Intelligent decision-making method for carbon nanosheet precursor ratio based on machine learning

Abstract

The invention relates to the technical field of carbon nanosheet preparation, and discloses a carbon nanosheet precursor ratio intelligent decision-making method based on machine learning. Data collection: collecting data samples of different mass ratios, corresponding preparation parameters and performance indexes of the carbon nanosheets of the citric acid and the urea in the process of preparing the carbon nanosheets by the microwave-assisted method; 2, data preprocessing: preprocessing the collected data samples, including data cleaning, normalization and feature selection, so as to improve data quality and applicability; according to the intelligent decision-making method for the carbon nanosheet precursor proportion based on machine learning, the proportion of citric acid and urea is accurately regulated and controlled through a machine learning model based on historical data and real-time feedback, so that the structure and performance of the carbon nanosheet are better, and the detection sensitivity of an electrochemical sensor to substances such as heavy metal ions Cu is improved; the detection limit is reduced, the anti-interference capability is enhanced, and stable and accurate detection in a complex water environment is ensured.

Description

Technical Field

[0001] This invention relates to the field of carbon nanosheet preparation technology, and in particular to a machine learning-based intelligent decision-making method for the proportion of carbon nanosheet precursors. Background Technology

[0002] With the continuous advancement of technology, electrochemical sensors are being used more and more widely in many fields, and the performance requirements for electrode materials are also increasing. Carbon nanosheets, as a highly promising electrode material, have a large specific surface area, excellent conductivity, and high electron mobility, which can significantly improve the detection sensitivity and performance of electrochemical sensors. However, traditional carbon nanosheet preparation methods have many drawbacks. Not only are the yields and efficiency low, but the reaction and purification process usually takes several days. Moreover, the reaction, cleaning, and purification processes consume a large amount of non-recyclable organic solvents, causing environmental pollution and seriously restricting the large-scale practical application of carbon nanosheets.

[0003] To address these issues, microwave-assisted carbon nanosheet preparation has emerged. By adding a certain ratio of reaction precursors, the yield of carbon nanosheets has been effectively improved, simplifying the preparation process, reducing costs, and increasing yield, thus enabling the mass production of carbon nanosheets. However, in the current microwave-assisted carbon nanosheet preparation process, the ratio of the precursors citric acid and urea still mainly relies on manual experience for control. This process is not only time-consuming and labor-intensive, but also difficult to achieve precise optimization, failing to fully leverage the advantages of the microwave method and limiting further improvements in the performance and production efficiency of carbon nanosheets. Summary of the Invention

[0004] Given that in the existing microwave method for preparing carbon nanosheets, the ratio of the precursor citric acid and urea still mainly relies on manual experience for control, this process is not only time-consuming and labor-intensive, but also difficult to achieve precise optimization, thus failing to fully leverage the advantages of the microwave method and limiting further improvements in the performance and production efficiency of carbon nanosheets, this invention is proposed.

[0005] Therefore, the purpose of this invention is to provide an intelligent decision-making method for the proportioning of carbon nanosheet precursors based on machine learning. The aim is to: mine potential patterns from a large amount of data by constructing a model to achieve intelligent decision-making and optimization of complex systems; introduce machine learning into the regulation of the proportioning of carbon nanosheet precursors, which can overcome the limitations of traditional manual regulation, achieve rapid and accurate proportioning optimization, further improve the preparation efficiency and quality of carbon nanosheets, reduce production costs, and promote the wider application of carbon nanosheets in fields such as electrochemical sensors.

[0006] To address the aforementioned technical problems, this invention provides the following technical solution: a machine learning-based intelligent decision-making method for carbon nanosheet precursor ratios, comprising the following steps: Step 1, Data Collection: Collect data samples of different mass ratios of citric acid and urea in the microwave-assisted preparation of carbon nanosheets, the corresponding preparation parameters, and the performance indicators of carbon nanosheets. Step 2, Data Preprocessing: The collected data samples are preprocessed, including data cleaning, normalization, and feature selection, to improve data quality and applicability; Step 3, Model Building: A predictive model is built using machine learning algorithms to establish a quantitative relationship between the mass ratio of citric acid and urea and the performance of carbon nanosheets. The preprocessed data samples are then used to train the constructed predictive model. The model is validated and optimized through methods such as cross-validation and hyperparameter tuning to improve the model's predictive accuracy and generalization ability. Step 4, ratio optimization: Based on the target performance index required for actual preparation, input the index into the optimized prediction model. Through model calculation and analysis, output the optimal mass ratio of citric acid and urea to achieve the target performance. According to the optimal mass ratio, accurately weigh citric acid and urea, dissolve them in an appropriate amount of deionized water, and stir thoroughly to form a uniform colorless and transparent solution. Step 5, ultrasonic treatment: The prepared solution is subjected to high-frequency ultrasonic treatment for 60-120 minutes to ensure that the components in the solution are fully mixed and form a stable dispersion system; Step 6, Microwave heating: Place the ultrasonically treated solution in a microwave oven, set the microwave power to 700-800W, and the heating time to 5-10 minutes, so that the solution gradually changes from colorless to light yellow solid. During this process, microwave energy promotes the precursor to undergo a chemical reaction to form carbon nanosheet precursor. Step 7, Grinding treatment: Grind the obtained light yellow solid into a fine powder to obtain carbon nanosheet material. Step 8, Electrode preparation: The prepared carbon nanosheet material is uniformly loaded onto the surface of a glassy carbon electrode. Through a specific coating or adhesion process, it is firmly bonded to the electrode, thereby creating a carbon nanosheet electrode material for electrochemical sensors to improve the performance of electrochemical trace detection of heavy metal ions Cu²⁺.

[0007] As a preferred embodiment of the present invention, the machine learning algorithm adopts an algorithm that simulates the structure and function of the human brain's neural network. It has a strong nonlinear fitting and feature learning ability, and can automatically discover the complex relationship between the ratio of citric acid and urea and the performance of carbon nanosheets. Through the transmission and calculation of a multi-layer neural network, it can achieve accurate prediction of the performance of unknown ratios.

[0008] As a preferred embodiment of the present invention, the performance indicators of the carbon nanosheets include specific surface area, conductivity, electrocatalytic activity, detection sensitivity and detection limit for heavy metal ions Cu²⁺.

[0009] In a preferred embodiment of the present invention, the preparation parameters further include solvent amount, reaction temperature and reaction time.

[0010] As a preferred embodiment of the present invention, it further includes a step of real-time updating and optimizing the prediction model, thereby improving the accuracy and adaptability of the model by continuously adding new preparation data samples.

[0011] As a preferred embodiment of the present invention, it includes a data acquisition module, a data processing module, a model building module, a model training module, a ratio decision module, a preparation execution module, and an electrode preparation module.

[0012] In a preferred embodiment of the present invention, the data acquisition module further includes a data storage unit for storing acquired historical data samples to provide data support for model training.

[0013] As a preferred embodiment of the present invention, it further includes a feedback adjustment module for adjusting and optimizing the prediction model based on the deviation between the actual performance of the prepared carbon nanosheets and the predicted performance.

[0014] As a preferred embodiment of the present invention, carbon nanosheets obtained by optimizing the ratio through machine learning are loaded onto a glassy carbon electrode to be used for the detection of heavy metal ions such as Cu²⁺ by an electrochemical sensor, exhibiting high sensitivity, low detection limit and good selectivity.

[0015] As a preferred embodiment of the present invention, it can be used for trace detection of heavy metal ions Cu²⁺ in environmental water samples, biological samples, and other fields, achieving rapid, accurate, and stable detection results, and promoting the widespread application of electrochemical sensors in environmental monitoring, biomedicine, and other fields.

[0016] Compared with the prior art, the present invention has at least the following beneficial effects: 1. This invention uses a machine learning model to precisely control the ratio of citric acid and urea based on historical data and real-time feedback, thereby optimizing the structure and performance of carbon nanosheets. This improves the detection sensitivity of electrochemical sensors for substances such as heavy metal ions Cu²⁺, reduces the detection limit, enhances anti-interference capabilities, and ensures stable and accurate detection in complex aquatic environments.

[0017] 2. This invention changes the traditional method of determining the ratio through repeated manual experiments. Instead, it uses machine learning algorithms to quickly analyze data and provide the optimal ratio scheme, which greatly shortens the precursor ratio optimization time, improves the carbon nanosheet preparation efficiency, accelerates product development and production cycles, and enables enterprises to respond quickly to market demands and seize market opportunities.

[0018] 3. This invention reduces the waste of raw materials and energy consumption caused by manual trial and error, while reducing labor costs, optimizing resource allocation, realizing the economical and efficient operation of the carbon nanosheet preparation process, improving the economic benefits and market competitiveness of enterprises, and promoting the large-scale industrial application of carbon nanosheets. Attached Figure Description

[0019] Figure 1 This is a TEM surface morphology observation image of the intelligent decision-making method for carbon nanosheet precursor ratio based on machine learning, as presented in this invention. Figure 2 This invention relates to a machine learning-based intelligent decision-making method for the proportioning of carbon nanosheet precursors in the detection of Cu. 2+ Anodic stripping differential pulse voltammetry; Figure 3 Other metal ions in Cu based on machine learning are used in the intelligent decision-making method for carbon nanosheet precursor ratios according to this invention. 2+ Interference test diagram; Figure 4 This is a schematic diagram of the overall process of the intelligent decision-making method for carbon nanosheet precursor ratio based on machine learning, as described in this invention. Detailed Implementation

[0020] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0021] Reference Figures 1-4 As an embodiment of the present invention, a machine learning-based intelligent decision-making method for the proportion of carbon nanosheet precursors is provided. This machine learning-based intelligent decision-making method for the proportion of carbon nanosheet precursors includes the following steps: Step 1, Data Collection Multi-dimensional data collection: Data samples were collected on different mass ratios of citric acid and urea in the microwave-assisted preparation of carbon nanosheets, the corresponding preparation parameters, and the performance indicators of the carbon nanosheets. Simultaneously, environmental factor data, such as reaction temperature and humidity, and equipment parameter data, such as microwave oven model and power stability, were collected to comprehensively evaluate their impact on the performance of the carbon nanosheets.

[0022] Detailed performance indicators: The performance indicators of carbon nanosheets include specific surface area, conductivity, electrocatalytic activity, detection sensitivity and detection limit for heavy metal ions (such as Cu²⁺, Pb²⁺, Cd²⁺), and stability under different acid and alkaline environments. Step 2, Data Preprocessing Data cleaning and denoising: The collected data samples are preprocessed, including data cleaning, removal of outliers and noise, to improve data quality. Wavelet transform or filtering algorithms are used to remove high-frequency noise from the experimental data.

[0023] Normalization and standardization: Data is normalized and feature selected to normalize data of different dimensions to the [0,1] or [-1,1] interval, eliminating the influence of dimensions. Principal component analysis (PCA) and other algorithms are used to extract key features, reduce data dimensionality, and improve model training efficiency.

[0024] Feature engineering enhancement: Based on domain knowledge, design new feature variables, such as the molar ratio of citric acid to urea and the initial pH value of the reactant solution, to enhance the model's ability to capture data features; Step 3, Model Building Multi-algorithm fusion modeling: A predictive model is constructed using machine learning algorithms to establish a quantitative relationship between the mass ratio of citric acid and urea and the performance of carbon nanosheets. An ensemble learning model is built by combining multiple machine learning algorithms, such as neural networks, support vector machines, and random forests.

[0025] Model training and optimization details: The constructed prediction model is trained using preprocessed data samples. The model is validated and optimized through methods such as cross-validation and hyperparameter tuning to improve the model's prediction accuracy and generalization ability. In cross-validation, k-fold cross-validation (e.g., k=5 or k=10) is used, dividing the dataset into k subsets. K-1 subsets are used alternately as the training set and 1 subset is used as the validation set to evaluate the model's stability and accuracy.

[0026] Hyperparameter optimization strategies: Utilize methods such as grid search, random search, or Bayesian optimization to systematically optimize the model's hyperparameters (such as the number of hidden layer neurons and learning rate in neural networks, and kernel function parameters in support vector machines) to find the optimal combination of hyperparameters and improve model performance. Step 4, Optimize the proportions Performance-oriented ratio optimization: Based on the target performance index required for actual preparation, the index is input into the optimized prediction model. Through model calculation and analysis, the optimal mass ratio of citric acid and urea to achieve the target performance is output.

[0027] Accurate weighing and solution preparation: According to the optimal mass ratio, use a high-precision electronic balance to accurately weigh citric acid and urea, dissolve them in an appropriate amount of deionized water, and stir thoroughly to form a uniform, colorless, and transparent solution; accurately control the concentration and volume of the solution to ensure the consistency and repeatability of experimental conditions.

[0028] Enhanced solution stability: During solution preparation, adding appropriate amounts of surfactants or stabilizers can improve solution stability, prevent precursor substances from agglomerating or precipitating in the solution, and thus ensure the uniformity and stability of subsequent reactions. Step 5, Ultrasonic treatment Ultrasonic treatment parameter optimization: The prepared solution is subjected to high-frequency ultrasonic treatment for 60-120 minutes to ensure that the components in the solution are fully mixed and form a stable dispersion system. At the same time, the ultrasonic power and frequency are optimized. According to the viscosity and volume of the solution, the appropriate ultrasonic power (e.g., 100-300W) and frequency (e.g., 20-40kHz) are selected to improve the ultrasonic treatment effect.

[0029] Real-time monitoring of ultrasonic treatment: During ultrasonic treatment, online monitoring technology (such as ultrasonic sensors, laser particle size analyzers, etc.) is used to monitor the dispersion state and particle size distribution of the solution in real time, and the ultrasonic treatment parameters are adjusted in a timely manner to ensure that the solution achieves the ideal dispersion effect. Step 6, Microwave Heating Control Precise microwave heating control: Place the ultrasonically treated solution in a microwave oven, set the microwave power to 700-800W, and the heating time to 5-10 minutes, so that the solution gradually changes from colorless to a light yellow solid.

[0030] Reaction process monitoring and feedback: During microwave heating, temperature and optical sensors are installed to monitor the temperature and color changes of the solution in real time. A machine learning-based reaction process monitoring model is constructed to predict the reaction progress and the formation of carbon nanosheets.

[0031] Intelligent control strategy: Based on monitoring data, the microwave power and heating time are dynamically adjusted through intelligent control algorithms such as fuzzy control or PID control to ensure the stability and consistency of the reaction process and improve the quality and yield of carbon nanosheets; Step 7, Refining the grinding process Grinding process optimization: The obtained light yellow solid was ground into a fine powder, thus obtaining carbon nanosheet material.

[0032] Particle size control and classification: Carbon nanosheet materials are finely ground using grinding media (such as ball mill beads) and grinding time of different particle sizes to control their particle size distribution. The ground powder is then classified by sieving or sedimentation methods to select carbon nanosheet materials with suitable particle size ranges for electrode fabrication, thereby improving the electrode's performance and stability. Step 8: Electrode fabrication and performance enhancement Improved electrode fabrication process: The prepared carbon nanosheet material is uniformly loaded onto the surface of the glassy carbon electrode, and then firmly bonded to the electrode through a specific coating or adhesion process.

[0033] Interface modification enhancement: Surface modification of carbon nanosheet materials, such as introducing functional groups or composite with other nanomaterials, can enhance their interaction and interfacial bonding with glassy carbon electrodes.

[0034] Electrochemical performance optimization: The electrochemical performance of the prepared carbon nanosheet electrode material was tested and optimized. Cyclic voltammetry, electrochemical impedance spectroscopy and other techniques were used to evaluate its detection performance for heavy metal ions.

[0035] The machine learning algorithm described employs an algorithm that simulates the structure and function of the human brain's neural network. It possesses powerful nonlinear fitting and feature learning capabilities, and can automatically discover the complex relationship between the ratio of citric acid and urea and the performance of carbon nanosheets. Through the transmission and calculation of a multi-layer neural network, it can achieve accurate prediction of the performance of unknown ratios.

[0036] The performance indicators of the carbon nanosheets include specific surface area, conductivity, electrocatalytic activity, detection sensitivity and detection limit for heavy metal ions Cu²⁺.

[0037] The preparation parameters also include solvent volume, reaction temperature, and reaction time.

[0038] It also includes steps for real-time updating and optimization of the prediction model, improving the model's accuracy and adaptability by continuously adding new preparation data samples.

[0039] Includes a data acquisition module: used to collect data on the ratio of citric acid and urea, preparation parameters, and the properties of carbon nanosheets during the microwave-assisted preparation of carbon nanosheets; Data processing module: performs preprocessing operations on the collected data; Model building module: Builds predictive models based on selected machine learning algorithms; Model training module: Uses preprocessed data to train, validate, and optimize the prediction model; Component ratio decision module: Based on the target performance indicators, the optimal ratio of citric acid and urea is given using the optimized prediction model; Preparation execution module: Dissolve citric acid and urea according to the optimal ratio, perform ultrasonic treatment, microwave heating and grinding operations to obtain carbon nanosheet materials; Electrode fabrication module: Carbon nanosheet materials are loaded onto glassy carbon electrodes to fabricate electrode materials.

[0040] The data acquisition module also includes a data storage unit for storing collected historical data samples to provide data support for model training.

[0041] It also includes a feedback adjustment module, which is used to adjust and optimize the prediction model based on the deviation between the actual carbon nanosheet performance and the predicted performance.

[0042] Carbon nanosheet electrode materials are fabricated by loading carbon nanosheets, which are obtained by optimizing the ratio through machine learning, onto glassy carbon electrodes. These materials are used in electrochemical sensors to detect heavy metal ions such as Cu²⁺, exhibiting high sensitivity, low detection limit, and good selectivity.

[0043] Electrochemical sensors can be used for trace detection of heavy metal ions Cu²⁺ in environmental water samples, biological samples, and other fields, achieving rapid, accurate, and stable detection results, and promoting the widespread application of electrochemical sensors in environmental monitoring, biomedicine, and other fields.

[0044] Experimental results 1. Characterization of carbon nanosheets Observed by transmission electron microscopy (TEM) Figure 1 As can be seen, the carbon nanosheets have irregular shapes and sizes ranging from 30 to 100 nm. 2. Trace detection of heavy metal ions Cu using carbon nanosheet electrodes 2+ performance The heavy metal ions Cu were disposed of using an acetate buffer solution with a pH of 5. 2+ Dilute to different concentrations for testing. Figure 2 From 1 mg / L -1 Up to 100mg / L -1 Different concentrations of heavy metal ions Cu 2+ Anodic dissolution differential pulse voltammogram of the solution. Clearly, with the increase of heavy metal ions Cu... 2+ The increase in concentration leads to an increase in oxidation peak current, indicating that the peak current is affected by low concentrations of heavy metal ions Cu. 2 + The response is sensitive. The peak oxidation current value is fitted to the heavy metal ion Cu. 2+ The linear relationship between concentrations, where the correlation coefficient (R) 2 The signal-to-noise ratio (SNR) is 0.997. Based on a three-fold SNR, the calculated detection limit is 0.27 mg / L. -1The excellent detection capability is due to the numerous reaction sites and superior electrocatalytic activity of carbon nanosheet materials. Therefore, carbon nanosheet electrodes are a promising electrode material for constructing high-performance electrochemical sensors.

[0045] 3. Selectivity Selectivity indicates the electrode's resistance to interference in complex aquatic environments. (The text then abruptly shifts to a seemingly unrelated topic: Cd...) 2+ Zn 2+ Ca 2+ Pb 2+ Mg 2+ Na + Al 3+ Fe 3+ Interfering ions were added to Cu 2+ In standard solutions; such as Figure 3 As shown, when Cd is added 2+ Zn 2+ Ca 2+ Pb 2+ Mg 2+ Na + Al 3+ Fe 3+ At that time, Cu 2+ The signal changes slightly, with a relative response deviation between -1.9% and 2.8%, which means that the carbon nanosheet electrode has good anti-interference performance against the above eight ions.

[0046] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A machine learning-based intelligent decision-making method for the proportioning of carbon nanosheet precursors, characterized in that, Includes the following steps: Step 1, Data Collection: Collect data samples of different mass ratios of citric acid and urea in the microwave-assisted preparation of carbon nanosheets, the corresponding preparation parameters, and the performance indicators of carbon nanosheets. Step 2, Data Preprocessing: The collected data samples are preprocessed, including data cleaning, normalization, and feature selection, to improve data quality and applicability; Step 3, Model Building: A predictive model is built using machine learning algorithms to establish a quantitative relationship between the mass ratio of citric acid and urea and the performance of carbon nanosheets. The preprocessed data samples are then used to train the constructed predictive model. The model is validated and optimized through methods such as cross-validation and hyperparameter tuning to improve the model's predictive accuracy and generalization ability. Step 4, ratio optimization: Based on the target performance index required for actual preparation, input the index into the optimized prediction model. Through model calculation and analysis, output the optimal mass ratio of citric acid and urea to achieve the target performance. According to the optimal mass ratio, accurately weigh citric acid and urea, dissolve them in an appropriate amount of deionized water, and stir thoroughly to form a uniform colorless and transparent solution. Step 5, ultrasonic treatment: The prepared solution is subjected to high-frequency ultrasonic treatment for 60-120 minutes to ensure that the components in the solution are fully mixed and form a stable dispersion system; Step 6, Microwave heating: Place the ultrasonically treated solution in a microwave oven, set the microwave power to 700-800W, and the heating time to 5-10 minutes, so that the solution gradually changes from colorless to light yellow solid. During this process, microwave energy promotes the precursor to undergo a chemical reaction to form carbon nanosheet precursor. Step 7, Grinding treatment: Grind the obtained light yellow solid into a fine powder to obtain carbon nanosheet material. Step 8, Electrode preparation: The prepared carbon nanosheet material is uniformly loaded onto the surface of a glassy carbon electrode. Through a specific coating or adhesion process, it is firmly bonded to the electrode, thereby creating a carbon nanosheet electrode material for electrochemical sensors to improve the performance of electrochemical trace detection of heavy metal ions Cu²⁺.

2. The intelligent decision-making method for carbon nanosheet precursor ratio based on machine learning according to claim 1, characterized in that: The machine learning algorithm described employs an algorithm that simulates the structure and function of the human brain's neural network. It possesses powerful nonlinear fitting and feature learning capabilities, and can automatically discover the complex relationship between the ratio of citric acid and urea and the performance of carbon nanosheets. Through the transmission and calculation of a multi-layer neural network, it can achieve accurate prediction of the performance of unknown ratios.

3. The intelligent decision-making method for carbon nanosheet precursor ratio based on machine learning according to claim 2, characterized in that: The performance indicators of the carbon nanosheets include specific surface area, conductivity, electrocatalytic activity, detection sensitivity and detection limit for heavy metal ions Cu²⁺.

4. The intelligent decision-making method for carbon nanosheet precursor ratio based on machine learning according to claim 3, characterized in that: The preparation parameters also include solvent volume, reaction temperature, and reaction time.

5. The intelligent decision-making method for carbon nanosheet precursor ratio based on machine learning according to claim 4, characterized in that: It also includes steps for real-time updating and optimization of the prediction model, improving the model's accuracy and adaptability by continuously adding new preparation data samples.

6. The microwave-assisted preparation system for carbon nanosheet electrode materials using the intelligent decision-making method for carbon nanosheet precursor ratio based on machine learning as described in claim 5, characterized in that: It includes a data acquisition module, a data processing module, a model building module, a model training module, a ratio decision module, a preparation execution module, and an electrode preparation module.

7. The intelligent decision-making method for carbon nanosheet precursor ratio based on machine learning according to claim 6, characterized in that: The data acquisition module also includes a data storage unit for storing collected historical data samples to provide data support for model training.

8. The intelligent decision-making method for carbon nanosheet precursor ratio based on machine learning according to claim 7, characterized in that: It also includes a feedback adjustment module, which is used to adjust and optimize the prediction model based on the deviation between the actual carbon nanosheet performance and the predicted performance.

9. A carbon nanosheet electrode material prepared by the method of claim 1, characterized in that: Carbon nanosheets, obtained through machine learning optimization, are loaded onto glassy carbon electrodes to create an electrochemical sensor for detecting heavy metal ions such as Cu²⁺, exhibiting high sensitivity, low detection limit, and good selectivity.

10. An electrochemical sensor comprising the carbon nanosheet electrode material of claim 9, characterized in that: It can be used for trace detection of heavy metal ions Cu²⁺ in environmental water samples, biological samples and other fields, achieving rapid, accurate and stable detection results, and promoting the widespread application of electrochemical sensors in environmental monitoring, biomedicine and other fields.

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