are fabricated by photolithography and lift-off, which are mature techniques in the field. They are connected to an external proportional-integral-derivative (PID) temperature controller (e.g. Thorlabs TC200) via wires. The PID controller dynamically adjusts the current of the heater according to the temperature signal fed back by the sensor, so as to precisely control the temperature of the detection region at a set value (e.g. 37°C ± 0.2°C).

[0048] Bypass sampling port: A vertical hole with a diameter of about 50 μm is opened in the sidewall of the microfluidic channel, which is formed together when the mold is processed. A micro silica gel tube with a matching inner diameter is bonded above the hole as a sampling tube, and the interface is sealed with epoxy resin. When sampling is needed, a finer capillary needle (outer diameter 40 μm) is inserted from the other end of the silica gel tube, and about 10-100 nL of fluid sample can be extracted by capillary action or by applying a small negative pressure, for subsequent offline mass spectrometry or other analysis.

[0049] As a further aspect of the application: the signal acquisition and processing module uses multiplexing technology to simultaneously or time-divisionally acquire signals of each channel, and has a built-in self-calibration algorithm that can automatically compensate for baseline drift caused by membrane performance decay or environmental interference.

[0050] The above-mentioned specific implementation manner is as follows: Multiplexing hardware circuit: a multiplexing analog switch circuit board (e.g. using ADI's ADG732 32-channel analog multiplexer chip) is designed. The working electrodes (WE) of all channel graphene sensors are connected to the input terminals of the multiplexer, and the common output terminal of the multiplexer is connected to the input terminal of a high-precision transimpedance amplifier (for current measurement) or a lock-in amplifier (for impedance measurement). The microcontroller (e.g. STM32 series) controls the channel switching sequence and rate of the multiplexer through a serial peripheral interface (SPI). For example, at a speed of 10 channels per second, the current signals of each channel are acquired time-divisionally.

[0051] Software implementation of self-calibration algorithm: the following algorithm flow is embedded in the software of the signal processing module (e.g. Python program running on PC): a. Baseline learning phase: after the system is powered on for the first time or after each chip replacement, the channel signals are continuously acquired for 60 seconds under the condition of pure buffer (e.g. PBS) being passed, and the average value μ and the standard deviation σ are calculated as the initial baseline B0.

[0052] b. Real-time monitoring and drift detection: During the subsequent detection process, collect the "background" signal Bn without sample injection every interval (e.g. 30 seconds). Calculate the difference between the current background signal and the initial baseline .

[0053] c. Drift compensation: If ΔB exceeds the preset threshold (e.g. 3σ), it is determined that significant baseline drift has occurred. The algorithm subtracts the current from all subsequent detection sample raw signal values Sn in real time to obtain the compensated signal which is used for concentration calculation again.

[0054] d. Threshold update: According to the trend of the change of over time, the drift compensation model (e.g. linear fitting) can be dynamically updated.

[0055] As a further scheme of the present application, it further comprises: An optical detection module comprising a movable confocal optical scanning head capable of performing fluorescence or Raman spectrum scanning on the surface of the graphene composite film of each channel; The surface of the graphene composite film is modified with fluorescently labeled probes corresponding to the target analyte or a Raman enhancement substrate.

[0056] The specific way to realize optical detection fusion is as follows: Movable optical scanning head: A set of commercial microscope automatic stage (such as Prior Scientific ProScan III) is used to carry the microfluidic chip. A miniature confocal optical probe (e.g. containing an excitation fiber, a collection fiber and a miniature lens group) is installed on a three-dimensional precision motorized displacement stage above the stage. The displacement stage is driven by a stepper motor, and the control software can accurately move and position the optical probe above any channel according to the coordinates (previously calibrated) of the detection area of each channel on the chip. The excitation light (such as 488nm laser) is introduced through the optical fiber and focused on the surface of the graphene film, and the generated fluorescence or Raman scattering light is guided out to the spectrometer (such as Ocean Optics QE Pro) by the collection fiber.

[0057] Optical functionalization of graphene film: For fluorescence detection: After the graphene composite film is modified with specific recognition molecules (such as antibodies), the detection adopts the "sandwich method". The target in the sample is captured by the antibody on the film, and then the secondary antibody labeled with a fluorescent group (such as Cy5) is added. Graphene is an excellent fluorescence quencher, but when the target exists to form a "sandwich" structure, the fluorescence group is separated from the film by the protein layer, and the quenching is weakened, thereby generating a detectable fluorescence signal.

[0058] For Raman-enhanced detection: Gold nanoparticles of high density and uniformity are incorporated into the graphene composite film as "hot spots" for surface-enhanced Raman scattering (SERS). Raman reporter molecules that can specifically bind to the target molecules (such as 4-mercaptobenzoic acid) are immobilized on the film. When the target molecules are present, their binding changes the local chemical environment of the reporter molecules, causing a shift in their characteristic Raman peaks or a change in intensity, which can be detected by scanning the SERS spectrum.

[0059] As a further aspect of the application: the system control module integrates artificial intelligence algorithms, which can dynamically adjust: the sample flow rate of each channel; the detection parameters; and automatically select and execute pre-set follow-up analysis procedures based on preliminary results.

[0060] The specific algorithm and workflow for intelligent control are as follows: AI algorithm integration: On the system control PC, a lightweight machine learning model (such as based on decision trees or simple neural networks) is deployed using open-source libraries such as Python's Scikit-learn or TensorFlow Lite. The model is trained using historical data during the development phase, learning the mapping relationship between different signal patterns (such as the slope of the response curve, the time to reach the plateau value) and the optimal detection parameters (such as the best flow rate, the best reaction time).

[0061] Dynamic adjustment examples: Flow rate adjustment: The system starts running at the standard flow rate v0. The AI model analyzes the initial slope of the signal rise in each channel in real time. If the slope of a certain channel is much higher than the average, the model judges that the target concentration of that channel may be very high, and to avoid the sensor from being saturated too quickly, it sends instructions to the injection pump through the control program to automatically reduce the flow rate of that channel to to obtain more accurate plateau signals for quantification.

[0062] Parameter adjustment: For electrochemical impedance spectroscopy detection, the AI model can automatically adjust the frequency range or excitation voltage amplitude of subsequent scans based on the quality of the initial scan spectrum to obtain the best signal-to-noise ratio spectrum.

[0063] Automatic selection of subsequent procedures: The system is preset with multiple analysis protocols (Protocol A, B, C…). For example, Protocol A is “routine concentration detection”, Protocol B is “high concentration sample dilution re-detection”, and Protocol C is “triggered optical confirmation scanning”. When the AI model preliminarily judges that a certain channel is strongly positive according to the electrical signal, it will automatically terminate the current Protocol A, and according to the preset logic, control the fluid switching valve to transfer the remaining sample of the channel to an online diluter for dilution, and then execute Protocol B for re-quantification. At the same time, it can send instructions to the optical module to execute Protocol C to perform SERS scanning on the channel position to confirm the result.

[0064] Referring to Figure 2 A method for preparing a microfluidic chip unit for the system described in any of the above, characterized in that it comprises the steps of: forming a microfluidic network mold on a substrate layer by photolithography or laser etching; obtaining a microfluidic layer by PDMS casting replication; generating a graphene composite film in situ at the microfluidic detection area by vacuum-assisted self-assembly technology; directing the functional reagent to the membrane surface for modification by microfluidic method; irreversibly sealing the microfluidic layer with the substrate containing electrodes.

[0065] The specific steps to realize the preparation method are as follows: Mold making: Single crystal silicon wafer is selected as the mold substrate. A layer of SU-82050 negative photoresist is spin-coated on its surface, with a thickness of 100 μm. After covering with a mask plate, UV light is used for exposure, and after development, an SU-8 positive mold with a convex microfluidic structure is obtained. This standard photolithography process has detailed operating procedures in micro-electro-mechanical system (MEMS) processing.

[0066] PDMS flow channel layer replication: Mix polydimethylsiloxane (PDMS) prepolymer and curing agent at a mass ratio of 10:1, stir and degas. Pour the mixture into the SU-8 mold, place it in an 80°C oven for 1 hour. After cooling, carefully peel off the PDMS block with a recessed microfluidic structure from the mold. Use a punch to punch a 1.5 mm diameter hole at the inlet and outlet positions of the flow channel.

[0067] In situ generation of graphene composite film: Temporarily seal the PDMS flow channel layer to a flat glass sheet. Place the glass sheet in a vacuum chamber, and then place a piece of graphene film on the glass sheet. After vacuumizing, the graphene film is transferred to the surface of the microfluidic layer. The composite dispersion is injected from the inlets, filling the detection area. The whole assembly is put into a vacuum desiccator, vacuumed to -0.1 MPa and kept for 10 minutes. This process drives the gas out of the liquid in the flow channel by negative pressure, and promotes the close adsorption and self-assembly of GO sheets on the electrode substrate. Subsequently, it is put into a 60°C oven for slow drying for 12 hours, forming the composite film.

[0068] Microfluidic functionalization modification: The PDMS flow channel layer is peeled off from the temporary glass sheet. Using an external microsyringe pump and a multi-way valve, different antibody solutions are pumped into different channels, incubated at room temperature, and the functionalization modification of the film is completed. All channels are flushed with PBS buffer.

[0069] Irreversible sealing: another glass sheet is prepared as an electrode substrate, on which interdigital electrodes and gold wires have been prepared by photolithography and sputtering process. The glass sheet and the functionalized PDMS flow channel layer are put into an oxygen plasma cleaner and treated at 100 W power for 45 seconds. Immediately after treatment, the flat surface of the PDMS is aligned and attached to the electrode area of the glass sheet, and pressed gently. Due to the formation of silicon hydroxyl groups on the surface after plasma treatment, a firm covalent bond is formed between the two, achieving irreversible sealing.

[0070] Please refer to Figure 3 a method for multi-index parallel biochemical detection using the system of any of the above, characterized in that it comprises the steps of: injecting different samples to be tested or the same sample into different channels respectively; controlling the sample to flow through the functionalized graphene composite film sensor; the target analyte binds to the recognition element on the film, causing changes in the electrical or optical properties of the film; the signal acquisition module records the response signals of each channel; the processing module converts the signals into concentration information and outputs a multi-index detection report.

[0071] The specific operation process of the detection method is as follows: Sample preparation and injection: prepare the serum sample to be tested. If multi-index detection is performed (such as simultaneous measurement of glucose, lactic acid and uric acid), take 10 μL of the same serum sample and add it to three different sample reservoirs using a micropipette. If multi-sample and same-index screening is performed, take 10 μL of three different serum samples and add them to three reservoirs respectively. Connect the reservoirs to the corresponding inlets of the multi-channel syringe pump.

[0072] Sample loading and reaction: The method is initiated by the control software. The syringe pump pumps three samples into three independent microfluidic channels simultaneously at a constant flow rate of 5 μL / min. The samples mix with the pre-stored reaction buffer in the channels as they flow through the mixing structure, and then flow to the functionalized graphene composite membrane sensor area (e.g., channel one membrane modified glucose oxidase, channel two membrane modified lactate oxidase, channel three membrane modified uricase). The target substances catalyze the reaction with the enzymes on the membrane to produce products such as hydrogen peroxide, causing the conductivity of the membrane to change.

[0073] Signal recording: The signal acquisition module scans and records the current response values (i-t curve) of the three channel sensors at a constant potential at a frequency of 1 time per second, and records for 3 minutes.

[0074] Data processing and report generation: The signal processing module reads the recorded current data. For each channel, the platform current value I after the current-time curve reaches stability is taken. The I value is substituted into the calibration curve equation (concentration where k and b are known constants) established by the standard substance in advance, and the concentrations C1, C2, C3 of the target substances are automatically calculated. Finally, the processing module integrates these results, channel numbers, detection substance names, detection times, and other information to generate an electronic detection report in table form, and displays it on the software interface or prints it out. The entire conversion process from signal to concentration is a standardized data analysis process.

[0075] The above is only a specific embodiment of the present application, but the protection scope of the present application is not limited thereto, and any person skilled in the art can easily think of changes or replacements within the technical scope disclosed by the present application, which should be covered within the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

Claims

1. A multi-channel microfluidic biochemical detection system based on graphene composite film, characterized in that, The application relates to a microfluidic chip system for multi-target detection, comprising: at least one microfluidic chip unit, which comprises: a substrate layer; a multi-channel microfluidic network formed on the substrate layer, the network comprising at least three independent microfluidic channels; a sample introduction interface connected to the inlet of each microfluidic channel; a waste liquid collection interface connected to the outlet of each microfluidic channel; a graphene composite membrane sensor integrated in a specific detection area of each microfluidic channel, the sensor being composed of a support layer and a functionalized graphene composite membrane; a signal acquisition and processing module for real-time monitoring and processing of electrochemical or electrical impedance signals output by the sensors in each channel; a system control module for coordinating the timing of sample injection, fluid control and data acquisition. 2.The multi-channel microfluidic biochemical detection system based on graphene composite film according to claim 1, characterized in that, The microfluidic chip unit adopts a modular and detachable design, and each unit is connected in parallel or series through a standardized interface, so that the number of detection channels can be expanded. 3.The multi-channel microfluidic biochemical detection system based on graphene composite film according to claim 1, characterized in that, The graphene composite membrane has a three-dimensional porous structure and is composed of graphene oxide and functional nanomaterials, wherein the functional nanomaterials include but are not limited to at least one of metal nanoparticles, quantum dots, magnetic nanoparticles or molecularly imprinted polymers. 4.The multi-channel microfluidic biochemical detection system based on graphene composite film according to claim 3, characterized in that, The thickness of the graphene composite membrane is 10-500 nanometers, the porosity is 30%-80%, and the graphene composite membranes in different channels are differentially functionalized so that they can specifically respond to different types of target analytes. 5.The multi-channel microfluidic biochemical detection system based on graphene composite film according to claim 1, characterized in that, Each microfluidic channel is provided with: a micro-mixing structure for promoting the mixing of samples and reagents; a temperature control area for maintaining or adjusting the reaction temperature; at least one bypass sampling port for extracting a small amount of sample for auxiliary analysis during the detection process. 6.The multi-channel microfluidic biochemical detection system based on graphene composite film according to claim 1, characterized in that, The signal acquisition and processing module adopts a multiplexing technology to simultaneously or time-divisionally acquire signals in each channel, and is provided with a self-calibration algorithm, which can automatically compensate for baseline drift caused by membrane performance decay or environmental interference. 7.The multi-channel microfluidic biochemical detection system based on graphene composite film according to claim 1, characterized in that, Further comprising: an optical detection module comprising a movable confocal optical scanning head, which can perform fluorescence or Raman spectrum scanning on the surface of the graphene composite membrane in each channel; the surface of the graphene composite membrane is modified with fluorescently labeled probes or Raman-enhanced substrates corresponding to the target analyte. 8.The multi-channel microfluidic biochemical detection system based on graphene composite film according to claim 1, characterized in that, The system control module is integrated with an artificial intelligence algorithm, which can dynamically adjust: the sample flow rate of each channel; detection parameters; and automatically select and execute a preset subsequent analysis program according to the preliminary results.

9. A method for fabricating a microfluidic chip unit for use in the system of any one of claims 1-8, characterized by, The application further provides a method for detecting multiple targets by using the microfluidic chip system, comprising the steps of: forming a microfluidic channel network mold on the substrate layer by photolithography or laser etching; obtaining a microfluidic channel layer by PDMS pouring replication; generating a graphene composite membrane in situ at the detection area of the microfluidic channel by vacuum-assisted self-assembly technology; directly transporting functional reagents to the surface of the membrane through microfluidic mode for modification; irreversibly sealing the microfluidic channel layer and the substrate containing electrodes.

10. A method for multi-index parallel biochemical detection using the system of any one of claims 1-8, characterized in that, The application further provides a method for detecting multiple targets by using the microfluidic chip system, comprising the steps of: injecting different samples to be detected or the same sample into different channels respectively; controlling the sample to flow through the functionalized graphene composite membrane sensor; target analytes bind to the recognition elements on the membrane, causing changes in the electrical or optical properties of the membrane; the signal acquisition module records the response signals of each channel; the processing module converts the signals into concentration information and outputs a multi-index detection report.

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