Protein detection method based on coupling of liquid drop triboelectricity and sliding dynamics and related equipment

Abstract

The invention relates to the technical field of protein determination, in particular to a protein detection method based on coupling of liquid drop triboelectricity and sliding dynamics and related equipment. The method comprises the following steps: acquiring a friction electric signal and a droplet sliding image of a to-be-detected protein droplet; determining droplet kinetic parameters of the to-be-detected protein droplet according to the droplet sliding image; determining droplet electrical parameters of the to-be-detected protein droplet according to the friction electric signal; performing feature fusion on the liquid drop electrical parameters and the liquid drop kinetic parameters to obtain friction sliding features of the to-be-detected protein liquid drops; and determining the protein droplet type or protein droplet concentration of the to-be-detected protein droplet according to the friction sliding characteristics. According to the method, characteristic coupling is carried out through liquid drop electrical parameters and liquid drop kinetic parameters, direct determination of a protein sample is realized, protein liquid drop type and concentration determination steps are reduced, and determination efficiency is improved. The method has the advantages that the cost is low, the testing device is simple, pretreatment is not needed, and the detection result response is fast.

Description

Technical Field

[0001] This application relates to the field of protein assay technology, and in particular to a protein detection method and related equipment based on the coupling of droplet triboelectricity and sliding dynamics. Background Technology

[0002] In related technologies, triboelectric nanosensors are novel sensors based on the coupling effect of triboelectricity and electrostatic induction, capable of generating electrical signals through the flow of liquid at a triboelectric interface. Since different types of liquids generate different electrical signals, the determination of unknown liquids using the electrical signals from triboelectric nanosensors is widely adopted as a new qualitative and quantitative analysis technique.

[0003] In the field of biosensing, triboelectric nanosensors typically require the modification of the triboelectric layer surface with specific recognition molecules (such as antibodies or nucleic acid probes). When the molecules of the target liquid bind to the triboelectric layer, they will output an electrical signal with a specific waveform. Analyzing this electrical signal can enable the detection of the type and concentration of the target liquid.

[0004] Modifying the surface of the triboelectric layer material in triboelectric nanosensors with specific recognition molecules is a tedious process. Unmodified triboelectric nanosensors output triboelectric signals with insufficient characteristics, making it difficult to directly determine the type and concentration of target protein liquids. Summary of the Invention

[0005] To overcome the problems existing in related technologies, this application provides a protein detection method and related equipment based on the coupling of droplet triboelectricity and sliding dynamics. This method extracts features by fusing the triboelectric signal output by a non-specifically molecularly modified triboelectric nanosensor with droplet dynamic parameters recorded by droplet sliding images. It can directly determine the type and concentration of the target protein liquid, reduce the number of measurement steps, and improve the measurement efficiency. This application has the advantages of low cost, simple testing equipment, no need for pretreatment, and fast response of detection results.

[0006] To achieve the above objectives, one aspect of this application proposes a protein detection method based on the coupling of droplet triboelectricity and sliding dynamics, comprising: The triboelectric signal and droplet sliding image of the protein droplet to be detected are acquired; the triboelectric signal is the output signal of the protein droplet to be detected sliding on the triboelectric nanosensor, and the droplet sliding image is a video frame image of the protein droplet to be detected during the sliding process. The droplet dynamics parameters of the protein droplet to be detected are determined based on the droplet sliding image. The droplet electrical parameters of the protein droplet to be detected are determined based on the triboelectric signal. The electrical and dynamic parameters of the droplet are fused to obtain the frictional sliding characteristics of the protein droplet to be detected. The type or concentration of the protein droplet to be detected is determined based on the frictional sliding characteristics.

[0007] In one embodiment, the droplet dynamics parameters include droplet sliding velocity and contact angle hysteresis; The droplet dynamics parameters of the protein droplet to be detected are determined based on the droplet sliding image, specifically including: For each frame of the droplet sliding image, the forward contact point of the protein droplet to be detected is identified to obtain the forward contact point image coordinates of the protein droplet to be detected. The droplet sliding speed is determined based on the image coordinates of the forward contact point and the droplet sliding time. Contour recognition is performed on the protein droplet to be detected in each frame of the droplet sliding image to obtain the forward contact angle and backward contact angle of the protein droplet to be detected; The contact angle hysteresis is obtained from the difference between the forward contact angle and the backward contact angle.

[0008] In one embodiment, the droplet electrical parameters include voltage value, cumulative signal strength, charging rate, and peak-to-peak time difference; The droplet electrical parameters of the protein droplet to be detected are determined based on the triboelectric signal, specifically including: Obtain the voltage value of the triboelectric signal; The cumulative signal strength is obtained by integrating the voltage value of the triboelectric signal over the droplet's sliding time. The charging rate is determined based on the slope of the curve of the rising edge of the voltage value of the triboelectric signal; The peak-to-peak time difference is determined based on the time interval between the positive and negative peaks of the triboelectric signal.

[0009] In one embodiment, determining the protein droplet type or concentration of the protein droplet to be detected based on the frictional sliding characteristics specifically includes: The nearest neighbors of the frictional sliding feature are searched in the feature database to obtain the protein droplet type or protein droplet concentration of the frictional sliding feature. The feature database is constructed by measuring droplets with known protein droplet types and known protein droplet concentrations.

[0010] In one embodiment, determining the protein droplet type or protein droplet concentration based on the frictional sliding characteristics further includes: The frictional sliding features are input into the trained classification model to obtain the protein droplet type or protein droplet concentration of the protein droplet to be detected. The classification model is trained using feature data obtained by measuring droplets with known protein droplet types and known protein droplet concentrations.

[0011] To achieve the above objectives, another aspect of this application proposes a protein detection system, comprising: Triboelectric nanosensors are used to collect triboelectric signals from protein droplets to be detected. The image acquisition module is used to acquire images of the droplet sliding of the protein droplet to be detected; The data processing module is used to perform the steps in the protein detection method described in this application.

[0012] In one embodiment, the triboelectric nanosensor includes a droplet sliding friction layer, an electrode, and a substrate; The droplet sliding friction layer is attached to the surface of the substrate, wherein when the protein droplet to be detected slides on the droplet sliding friction layer, the electrode outputs the triboelectric signal.

[0013] In one embodiment, the droplet sliding friction layer comprises a polytetrafluoroethylene (PTFE) layer that has been plasma-treated and impregnated with lubricating oil.

[0014] To achieve the above objectives, another aspect of this application proposes an electronic device, including a memory and a processor, wherein the memory stores executable code, which, when executed by the processor, implements the protein detection method described in this application.

[0015] To achieve the above objectives, another aspect of this application proposes a computer-readable storage medium storing executable code, which, when executed by a processor, implements the protein detection method described in this application.

[0016] The technical solution provided in this application may include the following beneficial effects: This application provides a protein detection method and related equipment based on the coupling of droplet triboelectricity and sliding dynamics. The method acquires the triboelectric signal and droplet sliding image of the protein droplet to be detected; determines the droplet dynamic parameters of the protein droplet to be detected based on the droplet sliding image; determines the droplet electrical parameters of the protein droplet to be detected based on the triboelectric signal; fuses the droplet electrical parameters and droplet dynamic parameters to obtain the triboelectric sliding characteristics of the protein droplet to be detected; and determines the protein droplet type or protein droplet concentration based on the triboelectric sliding characteristics. This method extracts features from the triboelectric signal acquired by an unmodified triboelectric nanosensor to obtain droplet electrical parameters; and acquires images of the droplet sliding process to obtain droplet dynamic parameters. The feature fusion of these two methods significantly amplifies the differences in protein droplet type and concentration between different liquids, thereby overcoming the limitation that unmodified triboelectric nanosensors cannot directly determine the protein droplet type and concentration, and directly obtaining the protein droplet type or protein droplet concentration of the protein droplet to be detected.

[0017] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and do not limit this application. Attached Figure Description

[0018] The above and other objects, features and advantages of this application will become more apparent from the more detailed description of exemplary embodiments thereof in conjunction with the accompanying drawings, wherein the same reference numerals generally represent the same components in the exemplary embodiments thereof.

[0019] Figure 1 A schematic flowchart of the protein detection method provided in the embodiments of this application; Figure 2 A flowchart illustrating step 102 of the protein detection method provided in this application embodiment; Figure 3 A geometric representation of the droplet dynamics parameters in the method provided in the embodiments of this application; Figure 4 A flowchart illustrating step 103 of the protein detection method provided in this application embodiment; Figure 5 A geometric representation of the droplet electrical parameters in the method provided in the embodiments of this application; Figure 6 A comparison of signals generated by different types of protein droplets; Figure 7 This is a schematic diagram showing the distribution of different types of protein droplets on a six-dimensional radar image; Figure 8A schematic diagram of the distribution of human serum albumin at different concentrations on a six-dimensional radar image; Figure 9 This is a schematic diagram of the protein detection system shown in the embodiments of this application; Figure 10 This is another structural schematic diagram of the protein detection system shown in the embodiments of this application; Figure 11 A signal comparison diagram of single-electrode and dual-electrode structures of a protein detection system; Figure 12 This is a comparison of the maximum voltage values ​​for single-electrode and dual-electrode structures in a protein detection system. Detailed Implementation

[0020] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of this application and are not intended to limit it. In the following description, when referring to the accompanying drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with those of this application; they are merely examples of apparatuses and methods consistent with some aspects of the embodiments of this application as detailed in the appended claims.

[0021] It is understood that the terms “first,” “second,” etc., used in this application may be used herein to describe various concepts, but unless otherwise stated, these concepts are not limited by these terms. These terms are only used to distinguish one concept from another. For example, without departing from the scope of the embodiments of this application, first information may also be referred to as second information, and similarly, second information may also be referred to as first information. Depending on the context, the words “if,” “when,” or “in response to a determination” as used herein may be interpreted as “when…” or “when…” or “in response to a determination.”

[0022] As used in this application, the terms "at least one", "multiple", "each", "any", etc., "at least one" includes one, two or more, "multiple" includes two or more, "each" refers to each of the corresponding multiples, and "any" refers to any one of the multiples.

[0023] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing embodiments of this application only and is not intended to limit this application.

[0024] In related technologies, in the field of biosensing, triboelectric nanosensors modify the surface of the triboelectric layer with specific recognition molecules, causing the surface charge density or interface properties of the biomolecules being measured to change due to binding events, thereby causing changes in the output electrical signal, and thus enabling the determination of glucose, lactic acid, urea, ions and some biomacromolecules.

[0025] Triboelectric Nanosensor (TENS) is a sensor based on triboelectric nanogenerator that can directly convert mechanical stimuli (such as pressure, vibration, motion, droplet impact, etc.) into electrical signal output.

[0026] The process of modifying the surface of the tribological layer material of the tribological nanosensor with specific recognition molecules is cumbersome, which reduces the efficiency of measuring protein droplets. Existing technologies have the following drawbacks: (1) Triboelectric nanosensors rely on surface functionalization modification. Only through specific molecular modification can the identification of specific molecules, ions or markers be achieved. For other types of liquids, multiple measurements are required to eliminate them one by one.

[0027] (2) The triboelectric signal output by the unmodified triboelectric nanosensor is difficult to effectively distinguish the type and concentration of protein droplets in the liquid to be detected.

[0028] To address the aforementioned technical deficiencies, this application provides a protein detection method and related equipment based on the coupling of droplet triboelectricity and sliding dynamics. The method obtains droplet electrical parameters through triboelectric signals generated by an unmodified triboelectric nanosensor, and extracts features by fusing droplet dynamic parameters recorded from droplet sliding images. The resulting fused feature representation can directly determine the type and concentration of the protein droplet to be detected.

[0029] Figure 1 This is a schematic flowchart of the protein detection method provided in the embodiments of this application.

[0030] See Figure 1 The protein detection method provided in this application includes the following steps: 101. Acquire the triboelectric signal and droplet sliding image of the protein droplet to be detected; 102. Determine the droplet dynamics parameters of the protein droplet to be detected based on the droplet sliding image; 103. Determine the droplet electrical parameters of the protein droplet to be detected based on the triboelectric signal; 104. The electrical parameters and dynamic parameters of the droplet are fused to obtain the frictional sliding characteristics of the protein droplet to be detected; 105. Determine the type or concentration of the protein droplet to be detected based on the frictional sliding characteristics.

[0031] In step 101, the triboelectric signal is the output signal of the protein droplet to be detected sliding on the triboelectric nanosensor, and the droplet sliding image is a video frame image of the protein droplet to be detected sliding down.

[0032] In this embodiment, triboelectric signals collected by an unmodified triboelectric nanosensor are used for feature extraction to obtain droplet electrical parameters. Additionally, droplet dynamic parameters are obtained by acquiring images of the droplet sliding process. These two sets of parameters are then fused. The resulting feature representation enhances the signal through the mutual complementarity of multiple signals, significantly amplifying the differences in the types and concentrations of physical protein droplets between different liquids. This overcomes the limitation of unmodified triboelectric nanosensors being unable to directly measure these parameters, enabling direct measurement of protein liquids.

[0033] Figure 2 This is a flowchart illustrating step 102 of the protein detection method provided in an embodiment of this application.

[0034] Figure 3 A geometric representation of the droplet dynamics parameters in the method provided in the embodiments of this application.

[0035] In one embodiment, the droplet dynamics parameters include droplet sliding velocity and contact angle hysteresis.

[0036] See Figure 2 Step 102 of the method shown in the embodiments of this application includes, but is not limited to: 201. For each frame of the droplet sliding image, identify the forward contact point of the protein droplet to be detected to obtain the forward contact point image coordinates of the protein droplet to be detected; 202. Determine the droplet sliding speed based on the image coordinates of the forward contact point and the droplet sliding time; 203. Perform contour recognition on the protein droplet to be detected in each frame of the droplet sliding image to obtain the forward contact angle and backward contact angle of the protein droplet to be detected; 204. The contact angle hysteresis is obtained based on the difference between the forward contact angle and the backward contact angle.

[0037] It is understandable that the droplet sliding time is the response time of the triboelectric signal, that is, the time it takes for the droplet to slide down the surface. Figure 3 The duration of the slide on the substrate shown. Preferably, the angle between the substrate and the horizontal plane is 45 degrees.

[0038] In step 201, the forward contact point image coordinates are the pixel with the lowest height in the pixel region of the protein droplet to be detected, i.e., the distance... Figure 3 The pixel furthest from the 0 mark on the substrate shown.

[0039] In step 202, the pixel distance between the image coordinates of the forward contact point at the initial and final times is calculated, the droplet displacement is determined by the pixel distance, and the droplet displacement is removed to obtain the droplet sliding speed based on the droplet sliding time.

[0040] In step 203, the forward contact angle and the backward contact angle are as follows: Figure 3 As shown, the contact angle lag is equal to the difference between the forward contact angle and the backward contact angle.

[0041] Figure 4 This is a flowchart illustrating step 103 of the protein detection method provided in an embodiment of this application.

[0042] Figure 5 A geometric representation of the droplet electrical parameters in the method provided in the embodiments of this application.

[0043] In one embodiment, the droplet electrical parameters include voltage value, cumulative signal strength, charging rate, and peak-to-peak time difference.

[0044] See Figure 4 Step 103 of the method shown in the embodiments of this application includes, but is not limited to: 301. Obtain the voltage value of the triboelectric signal; 302. The cumulative signal strength is obtained by integrating the voltage value of the triboelectric signal over the droplet sliding time; 303. Determine the charging rate based on the slope of the curve of the rising edge of the voltage value of the triboelectric signal; 304. Determine the peak-to-peak time difference based on the time interval between the positive peak and the negative peak of the triboelectric signal.

[0045] Furthermore, after normalizing the two-dimensional droplet dynamic parameters and the four-dimensional droplet electrical parameters obtained in the above steps, the six-dimensional frictional sliding characteristics are fused together.

[0046] It should be noted that existing triboelectric nanosensors have not undergone specific modification, and it is difficult to effectively determine the type and concentration of protein droplets in protein solutions by relying on a single electrical index (such as voltage peak-to-peak value). The triboelectric signals generated by different concentration ranges and different types of protein solutions do not show significant differences.

[0047] This application proposes a protein detection method that couples "triboelectric signals" and "droplet sliding dynamics," applicable to the determination of biomacromolecule solutions. This method can effectively distinguish between different types and concentrations of protein solutions. Furthermore, by utilizing droplet electrical and kinetic parameters, the differences in protein droplet type and concentration within the protein solution can be "amplified" through triboelectric sliding characteristics, significantly improving the accuracy and efficiency of protein solution determination.

[0048] In one embodiment, step 105 of the method shown in this application includes: searching for the nearest neighbor of the frictional sliding feature in a feature database to obtain the protein droplet type or protein droplet concentration of the frictional sliding feature.

[0049] It should be noted that the feature database is pre-constructed using data collected from protein solutions with known droplet types and concentrations using the protein detection method described in the embodiments of this application.

[0050] In another embodiment of step 105, step 105 may further include: inputting the frictional sliding features into the trained classification model to obtain the protein droplet type or protein droplet concentration of the protein droplet to be detected.

[0051] It should be noted that the classification model constructs training samples using feature data obtained from protein solutions with known droplet types and concentrations, and trains the classification model using these training samples. The classification model is either a classification tree or a decision tree in a neural network model.

[0052] The protein detection method based on the coupling of droplet triboelectricity and sliding dynamics provided in this application relates to the field of information technology. This protein detection method can be applied to a terminal, a server, or software running on either a terminal or a server. In some embodiments, the terminal can be a smartphone, tablet, laptop, desktop computer, smart speaker, smartwatch, or in-vehicle terminal, but is not limited to these. The server can be configured as an independent physical server, a server cluster consisting of multiple physical servers, or a distributed system. It can also be configured as a cloud server providing basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, CDN, and big data and artificial intelligence platforms. The server can also be a node server in a blockchain network. The software can be an application implementing the protein detection method, but is not limited to the above forms.

[0053] This application can be used in a wide variety of general-purpose or special-purpose computer system environments or configurations. Examples include: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronics devices, network PCs, minicomputers, mainframe computers, and distributed computing environments including any of the above systems or devices. This application can be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc., that perform specific tasks or implement specific abstract data types. This application can also be practiced in distributed computing environments where tasks are performed by remote processing devices connected via a communication network. In distributed computing environments, program modules can reside in local and remote computer storage media, including storage devices.

[0054] The following is a detailed description and explanation of the solutions in the embodiments of this application, using specific application examples of protein liquid assays: A. Qualitative Analysis: In this application embodiment, four typical proteins with different molecular weights, isoelectric points, and surface charge characteristics were selected to prepare the test liquid, and the four protein droplets were qualitatively analyzed by the method described above.

[0055] The four types of liquids to be tested are: lysozyme (Lyz), fibrinogen (Fbg), hemoglobin (Hb), and human serum albumin (HSA).

[0056] A1. Four types of protein droplets were released onto the sliding surface of a triboelectric nanosensor, and the triboelectric signals and droplet sliding images during the sliding process were collected simultaneously.

[0057] Figure 6 A comparison of signals generated by different types of protein droplets.

[0058] like Figure 6 As shown, the first row represents the waveform of the triboelectric signal of protein droplets. The waveforms of triboelectric signals generated by different proteins show significant differences in peak size and waveform width.

[0059] The second row shows the trajectory of the protein droplets, a curve depicting the displacement of the droplets over time, with the slope of the curve representing the droplet's sliding speed. The lysozyme droplet exhibits a steeper sliding curve, indicating a faster sliding speed; while the human serum albumin curve is gentler and has a longer total sliding time, suggesting greater resistance to its interaction with the surface.

[0060] The third row represents the contact hysteresis of the protein droplet, recording the forward contact angle during the droplet's sliding process. and backward contact angle The contact angle hysteresis of different protein droplets on the lubricated surface. Significant differences exist, reflecting the different adsorption behaviors of different protein molecules at the interface.

[0061] A2. Extract the droplet dynamic parameters and droplet electrical parameters from the above triboelectric signal and droplet sliding image, respectively.

[0062] Droplet dynamics parameters include droplet sliding velocity and contact angle hysteresis. Droplet sliding velocity is the average speed of droplet sliding, reflecting the magnitude of interfacial friction. Contact angle hysteresis is the difference between the advance angle and the retreat angle, reflecting the pinning effect and wettability of protein droplets on the substrate of the triboelectric nanosensor.

[0063] The droplet's electrical parameters include voltage, cumulative signal strength, charging rate, and peak-to-peak time difference. The voltage is the peak value of the triboelectric signal, reflecting the interfacial charge transfer density. The cumulative signal strength is the integral of the voltage over time in the triboelectric signal, reflecting the cumulative signal strength during a single sliding process. Under fixed electrometer input impedance / test circuit parameters, its physical meaning is positively correlated with the total amount of charge transferred at the interface. The charging slope is the slope of the rising edge of the voltage waveform, reflecting the charge buildup rate when the droplet contacts the electrode edge. The peak-to-peak time difference is the time interval between the positive and negative peaks of the waveform, related to the magnitude of the interaction force between the protein droplet to be detected and the triboelectric layer.

[0064] A3. After standardizing the above 6 droplet dynamic parameters and droplet electrical parameters, the frictional sliding characteristics of the protein droplet to be detected are obtained, and a six-dimensional radar image is drawn based on the frictional sliding characteristics.

[0065] Figure 7 This is a schematic diagram of the distribution of different types of protein droplets on a six-dimensional radar image.

[0066] like Figure 7As shown, the four protein droplets exhibit distinctly different "fingerprint" shapes in the multidimensional feature space, achieving excellent differentiation. Human serum albumin (HSA, red area) exhibits characteristics of "high voltage, low velocity, and low hysteresis," with a large coverage area in the radar image, a voltage peak close to 300mV, slow sliding velocity, and small contact angle hysteresis, indicating that HSA molecules are easily adsorbed on the sliding surface of the triboelectric nanosensor. Fibrinogen (Fbg, purple area) exhibits characteristics of "high hysteresis and low velocity," with the largest contact angle hysteresis. Lysozyme (Lyz, yellow area) exhibits characteristics of "low voltage and low charge," with the smallest radar image area, mainly concentrated in the low-value region, indicating its weak electron exchange capacity with the fluorinated lubricating layer surface. Hemoglobin (Hb, pink area) exhibits characteristics of "high charge and broad peak duration," with a large cumulative signal intensity and a long peak-to-peak time difference, forming a unique characteristic distribution.

[0067] The protein detection method shown in the embodiments of this application has the ability to quantitatively analyze HSA molecular solutions of different concentrations and can sensitively distinguish the concentration changes of the same protein.

[0068] It is understood that by constructing a 6-dimensional characteristic fingerprint map using the protein detection method coupled with "triboelectricity-sliding dynamics" proposed in the embodiments of this application, the differences in the types and concentrations of physical protein droplets between different proteins can be significantly amplified. Figure 7 This study clearly demonstrates that the method can rapidly, intuitively, and with high sensitivity distinguish between lysozyme, fibrinogen, hemoglobin, and human serum albumin, validating its application potential in the field of label-free detection of body fluid proteins.

[0069] B. Quantitative analysis: In this embodiment, human serum albumin (HSA) was used as the model protein, and five groups of HSA molecular solutions were prepared at concentrations of 0.0001% (w / v), 0.001% (w / v), 0.01% (w / v), 0.1% (w / v), and 1% (w / v).

[0070] It should be noted that each concentration of HSA molecular solution was freshly prepared in the same batch of buffer system and mixed with gentle shaking before testing to avoid the influence of protein precipitation or aggregation on the results.

[0071] B1. Five groups of HSA molecular solutions were released into the sliding surface of a triboelectric nanosensor, and the triboelectric signals and droplet sliding images during the sliding process were collected simultaneously.

[0072] B2. Extract droplet dynamic parameters and droplet electrical parameters from the above triboelectric signals and droplet sliding images, respectively.

[0073] B3. After standardizing the above 6 feature parameters, the frictional sliding characteristics of the protein droplet to be detected are obtained, and a six-dimensional radar map is drawn based on the frictional sliding characteristics.

[0074] Figure 8 This is a schematic diagram of the distribution of human serum albumin at different concentrations on a six-dimensional radar chart.

[0075] Following the above method, the embodiments of this application tested five different concentrations of HSA molecular solutions and obtained the following results: Figure 8 The distribution of the five HSA molecular solutions shown on a six-dimensional radar chart.

[0076] like Figure 8 As shown, the five concentrations exhibit good separation in the six-dimensional feature space, with minimal overlap in the radar map regions between adjacent concentration groups. In particular, when considering multiple features such as voltage, area, velocity, and contact angle hysteresis, the fingerprint shape and size of HSAs with different concentrations show significant differences.

[0077] This embodiment verifies the ability to distinguish between different concentrations of HSA molecular solutions under the same protein type conditions, proving that the method can not only distinguish between different types of proteins, but also sensitively detect changes in the concentration of the same protein.

[0078] Figure 9 This is a schematic diagram of the protein detection system shown in an embodiment of this application.

[0079] See Figure 9 This application also provides a protein detection system, including: Triboelectric nanosensors are used to collect triboelectric signals from protein droplets to be detected. The image acquisition module is used to acquire images of the sliding motion of the protein droplets to be detected. The data processing module is used to execute steps 101 to 105 as described in the above embodiments.

[0080] It is understood that the content of the above method embodiments is applicable to this system embodiment. The specific functions implemented in this system embodiment are the same as those in the above method embodiments, and the beneficial effects achieved are also the same as those achieved in the above method embodiments.

[0081] Figure 10 This is another structural schematic diagram of the protein detection system shown in the embodiments of this application.

[0082] In one embodiment, the triboelectric nanosensor includes a droplet sliding friction layer, a substrate, and electrodes; The droplet sliding friction layer is attached to the surface of the substrate, wherein when the protein droplet to be detected slides on the droplet sliding friction layer, the electrode outputs the triboelectric signal.

[0083] In one embodiment, the substrate of the triboelectric nanosensor is preferably a PMMA (Polymethylmethacrylate) substrate, on which a copper electrode is pre-attached.

[0084] In this embodiment, the copper electrode is electrically connected to an electrometer via a wire, and the electrometer collects the triboelectric signal generated during the sliding of the droplet.

[0085] In one embodiment, two copper electrodes are disposed at the bottom of the PMMA substrate to enhance the signal strength of the triboelectric signal.

[0086] Figure 11 A signal comparison diagram of single-electrode and dual-electrode structures of a protein detection system; Figure 12 This is a comparison of the maximum voltage values ​​for single-electrode and dual-electrode structures in a protein detection system.

[0087] like Figure 11 and Figure 12 As shown, under the same droplet sliding conditions, the bottom dual-electrode structure can significantly enhance the triboelectric output signal, providing a higher signal-to-noise ratio and a more reliable detection basis for protein sample differentiation based on droplet sliding triboelectric signals.

[0088] In one embodiment, the droplet sliding friction layer comprises a polytetrafluoroethylene (PTFE) layer that has been plasma-treated and impregnated with lubricating oil.

[0089] In this embodiment, a polytetrafluoroethylene (PTFE) film is covered on the surface of the copper electrode and the exposed area therebetween. Lubricating oil is further introduced onto the surface of the PTFE film to form a lubricating fluid impregnation and sliding surface, which can prevent protein droplets from adhering to the sensor surface.

[0090] During detection, the droplet sliding device is tilted and releases the protein droplet to be tested from the top of the PMMA substrate. The protein droplet slides downward along the droplet sliding friction layer, and an electrometer collects the voltage values ​​of the two electrodes during the sliding process.

[0091] In one embodiment, the preparation steps of the droplet sliding device are as follows: Step 1: Select a methyl methacrylate (PMMA) board as the PMMA substrate and remove surface impurities and oil stains.

[0092] Step 2: On one side surface of the PMMA substrate, attach electrodes in a parallel direction, and connect wires to one end of each of the two copper electrodes to serve as interfaces for subsequent electrical testing and external circuit connections.

[0093] Step 3: Smoothly adhere the polytetrafluoroethylene (PTFE) film onto the surface of the PMMA substrate and the electrodes, ensuring that the PTFE film completely covers the electrode area and the exposed substrate surface. During application, spread the film gradually from one end, removing air bubbles and ensuring a tight bond between the PTFE film and the substrate, without obvious wrinkles or air pockets, thus forming the PTFE layer.

[0094] Step 4: After covering, use a deionized air purging device to continuously blow the PTFE surface for about 20 minutes to remove dust particles adsorbed on the surface and reduce static electricity accumulation, so as to obtain a clean PTFE film surface.

[0095] Step 5: Immediately after plasma treatment, immerse the entire sample in fluorinated lubricating oil to ensure the PTFE film is fully in contact with and wetted. The immersion time should be controlled to approximately 12 hours to allow the lubricating oil to fully penetrate the surface microstructure of the PTFE film and form a stable lubricating layer on its surface.

[0096] Step 6: After soaking, remove the sample from the lubricating oil and place it vertically for about 5 minutes to allow excess lubricating oil to flow down and slide off naturally until no more oil droplets flow out from the sample surface. This forms a uniform and stable sliding lubrication layer on the surface of the PTFE film, resulting in the droplet sliding friction layer used for droplet sliding and triboelectric detection.

[0097] In one embodiment, the tilt angle of the PMMA substrate is 30 to 60 degrees, and the tilt angle is the angle between the PMMA substrate and the horizontal plane.

[0098] Preferably, the PMMA substrate is tilted at an angle of 45 degrees. When the tilt angle is set to 45 degrees, the protein droplet to be detected exhibits stable and moderately sliding behavior, with the droplet's sliding time maintained at approximately 5.5 seconds. This speed ensures that the droplet can overcome resistance and smoothly pass through the electrode area, generating a complete triboelectric waveform; it also provides a suitable observation time, allowing the optical system to acquire clear images of droplet morphological changes with sufficient frames, facilitating accurate calculation of key dynamic parameters such as the advance angle, retreat angle, and sliding speed.

[0099] In one embodiment, the optical acquisition module is preferably a camera arranged to the side, used to capture images of the side of the droplet of the liquid to be detected, and record the morphology and trajectory of the protein droplet during the entire sliding process. The data processing module analyzes and processes the video frame images captured by the camera to obtain the droplet dynamic parameters during the droplet sliding process on the surface, including the droplet sliding speed, the advancing contact angle of the leading edge, the retreating contact angle of the trailing edge, and the contact angle hysteresis.

[0100] In one embodiment, the system further includes a droplet release module for releasing the droplet to be measured.

[0101] Preferably, the droplet release module is a needle. The protein solution to be tested is slowly extruded into protein droplets using a syringe pump, and the droplets are gently released onto the droplet sliding friction layer. Under the influence of gravity, the protein droplets slide on the surface, sequentially passing through the bottom copper electrode area. During the contact-separation and relative sliding process between the droplet and the lubricating PTFE surface, a triboelectric signal related to the droplet's motion is generated on the bottom electrode. This signal is collected and recorded in real time using an electrometer (Tonghui2690) to extract electrical signal characteristics such as voltage peak-to-peak difference, cumulative signal strength, charging slope, and peak-to-peak spacing.

[0102] This application also provides a computer-readable storage medium storing executable code, wherein the processor executes the executable code to implement the protein detection method described in this application.

[0103] Memory, as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs and non-transitory computer-executable programs. Furthermore, memory may include high-speed random access memory, and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device.

[0104] In some implementations, the memory may optionally include memory remotely located relative to the processor, which can be connected to the processor via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0105] The embodiments described in this application are for the purpose of more clearly illustrating the technical solutions of the embodiments of this application, and do not constitute a limitation on the technical solutions provided by the embodiments of this application. As those skilled in the art will know, with the evolution of technology and the emergence of new application scenarios, the technical solutions provided by the embodiments of this application are also applicable to similar technical problems.

[0106] Those skilled in the art will understand that the technical solutions shown in the figures do not constitute a limitation on the embodiments of this application, and may include more or fewer steps than shown, or combine certain steps, or different steps.

[0107] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0108] Those skilled in the art will understand that all or some of the steps in the methods disclosed above, as well as the functional modules / units in the systems and devices, can be implemented as software, firmware, hardware, or appropriate combinations thereof.

[0109] The terms “first,” “second,” “third,” “fourth,” etc. (if present) in the specification and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms “comprising” and “having,” and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0110] It should be understood that in this application, "at least one (item)" means one or more, and "more than" means two or more. "And / or" is used to describe the relationship between related objects, indicating that three relationships can exist. For example, "A and / or B" can represent three cases: only A exists, only B exists, and both A and B exist simultaneously, where A and B can be singular or plural. The character " / " generally indicates that the preceding and following related objects are in an "or" relationship. "At least one (item) of the following" or similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one (item) of a, b, or c can represent: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, and c can be single or multiple.

[0111] In the several embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of the units described above is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0112] The units described above as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0113] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0114] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes multiple instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned storage medium includes various media capable of storing programs, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0115] The preferred embodiments of the present application have been described above with reference to the accompanying drawings, but this does not limit the scope of the claims of the present application. Any modifications, equivalent substitutions, and improvements made by those skilled in the art without departing from the scope and substance of the embodiments of the present application shall be within the scope of the claims of the present application.

Claims

1. A protein detection method based on the coupling of droplet triboelectricity and sliding dynamics, characterized in that, include: Acquire the triboelectric signal and droplet sliding image of the protein droplet to be detected; The triboelectric signal is the output signal of the protein droplet to be detected sliding on the triboelectric nanosensor, and the droplet sliding image is a video frame image of the protein droplet to be detected sliding down. The droplet dynamics parameters of the protein droplet to be detected are determined based on the droplet sliding image. The droplet electrical parameters of the protein droplet to be detected are determined based on the triboelectric signal. The electrical and dynamic parameters of the droplet are fused to obtain the frictional sliding characteristics of the protein droplet to be detected. The type or concentration of the protein droplet to be detected is determined based on the frictional sliding characteristics.

2. The protein detection method as described in claim 1, characterized in that, The droplet dynamics parameters include droplet sliding velocity and contact angle hysteresis; The droplet dynamics parameters of the protein droplet to be detected are determined based on the droplet sliding image, specifically including: For each frame of the droplet sliding image, the forward contact point of the protein droplet to be detected is identified to obtain the forward contact point image coordinates of the protein droplet to be detected. The droplet sliding speed is determined based on the image coordinates of the forward contact point and the droplet sliding time. Contour recognition is performed on the protein droplet to be detected in each frame of the droplet sliding image to obtain the forward contact angle and backward contact angle of the protein droplet to be detected; The contact angle hysteresis is obtained from the difference between the forward contact angle and the backward contact angle.

3. The protein detection method as described in claim 1, characterized in that, The droplet electrical parameters include voltage value, cumulative signal strength, charging rate, and peak-to-peak time difference; The droplet electrical parameters of the protein droplet to be detected are determined based on the triboelectric signal, specifically including: Obtain the voltage value of the triboelectric signal; The cumulative signal strength is obtained by integrating the voltage value of the triboelectric signal over the droplet's sliding time. The charging rate is determined based on the slope of the curve of the rising edge of the voltage value of the triboelectric signal; The peak-to-peak time difference is determined based on the time interval between the positive and negative peaks of the triboelectric signal.

4. The protein detection method according to any one of claims 1 to 3, characterized in that, Determining the type or concentration of the protein droplet to be detected based on the described frictional sliding characteristics specifically includes: The nearest neighbors of the frictional sliding feature are searched in the feature database to obtain the protein droplet type or protein droplet concentration of the frictional sliding feature. The feature database is constructed by measuring droplets with known protein droplet types and known protein droplet concentrations.

5. The protein detection method according to any one of claims 1 to 3, characterized in that, Determining the type or concentration of the protein droplet to be detected based on the described frictional sliding characteristics also includes: The frictional sliding features are input into the trained classification model to obtain the protein droplet type or protein droplet concentration of the protein droplet to be detected. The classification model is trained using feature data obtained by measuring droplets with known protein droplet types and known protein droplet concentrations.

6. A protein detection system, characterized in that, include: Triboelectric nanosensors are used to collect triboelectric signals from protein droplets to be detected. The image acquisition module is used to acquire images of the droplet sliding of the protein droplet to be detected; A data processing module for performing the steps in the method according to any one of claims 1 to 5.

7. The protein detection system as described in claim 6, characterized in that, The triboelectric nanosensor includes a droplet sliding friction layer, electrodes, and a substrate; The droplet sliding friction layer is attached to the surface of the substrate, wherein when the protein droplet to be detected slides on the droplet sliding friction layer, the electrode outputs the triboelectric signal.

8. The protein detection system as described in claim 7, characterized in that, The droplet sliding friction layer includes a polytetrafluoroethylene (PTFE) layer, which is subjected to plasma treatment and lubricant impregnation.

9. An electronic device, characterized in that, The method includes a memory and a processor, wherein the memory stores executable code that, when executed by the processor, implements the method according to any one of claims 1 to 5.

10. A computer-readable storage medium, characterized in that, The device contains executable code that, when executed by a processor, implements the method described in any one of claims 1 to 5.

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