Sialic acid concentration detection method based on micro-cantilever sensor

Abstract

The invention relates to the technical field of biosensing, and particularly discloses a sialic acid (SA) concentration detection method based on a micro-cantilever sensor. Comprising the following steps: (1) preparing a monocrystalline silicon micro-cantilever sensor, and modifying 4-mercaptophenylboronic acid (4-MPBA) on the surface of a cantilever through an Au-S bond to serve as an SA acceptor molecule; (2) constructing an optical lever detection system, and monitoring the deformation of the cantilever beam in real time through laser reflection displacement; (3) enabling a sample to be detected to be in contact with the modified sensor, and triggering surface quality change of a cantilever beam by utilizing specific binding of 4-MPBA and SA, so as to cause resonant frequency deviation; and (4) establishing a relation model of the frequency value f and the SA concentration through a regression algorithm to realize quantitative detection of the SA concentration. Through a label-free dynamic detection mechanism, an enzyme labeling step required by a traditional ELISA method is avoided, and a rapid and low-cost solution is provided for cancer marker screening.

Description

Technical Field

[0001] This invention relates to a method for detecting sialic acid concentration, specifically a method for detecting sialic acid concentration based on a microcantilever beam sensor, belonging to the field of sialic acid concentration detection technology. Background Technology

[0002] In the medical field, "cancer" generally refers to malignant tumors originating from epithelial tissue, and it is the most common type of malignant tumor. Cancer is characterized by abnormal cell differentiation and proliferation, uncontrolled growth, invasiveness, and metastasis. Its development is complex, typically involving three stages: carcinogenesis, tumor promotion, and progression. The occurrence of cancer is closely related to various factors, such as smoking, infection, occupational exposure, environmental pollution, unhealthy diet, and genetic factors.

[0003] Sialic acid (SA) levels are often significantly elevated in the development and progression of various inflammatory diseases and malignant tumors, including lung cancer, gastric cancer, colorectal cancer, liver cancer, and ovarian cancer. Under normal circumstances, serum sialic acid concentration is stable, typically between 1.29 and 2.42 mmol / L. However, in patients with malignant tumors, due to significant changes in the structure and content of glycoproteins and glycolipids on the surface of tumor cells, large amounts of sialic acid may detach from the cell membrane and be released into the bloodstream, leading to elevated serum sialic acid concentrations. Furthermore, its level is usually positively correlated with the severity of the tumor. Therefore, the measurement of serum sialic acid levels has important clinical value in the auxiliary diagnosis, treatment monitoring, and prognostic assessment of tumors.

[0004] Currently, methods for detecting sialic acid mainly include liquid chromatography-mass spectrometry (LC-MS), electrochemical sensing, photoelectrochemical sensing, fluorescence detection, and colorimetry. While these methods possess certain sensitivity and selectivity, they generally suffer from high costs, complex operations, or strong equipment dependence, limiting their widespread application in rapid clinical diagnosis.

[0005] For example, Ji Suna et al. (Modern Biomedical Progress, 2017) established a method for detecting sialic acid in porcine visceral and muscle tissue based on liquid chromatography-mass spectrometry (LC-MS / MS). The average repeatability RSD was 1.2%, the stability RSD was 1.9%, the intra-day and inter-day precision RSDs were both less than 6.7%, and the average recovery rate was 92.9%–106.4%. This method has high sensitivity and good stability, but the equipment is expensive, the operating cost is high, and it is easily affected by matrix effects, especially for the detection of highly hydrophilic compounds (such as sugars).

[0006] Talanta et al. (2023) developed an amperometric biosensor incorporating an oxidase-mimicking Co / 2Fe metal-organic framework (MOF) for the detection of sialic acid. This sensor achieves the catalytic conversion and detection of SA by immobilizing a Co / 2Fe MOF with N-acetylneuraminic acid aldolase (NANA aldolase) on a gold screen-printed electrode, exhibiting a linear detection range of 0.02–1.00 mmol / L and a detection limit of 0.026 mmol / L. While this method offers high accuracy, it requires sophisticated equipment and involves a relatively complex operational procedure.

[0007] Cheng Yuanyuan, Kong Rongmei, et al. (Chinese Chemical Letters, 2023) constructed a colorimetric-assisted photoelectrochemical (PEC) sensor based on two pairs of cis-diol groups in the SA molecule. This system utilizes a sandwich structure constructed from gold-modified Bi₂S₃ (AuNPs@Bi₂S₃) and Au@PCN-224 to achieve specific recognition of SA and dual-mode signal output. This method offers high sensitivity and good visualization, but the system construction is complex and costly.

[0008] Zhao Pengfei et al. (Talanta, 2023) proposed a ratiometric fluorescence capillary blot sensor based on a UiO-66-NH2 metal-organic framework. This sensor achieves quantitative detection of salivary acid (SA) through blue light (440 nm) quenching and red light (655 nm) response of the blot layer. It is suitable for small-volume samples (15 μL) of human serum and saliva, exhibiting a low detection limit and good sensitivity and selectivity. However, its preparation and operation procedures are relatively complex, and the detection process is time-consuming.

[0009] Zhang Jian et al. (Chemical Research & Application, 2023) established a paper-based chip-based method for sialic acid detection. This method synthesized 4-mercaptophenylboronic acid-modified gold nanoparticles (4-MPBA-AuNPs), utilizing their specific binding to sialic acid (SA) to induce a color change (red to blue). The concentration of SA in the range of 0.9–2.9 mmol / L was quantitatively detected by taking photos with a mobile phone and analyzing the red-blue channel ratio using Photoshop image analysis software. This method is rapid and accurate, but the chip fabrication process is complex and highly dependent on material quality and equipment.

[0010] In summary, existing SA detection technologies suffer from problems such as expensive equipment, difficulty in balancing sensitivity and stability, complex operation, and susceptibility to interference. To overcome these shortcomings, there is an urgent need to develop a new method for sialic acid detection that is simple in structure, stable and reliable, highly sensitive, and suitable for rapid detection.

[0011] Therefore, this invention is proposed. Summary of the Invention

[0012] The purpose of this invention is to provide a method for detecting sialic acid concentration based on a microcantilever beam sensor in order to solve the above-mentioned problems. This method can directly and quickly detect sialic acid, is easy to operate, and has high accuracy.

[0013] This invention achieves the above objective through the following technical solution: a sialic acid concentration detection method based on a microcantilever beam sensor, comprising the following steps:

[0014] Step 1: Fabrication of the cantilever beam sensor. A micro cantilever beam sensor with gold plating on the beam surface is fabricated using MEMS technology with single-crystal silicon as the raw material.

[0015] Step 2: The acceptor molecule was modified on the cantilever beam by Au-S bond. The modified cantilever beam was washed with deionized water and stored at room temperature.

[0016] Step 3: Cantilever beam adsorption of target molecules. The interaction between the thiol group of 4-mercaptophenylboronic acid molecule and the gold substrate is utilized. The micro cantilever beam is treated with an ethanol solution containing 4-mercaptophenylboronic acid for 24 hours to modify the surface of the gold substrate with 4-mercaptophenylboronic acid, thus obtaining an adsorption SA sensitive layer. SA is adsorbed by the electrostatic attraction and other interactions between the phenylboronic acid modified on the gold substrate surface and SA.

[0017] Step 4: Set up the optical lever detection system;

[0018] Step 5: Dynamic detection by microcantilever sensor. The microcantilever after adsorbing target molecules is dynamically detected using the constructed optical lever system.

[0019] Step 6: Data processing. Statistically analyze the SA concentration and the corresponding resonant frequency of the microcantilever sensor, and analyze the relationship between SA concentration and the resonant frequency of the microcantilever sensor. Use a regression algorithm to obtain the relationship between SA concentration and the resonant frequency of the microcantilever sensor. Finally, determine the concentration of the measured SA solution by detecting the change in the resonant frequency of the microcantilever sensor caused by the sample.

[0020] Furthermore, in step 2, the repeatability, stability, and specificity of the microcantilever sensor are evaluated. At least three identical cantilever beams are set up, modified in the same way, and then tested with SA solutions of the same concentration. The changes in the resonant frequency of the cantilever beams are recorded, and the relative standard deviation is calculated based on these changes. Finally, the repeatability of the modified cantilever beams is discussed. Three surface-modified cantilever beam sensors were placed in temperature environments of 4℃, 20℃, and 30℃, respectively, and then tested again with SA solutions of the same concentration. The changes in the cantilever beam resonant frequency were recorded, and the relative standard deviation was calculated to obtain the stability of the modified cantilever beam. The three surface-modified cantilever beam sensors were placed at room temperature (20℃) for 3 days, 7 days, and 10 days, and then tested with SA solutions of the same concentration. The changes in the cantilever beam resonant frequency were recorded, and the relative standard deviation was calculated to discuss the timeliness of the modified cantilever beam. Small molecule glucose coexisting in serum samples was selected to evaluate the specificity of the sensor. The specificity of the prepared microcantilever beam sensor for SA detection was discussed by comparing the mass change of the cantilever beam under the same concentration of glucose solution and sialic acid solution.

[0021] Further, the specific method for step 3 is as follows: The SA solution is prepared by dissolving solid SA in 10 mL of Tris-HCl buffer (pH = 7.40), and then diluting it to multiple concentration gradients; the modified cantilever beam is immersed in the SA solutions of different concentrations under neutral pH conditions, and allowed to stand at room temperature for a certain period of time to achieve adsorption. The concentration range of the SA solution is 0.5 mmol / L to 3 mmol / L, with a concentration gradient of 0.25 mmol / L. The concentration range includes the concentration of SA in serum under normal conditions.

[0022] Furthermore, in step 4, the optical lever detection system includes:

[0023] Image display module: Composed of a CCD camera and a lens, it amplifies the tiny deflection of the free end of the microcantilever beam to the movement of the light spot position on the photodetector through beam reflection;

[0024] Optical inspection module: Composed of laser, photodetector and reflector, it observes the morphology of micro cantilever beam in real time through camera;

[0025] Dynamic excitation module: including clamping mechanism, piezoelectric ceramic and driving power supply, used for external driving of micro cantilever beam in dynamic mode, including clamping mechanism of micro cantilever beam, fixation of piezoelectric ceramic and output of driving signal;

[0026] Signal processing module: includes photodetector, processing circuit, data acquisition card and host computer program, used to complete the subsequent processing of measurement signals, convert optical signals into electrical signals and amplify them, and use data acquisition card to send electrical signals to host computer software for data analysis and processing.

[0027] Furthermore, in step 5, when detecting the dynamic frequency of the micro cantilever beam, a piezoelectric ceramic needs to be fixed between the micro cantilever beam clamping mechanism and the precision micro-motion stage. The wiring of the piezoelectric ceramic is connected to the driving power supply, which includes a signal generator and a power amplifier. The output parameters of the signal generator and the voltage output range of the power amplifier are set.

[0028] Furthermore, the calculation of SA concentration in step 6 is achieved through a machine learning regression model, including the following sub-steps:

[0029] a) Data preprocessing: The resonant frequency f of the microcantilever beam is normalized to eliminate environmental noise interference;

[0030] b) Feature extraction: Extracting time-domain and frequency-domain features from the dynamic response signal;

[0031] c) Model training: Using a sample set of SA with known concentrations, with the resonance frequency f and its derived features as inputs and the SA concentration as output, train the regression model;

[0032] d) Concentration prediction: Input the real-time detected f data into the trained regression model and output the SA concentration prediction value.

[0033] The technical effects and advantages of this invention are as follows: This invention achieves dynamic detection of a cantilever beam adsorbing sialic acid using an optical lever detection system. Data processing yields a fitted curve showing the mass change of the cantilever beam after adsorbing the target molecule in dynamic mode and the concentration of the target molecule in the solution. This allows for the detection of the target molecule concentration within a certain range. This method can be used to detect sialic acid concentration to assist in monitoring whether the sialic acid content in the sample exceeds the standard for serum sialic acid (1.29-2.42 M / L). Measuring serum sialic acid content has significant clinical value for the auxiliary diagnosis, efficacy observation, and prognosis of malignant tumors. Attached Figure Description

[0034] Figure 1 This is a schematic diagram of the self-assembly of the organic sulfide on the gold surface according to the present invention;

[0035] Figure 2 This is a schematic diagram of the reaction between sialic acid and phenylboronic acid according to the present invention;

[0036] Figure 3 This is a block diagram illustrating the principle of the optical lever detection system of the present invention;

[0037] Figure 4 This is the basic flowchart of the present invention;

[0038] Figure 5 This is a curve showing the fitting of the resonant frequencies at different concentrations according to the present invention;

[0039] Figure 6 This is a repeatability test curve for the present invention. Detailed Implementation

[0040] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0041] Example 1: Please refer to Figures 1-4 As shown, the sialic acid concentration detection method based on a microcantilever beam sensor includes the following steps:

[0042] Step 1: Fabrication of the micro cantilever beam sensor. The specific fabrication process is as follows:

[0043] (1) Substrate selection and pretreatment

[0044] A 300nm SiO2 insulating layer was prepared by double-sided polished (100) crystal orientation monocrystalline silicon wafer (thickness 300μm), RCA standard cleaning to remove organic / metal contaminants, and dry oxygen oxidation (temperature 1050℃, time 2h).

[0045] (2) Definition of cantilever beam structure

[0046] A 3 μm thick photoresist AZ4620 was spin-coated onto the substrate surface, followed by UV exposure to form a cantilever mask. Development (AZ400K:H₂O = 1:4) was then performed to obtain precise linewidths. After photolithography, deep silicon etching (DRIE) was performed using a Bosch process with alternating etching / passivation cycles. The cantilever thickness was controlled by the etching time. Finally, residual SiO₂ was etched using buffered hydrofluoric acid (BHF, HF:NH₄F = 1:7).

[0047] (3) Metallized coating

[0048] A 20-50 nm chromium layer is sputtered by magnetron sputtering to enhance the adhesion between the gold layer and the silicon substrate, followed by sputtering of a 100 nm gold layer.

[0049] (4) Back-side release structure

[0050] AZ4620 photoresist (8μm thickness) was spin-coated on the back side, aligned with the surface structure, and a back-side photolithography window was created. Anisotropic etching with KOH solution (40wt% concentration, 60℃) was then performed, terminating at the SiO2 stop layer on the front side, yielding an 800×90μm area. 2 Free end suspended structure.

[0051] The obtained gold-plated microcantilever sensor was treated with an ethanol solution (1 mol / L) containing 4-mercaptophenylboronic acid for 24 hours on a gold substrate cantilever beam.

[0052] Step 2: The cantilever beam was modified with acceptor molecules via Au-S bonds on the gold substrate. The modified cantilever beam was then washed with deionized water and stored at room temperature.

[0053] In actual experiments, to ensure data accuracy, it is necessary to modify three identical cantilever beams in the same way and then test them with SA solutions of the same concentration. The changes in the mass of the cantilever beams are recorded, and the relative standard deviation is calculated. The repeatability of the modified cantilever beams is discussed. The three prepared cantilever beam sensors are placed at low temperature (4℃) and room temperature (20℃ and 30℃), and the same concentration of SA solutions is tested again. The changes in the mass of the cantilever beams are recorded, and the relative standard deviation is calculated to obtain the stability of the modified cantilever beams.

[0054] Three prepared cantilever beam sensors were placed at room temperature (20℃) for 3, 7, and 10 days, and detected with SA solutions of the same concentration. The mass change of the cantilever beams was recorded, the relative standard deviation was calculated, and the time-dependent performance of the modified cantilever beams was discussed. Considering the complexity of real-world samples, small-molecule glucose coexisting in serum samples was selected to evaluate the sensor's specificity. The specificity of the prepared microcantilever beam sensor for SA detection was discussed by comparing the mass changes of the cantilever beams in glucose and sialic acid solutions of the same concentration.

[0055] By utilizing gold-sulfur bonds, sulfur-containing compounds with functional groups at the tail end are attached to the gold surface. The target biomolecules are then attached directly or through the action of an activator (or coupling agent) using the functional groups at the tail end, forming a self-assembled monolayer membrane, thereby achieving stable binding between the target biomolecules and the substrate.

[0056] Self-assembled monolayers (SAMs) are thermodynamically stable monolayers formed on the surface of a substrate material by organic molecules in solution through physicochemical interactions. They are characterized by in-situ spontaneous formation, highly ordered arrangement, few defects, and strong bonding. The reason why stable self-assembled monolayers can be formed is as follows:

[0057] (1) Gold is a commonly used solid substrate. It has excellent properties such as very good stability, good thermal and electrical conductivity, and easy processing. Moreover, there is no oxide film on the surface of gold, making it suitable as a substrate for forming SAMS. Furthermore, people have a thorough understanding of gold substrates, and subsequent experimental results are easy to analyze.

[0058] (2) Thiol groups can form strong Au-S bonds with gold (bond energy 184 KJ / mol), with very few other groups competing for them. In addition, organosulfur compounds can spontaneously and stably arrange themselves densely on the Au surface, so that the formed monolayer has a certain directionality and orderly arrangement.

[0059] (3) The preparation of mercapto-gold SAMs is relatively easy and can be achieved in both gas and liquid environments. Generally, it only requires immersing the metal substrate in a dilute solution of an organosulfur compound (typically about 1 mol / L) for a period of time at room temperature, followed by thorough washing and drying. It is generally believed that the self-assembly of organosulfur compounds on the gold surface involves two processes, such as... Figure 1 As shown. First, thiol groups undergo Langmuir adsorption on the gold surface. This process is very rapid; within just a few minutes, thiol molecules can occupy most of the sites on the substrate surface. The second step is the crystallization process of the thiol, where the thiol chains undergo ordered adjustment to form a crystalline film. This process takes a relatively long time, generally several to tens of hours.

[0060] Step 3: Cantilever beam adsorption of target molecules. Utilizing the interaction between the thiol group of 4-mercaptophenylboronic acid and the gold substrate, 4-mercaptophenylboronic acid is modified onto the surface of the gold substrate to prepare an SA-sensitive adsorption layer, such as... Figure 2 The diagram shows the adsorption of SA by utilizing the electrostatic attraction and other interactions between phenylboronic acid modified on the gold substrate surface and SA.

[0061] Under neutral conditions (pH=7.4), boric acid groups and SA exhibit selective binding effects that differ from their binding with other sugars. Therefore, under neutral conditions, the boric acid groups are used to achieve specific adsorption of SA.

[0062] Specifically, solid SA was dissolved in 10 mL of Tris-HCl buffer (pH = 7.40), and then diluted to different concentrations for future use. The modified cantilever beam was then immersed in SA solutions of different concentrations under neutral conditions and left at room temperature for a period of time. A series of SA concentrations for adsorption were selected around the normal serum sialic acid concentration (1.29–2.42 mmol / L). The concentration gradient of the SA solution was set at 0.25 mmol / L, with an overall concentration range of 0.25 mmol / L to 3 mmol / L, covering the normal steady-state sialic acid concentration range in the human body.

[0063] Simultaneously, the modified cantilever beam sensor also needs to measure the upper and lower limits of its measurable sialic acid concentration. Specifically, the modified cantilever beam sensor is used to adsorb low concentrations of sialic acid. After adsorption for a period of time, if the resonant frequency of the cantilever beam does not change after the low concentration of sialic acid is adsorbed by the modified cantilever beam, then this concentration is the lower limit of the sialic acid concentration that the cantilever beam can detect within this adsorption time. Similarly, if the resonant frequency of the cantilever beam no longer changes after the high concentration of sialic acid is adsorbed by the modified cantilever beam, then this concentration is the upper limit of the sialic acid concentration within this adsorption time.

[0064] Step 4: Set up the optical lever detection system. (Optical lever detection system...) Figure 3 )include:

[0065] Image display module: Composed of a CCD camera and a lens, it amplifies the tiny deflection of the free end of the microcantilever beam to the movement of the light spot position on the photodetector through beam reflection;

[0066] Optical inspection module: Composed of laser, photodetector and reflector, it observes the morphology of micro cantilever beam in real time through camera;

[0067] Dynamic excitation module: including clamping mechanism, piezoelectric ceramic and driving power supply, used for external driving of micro cantilever beam in dynamic mode, including clamping mechanism of micro cantilever beam, fixation of piezoelectric ceramic and output of driving signal;

[0068] Signal processing module: includes photodetector, processing circuit, data acquisition card and host computer program, used to complete the subsequent processing of measurement signals, convert optical signals into electrical signals and amplify them, and use data acquisition card to send electrical signals to host computer software for data analysis and processing.

[0069] Step 5: Dynamic detection of the microcantilever beam sensor. The instrument used to collect dynamic data is a Butterworth low-pass filter. The built optical lever system is used to dynamically detect the microcantilever beam after adsorbing the target molecules. When detecting the dynamic frequency of the microcantilever beam, a piezoelectric ceramic needs to be fixed between the microcantilever beam clamping mechanism and the precision micro-motion stage. The wiring of the piezoelectric ceramic is connected to the driving power supply. The driving power supply includes a signal generator and a power amplifier. Set the output parameters of the signal generator and the voltage output range of the power amplifier.

[0070] The collected voltage value was compensated and the morphology of the micro cantilever beam was monitored in real time by a camera. The collected raw data was a waveform containing noise. The cutoff frequency of the low-pass filter was adjusted according to the sampling frequency. After noise reduction, the Fourier transform of the time-domain waveform was performed to obtain the spectrum curve, and the resonant frequency after the mass of the cantilever beam changed was determined. The mass change on the surface of the cantilever beam was then calculated.

[0071] The microcantilever beam operating in dynamic mode is essentially a mechanical oscillator. Its resonance characteristics are characterized by the added mass on the beam and the viscoelasticity of the medium. When the analyte comes into contact with the sensitive molecule, it will produce an adsorption effect. Its resonance behavior is observed by excitation with external signals (such as alternating electric field, sound field, electromagnetic field). When the suspended mass on the microcantilever beam increases, the mechanical properties of the beam change. As the mass increases, its resonance frequency will decrease. The mass of the adsorbate on the cantilever beam is calculated by detecting the offset value of the resonance frequency.

[0072] When detecting the dynamic frequency of a micro cantilever beam, a piezoelectric ceramic needs to be fixed between the micro cantilever beam clamping mechanism and the precision micro-motion stage. The piezoelectric ceramic is connected to the driving power supply, which includes a signal generator and a power amplifier. The output parameters of the signal generator and the voltage output range of the power amplifier are set. The acquired voltage value is compensated, and the morphology of the micro cantilever beam is monitored in real time by a camera. The acquired raw data is a waveform containing noise. The cutoff frequency of the low-pass filter is adjusted according to the sampling frequency. After noise reduction, a Fourier transform is performed on the time-domain waveform to obtain the spectrum curve, which reveals the resonant frequency after the mass of the cantilever beam changes. The mass change on the surface of the cantilever beam is then calculated.

[0073] In the following example, assuming the beam width is b, the beam's moment of inertia I is expressed as follows:

[0074]

[0075] Based on the beam's geometry, Hooke's law is applied to a rectangular cantilever beam with effective mass and elastic constants to establish a simplified model of a resonant cantilever beam sensor. Its stiffness is k. From F = k·Δx, we know that Δx is the deflection displacement Δz of the cantilever beam. Therefore, the elastic constants of the cantilever beam are:

[0076]

[0077] Where E is the Young's modulus of the beam, and the vibration frequency relationship of the micro-cantilever beam is: For a cantilever beam with a uniform rectangular cross-section, its effective mass m * =0.24m0, where m0 is the mass of the beam itself. When the mass Δm is added to the end, the resonant frequency of the beam is given by the following formula:

[0078]

[0079] The mass of the beam is m0 = ρv = ρbtL. Under no-load conditions, Δm = 0. Therefore, according to equations 2 and 3, the initial eigenfrequency under no-load conditions is:

[0080]

[0081] The relationship between the mass change of the lower cantilever beam and the resonant frequency is as follows:

[0082]

[0083] When performing SA concentration detection using microcantilever beams, the concentration can be detected by the resonant frequency, and the mass change can be characterized by detecting the change in the resonant frequency of the microcantilever beam.

[0084] Step 6: Data processing. Statistically analyze the SA concentration and the corresponding cantilever beam resonant frequency changes, and examine the relationship between SA concentration and cantilever beam resonant frequency changes. The SA concentration is calculated using a machine learning regression model, including the following sub-steps:

[0085] a) Data preprocessing: The resonant frequency f of the microcantilever beam is normalized to eliminate environmental noise interference;

[0086] b) Feature extraction: Extracting time-domain and frequency-domain features from the dynamic response signal;

[0087] c) Model training: Using a sample set of SA with known concentrations, with the resonance frequency f and its derived features as inputs and the SA concentration as output, train the regression model;

[0088] d) Concentration prediction: Input the real-time detected f data into the trained regression model and output the SA concentration prediction value.

[0089] Figure 5 To obtain the relationship curve between SA concentration and the resonant frequency of the cantilever beam sensor.

[0090] Figure 6 This is a set of repeatability test curves.

[0091] Example 2: Surface modification method for micro cantilever beam sensors, including the following steps:

[0092] Step 1: Fabrication of the micro cantilever beam sensor. The specific fabrication process is as follows:

[0093] (1) Substrate selection and pretreatment

[0094] A 300nm SiO2 insulating layer was prepared by double-sided polished (100) crystal orientation monocrystalline silicon wafer (thickness 300μm), RCA standard cleaning to remove organic / metal contaminants, and dry oxygen oxidation (temperature 1050℃, time 2h).

[0095] (2) Definition of cantilever beam structure

[0096] A 3 μm thick photoresist AZ4620 was spin-coated onto the substrate surface, followed by UV exposure to form a cantilever mask. Development (AZ400K:H₂O = 1:4) was then performed to obtain precise linewidths. After photolithography, deep silicon etching (DRIE) was performed using a Bosch process with alternating etching / passivation cycles. The cantilever thickness was controlled by the etching time. Finally, residual SiO₂ was etched using buffered hydrofluoric acid (BHF, HF:NH₄F = 1:7).

[0097] (3) Back-end release structure

[0098] AZ4620 photoresist (8μm thickness) was spin-coated on the back side, aligned with the surface structure, and a back-side photolithography window was created. Anisotropic etching with KOH solution (40wt% concentration, 60℃) was then performed, terminating at the SiO2 stop layer on the front side, yielding an 800×90μm area. 2 Free end suspended structure.

[0099] The surface modification process for the micro cantilever beam sensor without a metal coating is as follows: A piranha solution is prepared: concentrated sulfuric acid and hydrogen peroxide solution are mixed in a 7:3 volume ratio. The cantilever beam is immersed in the piranha solution for 12 hours, then rinsed with plenty of deionized water and dried to achieve silicon surface hydroxylation. A silane coupling agent and an appropriate amount of ammonia are added to the deionized water to make the pH of the mixed solution greater than 8. The cantilever beam prepared in the previous step is placed in this solution and dried in an oven for 12 hours (40℃). After 12 hours, it is rinsed again with plenty of deionized water and then dried to obtain a cantilever beam with epoxy group modification, achieving hydroxyl dehydration condensation on the cantilever beam. The obtained cantilever beam is then immersed in a solution of 3-aminophenylboronic acid and ethanol for 5-6 hours, rinsed with deionized water, and dried to obtain a cantilever beam sensor with boric acid ligand modification.

[0100] The other steps are the same as in Example 1.

[0101] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the invention can be implemented in other specific forms without departing from its spirit or essential characteristics. Therefore, the embodiments should be considered in all respects as exemplary and non-limiting, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of equivalents of the claims are intended to be included within the present invention. No reference numerals in the claims should be construed as limiting the scope of the claims.

[0102] Furthermore, it should be understood that although this specification describes embodiments, not every embodiment contains only one independent technical solution. This narrative style is merely for clarity. Those skilled in the art should consider the specification as a whole, and the technical solutions in each embodiment can also be appropriately combined to form other embodiments that can be understood by those skilled in the art.

Claims

1. A method for detecting sialic acid concentration based on a microcantilever sensor, characterized by, The method comprises the following steps: Step 1: Fabrication of cantilever beam sensor, using MEMS technology to fabricate a micro cantilever beam sensor with a gold-plated beam surface from single crystal silicon as raw material; Step 2: Modification of receptor molecules on the cantilever beam, modification of the micro cantilever beam surface through Au-S bond, washing of the obtained modified cantilever beam with deionized water, and storage at room temperature; Step 3: Cantilever beam adsorbs target molecules, using the thiol group of 4-mercapto phenylboronic acid molecules to react with the gold substrate, treating the micro cantilever beam with 4-mercapto phenylboronic acid ethanol solution for 24 hours, modifying 4-mercapto phenylboronic acid on the gold substrate surface, and preparing an SA adsorption sensitive layer, using the electrostatic attraction between the modified gold substrate surface phenylboronic acid and SA to adsorb SA; Step 4: Build a light lever detection system; Step 5: Dynamic detection of micro cantilever beam sensor, using the built light lever system to dynamically detect the micro cantilever beam after adsorbing target molecules; Step 6: Data processing, statistics of SA concentration and corresponding micro cantilever beam sensor resonance frequency, analysis of the relationship between SA concentration and micro cantilever beam sensor resonance frequency, adoption of regression algorithm to obtain the relationship between SA concentration and micro cantilever beam sensor resonance frequency, and finally detection of sample to make the micro cantilever beam sensor resonance frequency change to know the measured SA solution concentration.

2. The microcantilever sensor-based sialic acid concentration detection method according to claim 1, characterized by: In step 2, the repeatability, stability and specificity of the micro cantilever beam sensor are detected. At least three same cantilever beams are set, the same modification is performed on the same cantilever beams, then the same concentration of SA solution is detected, then the resonance frequency change of the cantilever beam is recorded, the relative standard deviation is calculated based on the resonance frequency change of the cantilever beam, and finally the repeatability of the modified cantilever beam is discussed. The three surface-modified cantilever beam sensors are placed in temperature environments of 4℃, 20℃ and 30℃, then the same concentration of SA solution is detected again, the resonance frequency change of the cantilever beam is recorded, the relative standard deviation is calculated, and the stability of the modified cantilever beam is obtained. The three surface-modified cantilever beam sensors are placed at room temperature of 20℃ for 3 days, 7 days and 10 days, then the same concentration of SA solution is detected, the resonance frequency change of the cantilever beam is recorded, the relative standard deviation is calculated, the timeliness of the modified cantilever beam is discussed, the specificity of the sensor is evaluated by selecting the coexisting small molecule glucose in the serum sample, and the specificity of the prepared micro cantilever beam sensor for SA detection is discussed by comparing the mass change of the cantilever beam in the same concentration of glucose solution and sialic acid solution.

3. The microcantilever sensor-based sialic acid concentration detection method according to claim 1, characterized by: The specific method of step 3 is: the preparation method of the SA solution is: 0.1 mmol of SA is dissolved in 10 mL of Tris-HCl buffer solution with pH=7.40, and then diluted into multiple concentration gradients; the modified cantilever beam is soaked in the SA solution with different concentrations under the condition of neutral pH, and is placed at room temperature for a certain time to realize adsorption. The concentration range of the SA solution is 0.5 mM to 3 mM, and the concentration gradient is 0.25 mM, and the concentration range includes the concentration of SA in the serum under normal circumstances.

4. The microcantilever sensor-based sialic acid concentration detection method according to claim 1, characterized by: In step 4, the light lever detection system comprises: Image display module: composed of CCD camera and lens, through beam reflection to amplify the micro deflection of the free end of the micro cantilever to the movement of the light spot position on the photoelectric detector; Optical detection module: composed of laser, photoelectric detector and mirror, the morphology of the micro cantilever is observed in real time through the camera; Dynamic excitation module: including clamping mechanism, piezoelectric ceramic and driving power supply, used for external driving of micro cantilever in dynamic mode, including clamping mechanism of micro cantilever, fixation of piezoelectric ceramic and output of driving signal; Signal processing module: including photoelectric detector, processing circuit, data acquisition card and host computer program, used for subsequent processing of measurement signal, converting optical signal to electrical signal and amplifying operation, using data acquisition card to send electrical signal to host computer software for data analysis and processing.

5. The microcantilever sensor-based sialic acid concentration detection method according to claim 4, characterized by: In step 5, when detecting the dynamic frequency of the micro cantilever, the piezoelectric ceramic is fixed between the micro cantilever clamping mechanism and the precision micro motion stage, the wiring of the piezoelectric ceramic is connected with the driving power supply, the driving power supply includes signal generator and power amplifier, and the output parameters of the signal generator and the voltage output range of the power amplifier are set.

6. The method of claim 1, wherein: The calculation of SA concentration in step 6 is realized by machine learning regression model, including the following sub steps: a) data preprocessing: normalizing the resonance frequency f of the micro cantilever to eliminate environmental noise interference; b) feature extraction: extracting time domain features and frequency domain features from dynamic response signal; c) model training: using known concentration of SA sample set, taking resonance frequency f and its derived features as input and SA concentration as output to train regression model; d) concentration prediction: inputting the real-time detected f data into the trained regression model to output the predicted value of SA concentration.

7. The microcantilever sensor-based sialic acid concentration detection method according to claim 1, wherein: The specific instrument for collecting dynamic data is Butterworth low-pass filter.

Images