All-solid-state calibration-free ion concentration detection method

Abstract

The invention discloses an all-solid-state calibration-free ion concentration detection method. The method comprises the following steps: constructing a plurality of traditional three-electrode detection systems; in each traditional three-electrode detection system, current and impedance signals are synchronously acquired through a step potential electrochemical spectrum and an electrochemical impedance spectrum, and interface kinetic parameters are analyzed; on the basis of a traditional three-electrode detection system, an original reference electrode is removed, a counter electrode is reserved, all glassy carbon electrodes with gradient membrane volumes are connected in series one by one to obtain a new three-electrode detection system, the new three-electrode detection system is immersed in a to-be-detected solution, a preset potential is applied, and through transient current response in combination with calibrated kinetic parameters, the gradient membrane volumes of the glassy carbon electrodes are detected. And performing inversion to obtain the ion concentration of the to-be-detected solution. According to the method, automatic calibration is realized by analyzing interface process kinetic parameters of different-volume film ion selective electrodes and calculating the slope to generate a built-in standard curve. According to the method, interface process parameters are obtained through transient current, and dependence of a traditional detection system on the stable potential of a reference electrode is eliminated.

Description

Technical Field

[0001] The present invention relates to the field of automatic calibration analysis of electrochemical analysis, in particular to an all-solid-state calibration-free ion concentration detection method. Background Art

[0002] Ion-selective electrodes have broad application value in medical diagnosis, agricultural monitoring, and environmental analysis. Thanks to breakthroughs in solid-state ion-electron transduction materials and innovations in electronic information technology, a large number of high-performance micro-wearable sensing devices have emerged. Although these devices have demonstrated excellent detection performance, they still face huge challenges in practical applications. During sensor operation, the accumulation of contaminants on the electrode surface and the aging effect of the reference electrode interface will reduce detection reliability, requiring regular calibration to ensure device stability. However, frequent calibration not only interrupts the continuous detection process and prolongs analysis time, but also requires reliance on professional operators and supporting equipment, which conflicts with the technical requirements and development goals of standardized deployment and stable maintenance of wearable devices.

[0003] Current self-calibration / calibration-free research can be divided into two categories: hardware design and data processing. The hardware solution includes two subcategories: flow cell calibration and reference electrode calibration. The solution of calibrating the working electrode in a standard solution using a liquid flow cell and a microfluidic module has good stability, but the liquid flow cell is not suitable for calibration-free wearable devices because it is not portable and requires professional maintenance. The second type of hardware solution uses reference electrode short-circuit calibration to maintain the potential of the working electrode in the solution by short-circuiting the reference and working electrodes. An internal liquid-filled reference electrode is used to ensure the stability of the reference potential, but the volume limitation of the reference electrode makes it unsuitable for wearable scenarios. Although there are currently miniaturized reference electrode solutions (such as PVC heterogeneous membrane-wrapped AgBr-KBr composite salt solid microelectrodes, microfluidic paper-based reference electrodes, and hydrogel-based Ag / AgCl reference electrodes) that have shown performance comparable to internal liquid-filled references in scenarios reported in the literature, there is a lack of standardized comprehensive evaluation criteria for miniaturized solid-state references, which may cause problems when expanding to other applications. Methods based on data processing usually use modified test schemes combined with machine learning analysis, such as developing a low-selectivity ISE array and using machine learning to simulate and analyze the transient differential potential signal generated when the solution is switched. Although this method is suitable for differential potential analysis of multi-ion systems, it fails to detect simple samples of single ions. The reason is that the effectiveness of machine learning depends on the size of the data set, which limits its application in small sample scenarios. In summary, hardware-based calibration-free attempts introduce complex components and are not suitable for portable wearable devices. Data processing solutions based on machine learning face scalability bottlenecks due to sample size limitations. To solve the above problems, electrode process kinetics analysis has become another data processing path to achieve calibration-free smart devices.

[0004] Although kinetic methods and machine learning both fall under the category of embedded data processing, they differ fundamentally. Unsupervised machine learning offers the advantage of not relying on prior knowledge of the system, but this presents two key challenges: First, most unsupervised machine learning approaches lack semantic interpretation of the discrete feature extraction process, hindering understanding of physicochemical mechanisms. Second, due to the uninterpretability of features, generalization of the algorithms across different systems may encounter unpredictable issues. In contrast, electrode process kinetics, through the establishment of a rigorous physicochemical description of interfacial processes, establishes a theoretical understanding of the system. This gives it a unique adaptability advantage: when faced with unknown systems, targeted parameter adjustments can be made within the theoretical kinetic framework. Unknown systems with predictable parameters (those in which only parameter values vary, not the number of parameters), can maintain stable performance without requiring model reconstruction. Even under unusual circumstances, kinetic models based on mechanistic understanding can rapidly diagnose and correct errors. In contrast, machine learning often requires re-data collection and time-consuming parameter tuning in similar scenarios. The key difference lies in the fact that kinetic methods employ interpretable theoretical models to achieve mechanism-driven system adaptation, while machine learning relies on the black-box nature of data-driven optimization, requiring trial-and-error adjustments. Therefore, the analysis of electrode process kinetics provides solutions and theoretical support for the realization of calibration-free / self-calibration all-solid-state ISE and wearable devices. Summary of the Invention

[0005] In light of this, the present invention provides an all-solid-state, calibration-free ion concentration detection method. This method utilizes a working electrode array with a gradient membrane volume design, coupled with a single pseudo-reference electrode (surface-modified with only a transduction layer material) to sequentially collect potential signals in series. This method, combined with a pre-set interface dynamic model, performs concentration inversion calculations, enabling automated detection without the need for a reference electrode or manual calibration. By constructing a three-dimensional structure consisting of a glassy carbon electrode / carbon nanotube / sodium ion-selective membrane, the system successfully achieves self-calibrated sodium ion detection within a range of 0.01-100 mM, with a relative error of no more than 5% compared to a standard solution.

[0006] To achieve the above object, the present invention provides the following technical solutions:

[0007] The present invention discloses an all-solid-state calibration-free ion concentration detection method, comprising the following steps:

[0008] S1. Construct multiple traditional three-electrode detection systems: provide a working electrode, a reference electrode, and a counter electrode, and immerse them in a standard solution to obtain a traditional three-electrode detection system; the working electrode is a glassy carbon electrode modified with a gradient membrane volume, and the gradient membrane volume is 0-10 μL;

[0009] S2. In each traditional three-electrode detection system, current and impedance signals are collected simultaneously through step potential electrochemical spectroscopy and electrochemical impedance spectroscopy to analyze the interface kinetic parameters;

[0010] S3. Construct a new three-electrode detection system: Based on the traditional three-electrode detection system, remove the original reference electrode, retain the counter electrode, and connect all the gradient membrane volume glassy carbon electrodes in series one by one to obtain a new three-electrode detection system, which is then immersed in the solution to be tested. Among them, the 0 μL membrane-modified glassy carbon electrode (i.e., the electrode with only the transduction layer material modified on the surface) serves as the reference electrode (i.e., pseudo-reference electrode), and the remaining series-connected gradient membrane volume-modified glassy carbon electrodes serve as the working electrode array;

[0011] S4. In the new three-electrode detection system, a preset potential is applied, and the ion concentration of the test solution is inverted through the transient current response combined with the calibrated kinetic parameters, realizing automatic detection without reference electrodes and manual calibration.

[0012] The all-solid-state calibration-free ion concentration detection method according to claim 1, characterized in that in step S4, the inversion formula is:

[0013]

[0014] Where I(t) is the function of current changing with time, constants A and B represent the relaxation time distribution ratio, R1, R2, C1, C2, K1, K2, and K3 are the interface process kinetic coefficients, where: R1 and R2 are the resistances of different interface processes on the same working electrode, in Ω; C1 and C2 are the capacitances of different interface processes on the same working electrode, in F; K1 is the diffusion kinetic coefficient of the 0 μL film-modified glassy carbon electrode, and K2 and K3 are the diffusion kinetic coefficients of the gradient film volume-modified glassy carbon electrode; i R is the intrinsic residual current, in A; △E is the difference between the applied potential and the open circuit potential, in V.

[0015] As a further solution of the present invention: the potential step amplitude of the step potential electrochemical spectrum is 0.05V, the potential sampling range is -0.5 to 0.5V, each potential point is maintained for 100 to 300 seconds, and the current sampling interval is ≤0.1 second.

[0016] As a further solution of the present invention: the test frequency range of the electrochemical impedance spectroscopy is 0.01 Hz to 10 kHz, and the polarization pretreatment time for each potential point is ≥300 seconds.

[0017] As a further solution of the present invention: in step S2, analyzing the interface dynamic parameters includes:

[0018] The interface resistances R1 and R2 and the capacitances C1 and C2 were separated by distributed relaxation time analysis. The diffusion coefficients K1, K2, K3 and the residual current iR were fitted using a global optimization algorithm. R2 and K2 showed a concentration-linear response within the open circuit potential range of ±50 mV.

[0019] As a further solution of the present invention: step S4 is also included:

[0020] Based on the pre-calibrated residual current iR and interfering ion compensation factor, error correction is performed on the detection results of ion concentrations below 1μM.

[0021] As a further aspect of the present invention, the effective detection range of ion concentration is 10 μM–100 mM.

[0022] As a further solution of the present invention: the gradient membrane volume working electrode array includes four glassy carbon electrodes connected in series, and the coating membrane volumes are 1, 4, 7, and 10 μL respectively.

[0023] As a further solution of the present invention: the composition of the coated ion membrane solution is: sodium ion carrier 1-2wt%, NaTFPB 1-1.5wt%, o-NPOE 60-70wt%, PVC matrix 30-35wt%.

[0024] Compared with the prior art, the present invention has the following beneficial effects:

[0025] This paper develops an embedded self-calibration analysis method based on interfacial process kinetics, thereby establishing an all-solid-state self-calibration ion detection system that does not require a reference electrode. While maintaining the established workflow, this method is scalable to accommodate different sensing materials, electrodes, and target ions, providing a viable solution for the miniaturization of wearable devices and the development of a new generation of self-calibrating smart sensors.

[0026] The present invention proposes a method for automatic calibration by analyzing the interface process kinetic parameters of ion-selective electrodes with different volume membrane layers, calculating the slope and generating a built-in standard curve. Since this method obtains the interface process parameters through transient current, it gets rid of the traditional detection system's dependence on the stable potential of the reference electrode. This solution can achieve universal adaptation under a unified workflow and is compatible with a variety of detection targets and transduction layer material systems. Taking carbon nanotubes as an example, this study established a standard model based on four volume membrane layers of 1, 4, 7, and 10 μL, and achieved self-calibration detection of sodium ions in a wide concentration range of 0.1-100 mM without the need for a reference electrode (verified in the temperature range of 288-313 K). The results show that the detection system exhibits excellent stability (average relative standard deviation <4%) and accuracy (relative concentration error <3%). BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1Impedance spectroscopy data and machine learning classification results for interfacial process analysis in a traditional three-electrode detection system. (a) Impedance data for a glassy carbon electrode modified with a carbon nanotube transduction layer in the potential range of -0.5V to 0.5V. (b) A three-dimensional frequency-potential-phase angle relationship plot of the impedance data. (c) A frequency-potential-imaginary capacitance correlation plot of the impedance data. (d) A schematic diagram of machine learning classification of impedance data. (e) A step potential electrochemical spectroscopy (SPECS) curve for a glassy carbon electrode modified with a carbon nanotube transduction layer in the potential range of -0.5V to 0.5V. (f) A curve showing the relationship between electrode process kinetic parameters obtained by fitting the SPECS data.

[0028] Figure 2 The distribution pattern and slope analysis of the kinetic parameters of the ion-selective membrane electrode system at different concentrations as the potential changes in the traditional three-electrode detection system; among them, (a) is a comparative analysis of the kinetic parameter series resistance R1 between ion-selective membrane electrode systems with different concentrations. (b) is a comparative analysis of the series resistance R2. (c) is a comparative analysis of the capacitance parameter C1. (d) is a comparative analysis of the capacitance parameter C2. (e) is a comparative analysis of the diffusion coefficient K1. (f) is a comparative analysis of the diffusion coefficients K2 and K3. (g) is the distribution of the slopes of the kinetic parameters R1 and C1 of the ion-selective membrane with different concentrations in the potential range under the same applied potential. (h) is the slope distribution of the kinetic parameters R2 and C2. (i) is the slope distribution of the kinetic parameters K2 and K3.

[0029] Figure 3 The results of calibrating the new three-electrode detection system using kinetic parameters based on the traditional three-electrode detection system; (a) shows the transient current generated by the ion-selective membrane-modified electrode with different concentrations in a gradient NaCl solution. (b) shows the relationship between the kinetic parameters calculated from the transient current and the existing R2 and K2 curves, where the circle, triangle, and square marks represent three independent experiments and the fitting results, respectively. (c) shows the ion concentration error distribution obtained based on the inversion of the kinetic parameters and its relative deviation from the actual concentration. (d) shows the ion concentration error distribution and its relative deviation from the actual concentration after adding interfering ions.

[0030] Figure 4 Results of the new three-electrode detection system's stability assessment. (a) shows the calculated kinetic parameters and their position distribution on the R² and K² curves for the 0.1–100 mM concentration range at different temperatures. Different geometries correspond to different temperature conditions. (b) shows the average relative standard deviation of the four ion concentrations at different temperatures and the average relative deviation from the actual concentrations.

[0031] Figure 5This is a flow chart of the method of the present invention. Specifically, the original electrochemical data is first divided into two parts, SPECS data and EIS data. The EIS data is first classified by machine learning, and then the interface process in the EIS data is extracted by DRT analysis and established as the I(t) equation. The SPECS data is then fitted according to the I(t) equation to obtain different kinetic process parameters. DETAILED DESCRIPTION

[0032] To facilitate understanding of the present invention, the present invention will be described more fully below in conjunction with specific embodiments. However, the present invention can be implemented in many different forms and is not limited to the embodiments described herein. On the contrary, the purpose of providing these embodiments is to provide a more thorough and comprehensive understanding of the disclosure of the present invention.

[0033] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art of the present invention. The terms used in the specification of the present invention herein are only for the purpose of describing specific embodiments and are not intended to limit the present invention.

[0034] like Figure 5 As shown, this implementation specifically consists of two parts. The first part uses electrochemical methods to characterize and analyze the kinetics and related parameters of the working electrode in a traditional three-electrode system. This process is a conventional electrochemical experiment. The traditional three-electrode system consists of a 3mm diameter glassy carbon electrode (working electrode), a platinum wire electrode (counter electrode), and an Ag / AgCl electrode (reference electrode). The experiments were conducted in four glassy carbon electrode systems loaded with materials and modified with different membrane volumes (0, 4, 7, and 10μL). Step potential electrochemical spectroscopy (SPECS) and electrochemical impedance spectroscopy were used. The experimental medium (i.e., standard solution) was a 0.1mM NaCl solution. The second part tested the potential gradient of the NaCl solution concentration gradient (0.1μM-100mM). In this stage, an electrode array composed of glassy carbon electrodes loaded with different membrane volumes (4, 7, and 10μL) in series served as the working electrode. A glassy carbon electrode modified only with the transduction layer material but not the ion-selective membrane served as the reference electrode. The platinum wire electrode continued to serve as the counter electrode to form a three-electrode system. The test was carried out using the zero-current potentiometry method.

[0035] 1. Preparation of working electrode:

[0036] (1) Cleaning: First, polish five glassy carbon electrodes in sequence using alumina suspensions of different particle sizes (1 μm, 0.3 μm, and 0.05 μm), polishing for 1-2 minutes for each particle size. After polishing, immerse the electrodes in HNO3 solution, C2H5OH solution, and H2O for ultrasonic cleaning, each cleaning time being no less than 30 seconds. After thorough drying, set aside.

[0037] (2) Coating the transduction layer: A carbon nanotube aqueous dispersion with a concentration of 10 mg / ml was drop-coated on the surface of four clean glassy carbon electrodes in a volume of 10 μL. The transduction layer was formed after drying.

[0038] (3) Coating the ion membrane: Coat the surfaces of four clean glassy carbon electrodes with ion membrane solutions of corresponding membrane volumes (4, 7, and 10 μL), and after drying, obtain working electrodes modified with different membrane volumes. The preparation process of the sodium ion membrane solution is as follows:

[0039] A sodium ion membrane solution was prepared by dissolving 1 wt% sodium ion carrier (ETH 1062), 1.02 wt% NaTFPB, 65.32 wt% o-NPOE, and 32.66 wt% PVC (total mass 300 mg) in 3 mL of tetrahydrofuran (THF). All reagents were purchased from Sinopharm Chemical Reagent Co., Ltd. (China) and were of analytical grade.

[0040] 2. Analytical interface electrodynamic parameters:

[0041] All electrochemical experiments were conducted in a traditional three-electrode system using an Autolab PGSTAT302N electrochemical workstation (Metrohm, Herisau, Switzerland). The raw electrochemical data were divided into SPECS data and EIS data. The EIS data were first classified using machine learning. Then, DRT analysis was used to extract the interfacial processes in the EIS data and establish them as the I(t) equation. The SPECS data were then fitted according to the I(t) equation to obtain different kinetic process parameters.

[0042] The SPECS test applies a fixed potential step within a preset potential range, maintains each potential point for a constant time, and records the response current; the electrochemical impedance spectroscopy measures the impedance spectrum at each fixed potential point under the same potential range setting. The specific parameter settings and operation procedures are as follows:

[0043] In the SPECS test, the polarization phase of data acquisition lasts for 100 seconds, and a 1000-second equilibrium interval is set after each cycle. Subsequent cycles use a potential increment step of 0.025V and record the current response at a fixed interval of 0.01 seconds. The electrochemical impedance spectroscopy (EIS) test frequency range is 10kHz to 0.01Hz. Before each impedance test, a 300-second open circuit potential polarization pretreatment is performed to ensure system equilibrium. After each impedance test, the working potential is increased by 0.025V and left to stand for 1000 seconds before subsequent testing.

[0044] Based on the kinetic equation analysis of the electrode system interface process, the electrode system kinetic parameters are obtained and characterized as I-(t&E) physicochemical equations, and finally a reference-free electrode system is constructed according to the obtained kinetic parameters.

[0045] like Figure 1 As shown, Figure 1 AD shows the impedance spectroscopy data and machine learning classification results of interface process analysis. Figure 1 a presents the original impedance spectrum data; Figure 1 b is the three-dimensional relationship diagram of frequency-potential-phase angle derived from the original data; Figure 1 c is a frequency-potential-imaginary capacitance correlation diagram constructed based on the imaginary part of impedance, Figure 1 d shows the machine learning data processing process: principal component analysis (PCA) is used for data dimensionality reduction, and support vector machine (SVM) is selected for sample classification and screening. Figure 1 e shows the original data of the SPECS method, using a 50 mV step amplitude for 300 s of constant potential detection; Figure 1 Figure f shows the interfacial process parameters analyzed by fitting the kinetic equations. Different geometric symbols correspond to specific physical parameter types: these primarily include the potential-dependent interfacial resistance and capacitance, and the potential-independent diffusion coefficient and residual current. Physical identification of the interfacial process was achieved using the distributed relaxation time (DRT) analysis program (open source) compiled in MATLAB. Quantitative parameter analysis was performed by numerically fitting the kinetic equations using the global optimization software OpenLu64 (open source). The equations are as follows:

[0046]

[0047] Where I(t) is the function of current changing with time, constants A and B represent the relaxation time distribution ratio, R1, R2, C1, C2, K1, K2, and K3 are diffusion kinetic coefficients, and subscripts 1 and 2 correspond to the charge accumulation processes related to the geometric surface area of the working electrode interface and the porous structure of the carbon nanotube transduction layer, respectively. Among them, R1 and R2 are the resistances of different interface processes on the same working electrode; C1 and C2 are the capacitances of different interface processes on the same working electrode; K1 is the diffusion kinetic coefficient of the glassy carbon electrode modified with 0 μL membrane, and K2 and K3 are the diffusion kinetic coefficients of the glassy carbon electrode modified with gradient membrane volume; i R It characterizes the intrinsic residual current associated with the slowly changing physical and chemical processes in the electrode interface system during the entire experimental process; △E is the difference between the applied potential and the open circuit potential.

[0048] It should be noted that the above equation is valid for each electrode in the working electrode array.

[0049] Figure 2Figures af show the distribution patterns of six kinetic parameters (R1, R2, C1, C2, K2, and K3) of ion-selective membranes with different coating volumes as a function of potential. It should be noted that K1 is related to the diffusion coefficient of the transduction layer of the membraneless electrode and is not analyzed. Different colors correspond to different membrane coating volumes: black, red, blue, and green represent 1, 4, 7, and 10 μL, respectively. Figure 2 Figures ab show the distribution characteristics of the series resistances R1 and R2. It can be seen that as the coating volume of the ion-selective membrane increases, both resistance parameters show a volume-dependent increasing trend. Figure 2 cd shows the distribution of capacitance parameters C1 and C2. As the volume of the ion-selective membrane increases, the capacitance value decreases, and the attenuation is particularly significant under high membrane volume conditions. Figure 2 ef displays the diffusion coefficients K2 and K3, reflecting the dynamics of limited diffusion within the membrane. Because these coefficients are closely related to the ion concentration gradient within the membrane, they also decrease with increasing volume of the ion-selective membrane, with a significant decrease also observed at 10 μL. R2 and K2 maintain linear responses on both sides of the open-circuit potential, a characteristic that suggests these two parameters can serve as key indicators for quantitative inversion of ion concentration. The remaining parameters exhibit only a linear response on one side, but their nonlinear response regions are relatively stable, making them suitable as auxiliary factors for verifying ion concentration inversion results.

[0050] 4. Construct a new three-electrode detection system: Based on the traditional three-electrode detection system, the original reference electrode is replaced by a 0μL membrane-modified glassy carbon electrode, and the 4, 7, and 10μL membrane volume-modified glassy carbon electrodes are connected in series one by one to obtain a working electrode array, which together with the counter electrode constitutes a new three-electrode detection system and is immersed in the test solution for detection; a bulk solution with a pH = 7 is configured as the test solution, that is, an aqueous solution with a concentration gradient of 0.1-100mM NaCl.

[0051] Figure 3 Calibration using kinetic parameters is shown. Because the 10 μL ion-selective membrane is too thick, it results in a significant increase in the working electrode R1 and R2 (maximum increase exceeding 50%), which blocks the interfacial charge transfer process and is not conducive to ion transport. Therefore, only three thicknesses of 1, 4, and 7 μL are used. Figure 3 Figure a shows the experimental results, with different colored curves representing the transient current generated by electrodes with different membrane-coated volumes (black for 1 μL, red for 4 μL, and blue for 7 μL). Notably, within the concentration gradient range of 10 μM to 100 mM, the current peak exhibits a significant gradient increase. However, when the concentration is below 1 μM, the variability in the current peak decreases significantly. Figure 3 b shows the distribution of three independent experiments and calculated samples on the R2 and K2 curves. The linear fitting slopes corresponding to the kinetic parameters of the three experiments are marked on Figure 3b curve. The horizontal axis represents the Nernst equation under 298.15K conditions. Figure 2 The cation concentration gradient range obtained by converting the hi potential interval. Figure 3 c shows the statistical analysis of the relative error between the ion concentration inverted by this method and the actual concentration, as well as the distribution of the error bars of three experiments. The blue bar represents the relative error, where 10 -7 , 10 -6 and 10 -4 The error of the sample increases significantly. -7 with 10 -6 The large error of the sample is due to the mismatch between the electrode system dynamics and the model. -4 The error of the sample is caused by the algorithm not considering the discontinuity of the critical point function. The distribution of the error bars shows that the method is highly stable: in the seven concentration gradient tests, except for 10 -4 The relative standard deviations of all data samples except 10 are less than 5%. -4 The sample stability is reduced due to the discontinuity of the function critical point, which can be circumvented by selectively optimizing the function during the parameter inversion process. Figure 3 d shows the introduction of interfering ions (1 mM K + , Ca 2+ With Mg 2+ ) and the results of the parameter calculation error analysis after three independent experiments were highly consistent with the results of the sample without interfering ions. After the optimization function was selected, the 0.1mM parameter was completely matched with the actual concentration, and the relative standard deviation of all sample points was less than 3%. In addition, the low concentration of 10 -7 with 10 -6 The relative standard deviation of the samples decreased, which may be because the addition of interfering ions increased the ionic strength of the solution, thereby enhancing the ion diffusion and counter-diffusion processes in the membrane.

[0052] Figure 4 The evaluation results of the detection stability of the new three-electrode detection system are presented. Figure 4 a is the calculated results of kinetic parameters of 0.1-10mM concentration gradient in the temperature range of 288-313K. Different geometric shapes represent the slope distribution of kinetic parameters calculated under different temperature conditions. Figure 4 From the data a, we can see that the potential value converted by the slope of the system with higher temperature is larger (which can be converted into ion concentration through the Nernst equation). Figure 4b shows the distribution of the average relative error and average relative standard deviation of the four concentration gradients under various temperature conditions. The blue bar graph represents the error relative to the known concentration and can be used to evaluate the accuracy of the method: although the relative error increases with increasing temperature, it is always below 4%, indicating that the method maintains high accuracy under different temperature conditions. The increase in relative error is due to the large differences in the dynamics of different concentration membranes at high temperatures, suggesting that the R2 and K2 curves at different temperatures may need to be adjusted. The red dotted line shows the average relative standard deviation. After three independent experiments, the relative standard deviation of all temperature systems was less than 3%, demonstrating the excellent stability and reproducibility of the method.

[0053] In this embodiment, a new electrode system consisting of a pseudo-reference electrode constructed with only carbon nanotubes as the conductive layer and a gradient membrane volume working electrode array using Na+ as the detection object can achieve accurate analysis in the concentration range of 0.1-100mM and maintain stability in the temperature range of 288-313K. Based on the analysis of the kinetic parameters of the membrane electrode process with different volumes, a standard curve is established in a specific potential range through the parameter slope. In actual detection, the ion concentration is inferred by the kinetic parameters fitted by the step current characteristics. This case shows that the relative standard deviation under different temperature conditions is less than 3%, with excellent reproducibility; the relative error in the high concentration range is less than 4%, showing good accuracy.

[0054] Although this specification is described according to implementation methods, not every implementation method contains only one independent technical solution. This narrative method of the specification is only for the sake of clarity. Those skilled in the art should regard the specification as a whole. The technical solutions in each embodiment can also be appropriately combined to form other implementation methods that can be understood by those skilled in the art.

[0055] Therefore, the above description is only a preferred embodiment of the present application and is not intended to limit the scope of implementation of the present application; that is, all equivalent modifications made according to the scope of the claims of the present application are within the scope of protection of the claims of the present application.

Claims

1. A solid-state calibration-free ion concentration detection method, characterized in that: The following steps are involved: S1. Construct multiple traditional three-electrode detection systems: provide working electrode, reference electrode and counter electrode, immerse them in standard solution, and obtain traditional three-electrode detection system; The working electrode is a glassy carbon electrode modified with a gradient membrane volume, and the gradient membrane volume is 0-10 μL; S2. In each traditional three-electrode detection system, current and impedance signals are collected simultaneously through step potential electrochemical spectroscopy and electrochemical impedance spectroscopy to analyze the interface kinetic parameters; S3. Construct a new three-electrode detection system: Based on the traditional three-electrode detection system, remove the original reference electrode, retain the counter electrode, and connect all the glassy carbon electrodes with gradient membrane volumes in series one by one to obtain a new three-electrode detection system, which is then immersed in the solution to be tested; S4. In the new three-electrode detection system, a preset potential is applied, and the ion concentration of the test solution is inverted through the transient current response combined with the calibrated kinetic parameters, realizing automatic detection without reference electrodes and manual calibration.

2. The all-solid-state calibration-free ion concentration detection method according to claim 1, characterized in that: In step S4, the inversion formula is: Where I(t) is the function of current changing with time, constants A and B represent the relaxation time distribution ratio, R1, R2, C1, C2, K1, K2, and K3 are the interface process kinetic coefficients, where: R1 and R2 are the resistances of different interface processes on the same working electrode, in Ω; C1 and C2 are the capacitances of different interface processes on the same working electrode, in F; K1 is the diffusion kinetic coefficient of the 0 μL film-modified glassy carbon electrode, and K2 and K3 are the diffusion kinetic coefficients of the gradient film volume-modified glassy carbon electrode; i R is the intrinsic residual current, in A; △E is the difference between the applied potential and the open circuit potential, in V.

3. The all-solid-state calibration-free ion concentration detection method according to claim 1, characterized in that: The potential step amplitude of the step potential electrochemical spectrum is 0.05 V, the potential sampling range is -0.5 to 0.5 V, each potential point is maintained for 100 to 300 seconds, and the current sampling interval is ≤0.1 second.

4. The all-solid-state calibration-free ion concentration detection method according to claim 1, wherein: The test frequency range of the electrochemical impedance spectroscopy is 0.01 Hz to 10 kHz, and the polarization pretreatment time of each potential point is ≥300 seconds.

5. The all-solid-state calibration-free ion concentration detection method according to claim 1, characterized in that: In step S2, analyzing the interface dynamic parameters includes: The interface resistances R1, R2 and capacitances C1, C2 were separated by distributed relaxation time analysis; the diffusion coefficients K1, K2, K3 and the residual current i were fitted using a global optimization algorithm. R , where R2 and K2 exhibit concentration linear responses within the open circuit potential range of ±50 mV.

6. The all-solid-state calibration-free ion concentration detection method according to claim 1, characterized in that: Also includes step S4: Based on the pre-calibrated residual current i R and interfering ion compensation factors to correct errors in test results with ion concentrations below 1 μM.

7. The all-solid-state calibration-free ion concentration detection method according to claim 1, characterized in that: The effective detection range of ion concentration is 10μM–100mM.

8. The all-solid-state calibration-free ion concentration detection method according to claim 1, characterized in that: The gradient film volume working electrode array includes four glassy carbon electrodes connected in series, and the coating film volumes are 1, 4, 7, and 10 μL respectively.

9. The all-solid-state calibration-free ion concentration detection method according to claim 8, characterized in that: The composition of the coated ion membrane solution is: 1-2 wt% of sodium ion carrier, 1-1.5 wt% of NaTFPB, 60-70 wt% of o-NPOE and 30-35 wt% of PVC matrix.

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