A method for dynamically evaluating the number of microorganisms based on a carbon-based modified electrode

Abstract

The application discloses a kind of based on carbon-based modified electrode microbial quantity dynamic evaluation method.The method comprises the following steps: preparing carbon-based modified electrode doped with target microorganism;Carbon-based modified electrode is used as working electrode, and reference electrode and counter electrode constitute three-electrode system, and is placed in the medium containing target microorganism to be measured in the electrochemical scanning of target microorganism, and the electrochemical response signal is collected;The electrochemical response signal is processed, and the characteristic electrochemical parameter of target microorganism metabolite is obtained;According to the preset detection time threshold selection model: when detection time is less than or equal to preset duration, linear model is used;When detection time is greater than preset duration, dynamic metabolism model is used;Characteristic electrochemical parameter is substituted into the selected model, and the number of microorganisms in the medium to be measured is calculated.The application can realize accurate quantification, anti-interference detection and rapid dynamic evaluation of the number of microorganisms, and significantly improve detection efficiency and accuracy.

Description

Technical Field

[0001] This invention belongs to the field of microbial detection technology, and more specifically, relates to a method for dynamic evaluation of microbial numbers based on carbon-modified electrodes. Background Technology

[0002] Accurate and rapid detection of microbial counts is crucial in environmental monitoring (such as microbial corrosion in oilfield systems), food safety, medical diagnosis, and industrial biological process monitoring. Traditional microbial detection methods, such as plate count, maximum probability count, and direct microscopic counting, generally have inherent limitations, including being cumbersome, time-consuming (usually 24-72 hours), unable to achieve real-time online monitoring, and difficult to effectively distinguish between live and dead bacteria. These limitations make it difficult to meet the demands of modern applications for detection efficiency and intelligence.

[0003] To overcome the aforementioned drawbacks, electrochemical methods have become a research hotspot in the field of microbial sensing due to their advantages such as rapid response, high sensitivity, ease of device integration, and the potential for real-time online monitoring. Current electrochemical microbial detection strategies are mainly developing along the following three directions: (1) Indirect detection based on metabolic activity: Microbial activity is indirectly reflected by monitoring dissolved oxygen consumed by microbial respiration, pH changes caused by acidic substances produced by metabolism, or redox reactions of metabolic intermediates. However, such methods are easily affected by fluctuations in the background composition of the environment, and the relationship between electrochemical signals and the number of viable bacteria is complex and easily affected by the metabolic state of the microbial community, making it difficult to achieve accurate quantification.

[0004] (2) Detection based on direct electrochemical response of microorganisms: This method utilizes the direct electron transfer between certain electroactive microorganisms (such as Geobacterium and Shewanella) or their secreted redox mediators on the electrode surface to generate an electrical signal. However, this method has poor universality. For most environmental and industrial microorganisms (such as sulfate-reducing bacteria, Escherichia coli, and Staphylococcus aureus), the direct electron transfer efficiency is extremely low, and the signal is weak and unstable, making it difficult to directly apply to the detection of actual samples.

[0005] (3) Detection based on conductive polymer composites: Taking advantage of the fact that microbial surfaces are usually negatively charged, they are embedded as dopants into conductive polymers (such as polypyrrole, PEDOT (poly(3,4-ethylenedioxythiophene)) networks generated by electropolymerization on the electrode surface to achieve microbial immobilization. However, existing studies have focused on optimizing the composite material preparation process, assessing bacterial survival rate, and basic electrochemical characterization. A quantitative detection model or method that can directly, accurately, and universally convert the observed electrochemical signals into the number of microorganisms has not yet been established.

[0006] Meanwhile, carbon-based electrode materials (such as glassy carbon, graphene, and carbon nanotubes) are highly favored in the field of electrochemical biosensing due to their high specific surface area, excellent conductivity, chemical stability, and good biocompatibility. However, the surface of pure carbon materials lacks the ability to specifically recognize and enhance signals for target microorganisms and their metabolites. In complex media, they are easily affected by non-specific adsorption and background signals, resulting in insufficient detection sensitivity and specificity.

[0007] In summary, although existing technologies have demonstrated the potential of conductive polymers for immobilizing microorganisms and the excellent performance of carbon-based materials, the following prominent technical bottlenecks still exist: (i) Lack of quantitative models: Existing studies are mostly at the qualitative or semi-qualitative level, lacking precise mathematical models that directly correlate electrochemical signals (such as peak current and peak area) with the number of microorganisms, especially dynamic models that can reflect the growth and metabolic dynamics of microorganisms, making it difficult to achieve accurate quantitative assessment of the number of microorganisms.

[0008] (ii) Weak resistance to interference from complex media: In the detection of actual samples (such as oilfield produced water, industrial wastewater, food matrix, etc.), high concentrations of phosphate, chloride ions, surfactants and other components in the medium are prone to generate strong non-specific electrochemical responses, which seriously interfere with the characteristic signals of target metabolites. Existing methods lack effective in-situ anti-interference strategies, resulting in a decrease in detection accuracy and reliability.

[0009] (iii) Insufficient technology integration: The system failed to systematically integrate and optimize highly biocompatible microbial immobilization technology (such as conductive polymer doping), high-performance carbon-based electrode materials, and anti-interference design for complex media to form a complete, reliable, and practical microbial quantity detection solution.

[0010] Therefore, there is an urgent need in this field for a new method that can achieve rapid, accurate, and interference-resistant quantitative assessment of the number of viable microorganisms, in order to systematically solve the above-mentioned technical bottlenecks and meet the urgent needs of rapid detection of microorganisms in fields such as environmental monitoring, food safety, and industrial production. Summary of the Invention

[0011] The purpose of this invention is to propose a dynamic assessment method for microbial quantity based on carbon-modified electrodes, which solves the technical bottlenecks of existing microbial detection technologies, such as the lack of quantitative models, weak resistance to interference from complex media, and insufficient technology integration; and achieves accurate quantification, interference-resistant detection, and rapid dynamic assessment of microbial quantity, significantly improving detection efficiency and accuracy.

[0012] To achieve the above objectives, this invention proposes a method for dynamic assessment of microbial populations based on carbon-modified electrodes, comprising: Prepare carbon-based modified electrodes doped with target microorganisms; The carbon-based modified electrode was used as the working electrode, forming a three-electrode system with the reference electrode and the counter electrode. The system was placed in the test medium containing the target microorganism for electrochemical scanning, and the electrochemical response signal was collected. The electrochemical response signal is processed to obtain the characteristic electrochemical parameters of the target microbial metabolites; The model is selected based on a preset detection duration threshold: when the detection time is less than or equal to the preset duration, a linear model is used; when the detection time is greater than the preset duration, a dynamic metabolic model is used. By substituting the characteristic electrochemical parameters into the selected model, the number of microorganisms in the test medium can be calculated.

[0013] Optionally, the carbon-based modified electrode comprises, from bottom to top: Porous carbon substrate; A conductive polymer-microorganism composite membrane is formed on the surface of the porous carbon substrate, wherein the composite membrane is a PEDOT membrane doped with target microorganisms; An anti-interference layer is formed on the surface of the conductive polymer-microorganism composite membrane, wherein the anti-interference layer is a Nafion membrane.

[0014] Optionally, the conductive polymer-microbial composite membrane is prepared by the following electropolymerization process: The pretreated porous carbon substrate was used as the working electrode and placed in an electrolyte; the electrolyte contained EDOT monomer, supporting electrolyte and suspension containing target microorganisms. Polymerization was carried out at a constant potential of 1.0V to 1.2V (vs. Ag / AgCl) for 800s to 1000s. During the polymerization process, the PEDOT chains formed were positively charged in the oxidized state and were electrostatically attracted to the negatively charged microorganisms on the surface. This allowed the microorganisms to be embedded in the growing PEDOT network as dopants, forming a conductive polymer-microorganism composite film.

[0015] Optionally, the concentration of EDOT monomer in the electrolyte is 8 mM to 12 mM, the concentration of the supporting electrolyte is 0.05 M to 0.15 M, and the concentration range of the microbial suspension is 10 mM. 2 CFU / mL ~10 7 CFU / mL; the supporting electrolyte is selected from LiClO4, NaCl or KCl.

[0016] Optionally, the anti-interference layer is a Nafion film, which is modified onto the surface of a conductive polymer-microbial composite film by spin coating or electrochemical deposition. The process parameters for spin coating are: 5% w / w Nafion ethanol solution, rotation speed 2500 rpm to 3000 rpm, and spin coating time 60 s to 90 s.

[0017] Optionally, the porous carbon substrate is prepared by chemical vapor deposition, using methane as the carbon source, with a CH4:H2 volume ratio of 1:3, and deposited at 800℃~900℃ and 40kPa~60kPa for 1~2 hours to obtain a nanoporous structure with a specific surface area greater than 1200m² / g.

[0018] Optionally, the processing of the electrochemical response signal includes: The collected electrochemical response signals are filtered. The characteristic peak current value and / or characteristic peak integral area are extracted from the filtered signal and used as the characteristic electrochemical parameters.

[0019] Optionally, the expression for the linear model is: ; ; in, Microbial count (CFU / mL); Characteristic peak current (μA); Sensitivity coefficient (μA / CFU·mL) - ¹); This represents the background current (μA).

[0020] Optionally, the expression for the dynamic metabolic model is: ; After integration, we get: ; ; in, Microbial count (CFU / mL); The integral area of ​​the characteristic peak (μC); Maximum metabolic rate of microorganisms (h - ¹); Metabolic decay constant (h) - ¹); The detection time is in hours (h). The background integral area (μC) is the area without microorganisms.

[0021] Optionally, the filtering process is Savitzky-Golay filtering, and the filtering parameters include window width and polynomial order; The window width is 15, and the polynomial order is 3.

[0022] Optionally, cyclic voltammetry or differential pulse voltammetry can be used for scanning.

[0023] The beneficial effects of this invention are as follows: By preparing a carbon-based modified electrode doped with target microorganisms, the microorganisms are immobilized in situ on the electrode surface, achieving efficient coupling between microbial activity and the electrode interface; by constructing a three-electrode system using the carbon-based modified electrode as the working electrode and performing electrochemical scanning, electrochemical response signals generated by microbial metabolism are collected; by processing the response signals to obtain characteristic electrochemical parameters, effective extraction and quantification of metabolic signals are achieved; by dynamically selecting a linear model or a dynamic metabolic model according to a preset detection duration threshold, and substituting the characteristic electrochemical parameters into the model for calculation, accurate quantitative assessment of microbial quantity is finally achieved. This invention effectively solves the problems of missing quantitative models, weak anti-interference ability, and insufficient technology integration in existing technologies through synergistic optimization of electrode structure design, signal processing, and model selection. It achieves rapid, sensitive, and anti-interference detection of microbial quantity, and simultaneously meets the detection needs of different time scales through dual-model adaptation, significantly improving the universality and accuracy of the detection method.

[0024] The system of the present invention has other features and advantages that will be apparent from or will be set forth in detail in the accompanying drawings and following detailed description, which together serve to explain the particular principles of the invention. Attached Figure Description

[0025] The above and other objects, features and advantages of the present invention will become more apparent from the accompanying drawings, in which like reference numerals generally denote like parts.

[0026] Figure 1 A flowchart illustrating the steps of a method for dynamically assessing microbial populations based on a carbon-modified electrode according to the present invention is shown.

[0027] Figure 2 A flowchart illustrating the steps of a method for dynamically assessing microbial populations based on carbon-modified electrodes according to Embodiments 1 and 2 of the present invention is shown.

[0028] Figure 3 A schematic diagram of the structure of a carbon-based modified electrode according to Embodiment 1 of the present invention is shown.

[0029] Figure 4 A schematic diagram showing different survival states of SRB on the surface of a carbon-based modified electrode after testing according to Embodiment 1 of the present invention is shown.

[0030] Explanation of reference numerals in the attached figures: 1. Porous carbon substrate; 1-1. Porous substrate surface; 2. PEDOT; 3. Microorganisms; 4. Nafion layer. Detailed Implementation

[0031] The invention will now be described in more detail with reference to the accompanying drawings. While preferred embodiments of the invention are shown in the drawings, it should be understood that the invention can be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that the invention will be thorough and complete, and will fully convey the scope of the invention to those skilled in the art.

[0032] like Figure 1 As shown, a method for dynamic assessment of microbial populations based on a carbon-modified electrode according to the present invention includes: Prepare carbon-based modified electrodes doped with target microorganisms; A carbon-based modified electrode was used as the working electrode, forming a three-electrode system with a reference electrode and a counter electrode. The system was placed in a test medium containing the target microorganism for electrochemical scanning, and the electrochemical response signal was collected. The electrochemical response signal was processed to obtain the characteristic electrochemical parameters of the target microbial metabolites; The model is selected based on the preset detection duration threshold: when the detection time is less than or equal to the preset duration, a linear model is used; when the detection time is greater than the preset duration, a dynamic metabolic model is used. By substituting the characteristic electrochemical parameters into the selected model, the number of microorganisms in the test medium can be calculated.

[0033] Specifically, this invention first prepares a carbon-based modified electrode doped with target microorganisms. This electrode, using a porous carbon substrate and prepared via chemical vapor deposition, possesses a high specific surface area, providing an excellent interface for microbial loading and electron transfer. On the surface of the porous carbon substrate, a conductive polymer-microorganism composite film doped with target microorganisms is formed in situ using electrochemical polymerization. Specifically, the pretreated porous carbon substrate is used as the working electrode and placed in an electrolyte containing EDOT (3,4-ethylenedioxythiophene) monomer, a supporting electrolyte, and a suspension of target microorganisms. Electropolymerization is then carried out under constant potential conditions. During polymerization, the PEDOT chains formed by the oxidative polymerization of EDOT monomer are positively charged and electrostatically attract the negatively charged microorganisms, embedding the microorganisms as dopants within the growing PEDOT network, forming a stable PEDOT-microorganism composite film. Subsequently, an anti-interference layer, preferably a Nafion (perfluorosulfonic acid resin) membrane, is modified onto the surface of the composite membrane. A dense thin film is formed by spin coating or electrochemical deposition. Utilizing its ion-selective permeability, this layer effectively shields the non-specific electrochemical responses of interfering substances such as phosphates and chloride ions in the test medium, significantly improving the signal-to-noise ratio. This carbon-based modified electrode integrates efficient microbial immobilization, enhanced electron transfer, and anti-interference capabilities.

[0034] Next, the prepared carbon-based modified electrode was used as the working electrode, forming a three-electrode system with a reference electrode (e.g., Ag / AgCl) and a counter electrode (e.g., platinum wire). This system was then placed in a test medium containing the target microorganism for electrochemical scanning. Based on the redox characteristics of the target microorganism's metabolites, an appropriate scanning potential range and scan rate were set, and electrochemical response signals were acquired using cyclic voltammetry or differential pulse voltammetry. An appropriate settling time could be provided before scanning to allow the electrode surface to reach a steady state. During the scanning process, the curve of current versus potential was recorded, thus obtaining the electrochemical response signal generated by the microbial metabolic activity.

[0035] The acquired electrochemical response signals are then processed to obtain characteristic electrochemical parameters of the target microbial metabolites. First, the raw signal is filtered, preferably using Savitzky-Golay filtering to remove high-frequency noise and preserve characteristic peak shapes. Then, characteristic peak current values ​​(suitable for cyclic voltammetry) and / or characteristic peak integral areas (suitable for differential pulse voltammetry) are extracted from the filtered signal. For cyclic voltammetry, the characteristic peak current value directly reflects the instantaneous redox activity of the metabolites; for differential pulse voltammetry, the characteristic peak integral area characterizes the total amount of metabolites participating in the reaction over a certain time period. Both can serve as characteristic electrochemical parameters related to the microbial population.

[0036] After obtaining the characteristic electrochemical parameters, this invention selects an appropriate quantitative model based on a preset detection duration threshold. This threshold can be preset according to actual detection needs, such as 1 hour or 2 hours. When the detection time is less than or equal to the preset duration, it indicates that the microbial metabolism is in a relatively steady state, and a linear model is used for calculation. When the detection time is greater than the preset duration, the microbial metabolism may undergo dynamic changes (such as growth or decay), and a dynamic metabolic model is used for evaluation. This dual-model adaptation strategy satisfies the need for rapid screening while ensuring the accuracy of long-term monitoring.

[0037] Finally, the extracted characteristic electrochemical parameters are substituted into the selected model to calculate the number of microorganisms in the test medium.

[0038] In one example, such as Figure 2 As shown, the carbon-based modified electrode includes, from bottom to top: Porous carbon substrate; A conductive polymer-microbe composite membrane formed on the surface of a porous carbon substrate, wherein the composite membrane is a PEDOT membrane doped with target microorganisms; An anti-interference layer is formed on the surface of the conductive polymer-microbial composite membrane, and the anti-interference layer is a Nafion membrane.

[0039] Specifically, the carbon-based modified electrode adopts a three-layer structure design from bottom to top, namely a porous carbon substrate, a conductive polymer-microbial composite membrane, and an anti-interference layer. The layers work together to achieve efficient microbial immobilization, signal enhancement, and anti-interference detection.

[0040] The bottom layer is a porous carbon substrate, preferably prepared by chemical vapor deposition using methane as the carbon source. The substrate is deposited at high temperatures to form a carbon material with a nanoporous structure, achieving a specific surface area of ​​over 1200 m² / g. This high specific surface area nanoporous structure not only provides ample attachment sites for the subsequent conductive polymer-microbe composite membrane but also offers an efficient conduction pathway for electron transfer during the electrode reaction process. The porous carbon substrate itself possesses excellent conductivity, chemical stability, and biocompatibility, effectively supporting the upper structure and maintaining the overall stability of the electrode.

[0041] A conductive polymer-microorganism composite membrane, doped with target microorganisms, is formed in situ on the surface of a porous carbon substrate via electrochemical polymerization. During preparation, the pretreated porous carbon substrate is used as the working electrode and placed in an electrolyte containing EDOT monomers, a supporting electrolyte, and a suspension of the target microorganisms. Electropolymerization is carried out under constant potential conditions. During polymerization, the PEDOT chains formed by the oxidative polymerization of EDOT monomers are positively charged in the oxidized state, attracting the negatively charged microorganisms on the surface through electrostatic attraction. This allows the microorganisms to be uniformly embedded as dopants within the growing PEDOT network. This in-situ embedding method not only achieves efficient immobilization of microorganisms on the electrode surface, preventing their detachment during detection, but also provides a direct conduction channel for electrons generated by microbial metabolism through the PEDOT conductive polymer network, significantly enhancing the intensity and stability of the electrochemical signal. Furthermore, the PEDOT membrane itself exhibits good biocompatibility, maintaining the activity of the microorganisms during detection and ensuring that the detection results reflect the true metabolic state of the microorganisms.

[0042] An anti-interference layer, a perfluorosulfonic acid resin membrane, is further modified onto the surface of the conductive polymer-microbial composite membrane. Nafion is a perfluorosulfonic acid resin material with ion-selective permeability. The sulfonic acid groups in its molecular structure selectively allow cations to pass through while effectively blocking anionic interferences (such as phosphate and chloride ions) from approaching the electrode surface. The Nafion membrane can be uniformly coated onto the composite membrane surface using spin coating or electrochemical deposition to form a dense film with controllable thickness. The function of this layer is twofold: firstly, by utilizing its nanoscale pore size and ion exchange characteristics, it effectively shields the electrode surface from interference by non-specific electroactive substances in the test medium, significantly reducing background noise and improving the signal-to-noise ratio to over 20 dB; secondly, the Nafion membrane has good film-forming properties and chemical stability, protecting the microorganisms in the inner composite membrane from direct impact from complex external media, while not affecting the diffusion of small molecule electroactive substances (such as certain reducing metabolites) produced by microbial metabolism to the electrode surface.

[0043] The three-layer structure is tightly integrated from bottom to top: the porous carbon substrate provides a high specific surface area framework and conductive pathways; the PEDOT-microbial composite membrane enables active immobilization of microorganisms and enhances electron transfer; and the Nafion anti-interference layer endows the electrode with excellent resistance to interference from complex media. This structural design systematically solves the technical problems of unstable microbial immobilization, weak signals, and susceptibility to interference in existing microbial detection technologies, laying a solid foundation for the accurate acquisition of subsequent electrochemical signals and precise quantification of microbial numbers.

[0044] In one example, the conductive polymer-microbial composite membrane is prepared via the following electropolymerization process: The pretreated porous carbon substrate was used as the working electrode and placed in an electrolyte; the electrolyte contained EDOT monomer, supporting electrolyte and suspension containing target microorganisms; Polymerization was carried out at a constant potential of 1.0V to 1.2V (vs. Ag / AgCl) for 800s to 1000s. During the polymerization process, the PEDOT chains formed were positively charged in the oxidized state and were electrostatically attracted to the negatively charged microorganisms on the surface. This allowed the microorganisms to be embedded in the growing PEDOT network as dopants, forming a conductive polymer-microorganism composite film.

[0045] Specifically, the conductive polymer-microbial composite membrane is prepared in situ via electropolymerization. This process uses a pretreated porous carbon substrate as the working electrode, placed in an electrolyte containing EDOT monomers, a supporting electrolyte, and a suspension of target microorganisms. In the electrolyte, the EDOT monomers serve as the precursor to the conductive polymer, the supporting electrolyte provides the ionic conductivity required for polymerization and maintains the electroneutrality of the solution, while the target microbial suspension represents the active biological component to be doped. Polymerization is performed at a constant potential of 1.0V–1.2V (vs. Ag / AgCl) for 800–1000 seconds. During this process, the EDOT monomers undergo oxidative polymerization on the surface of the working electrode, gradually forming PEDOT polymer chains. The polymerized PEDOT chains carry a positive charge in the oxidized state, while the microbial cell surface typically carries a negative charge due to the presence of functional groups such as carboxyl and phosphate groups. This results in a strong electrostatic attraction between the two. This electrostatic interaction causes the microorganisms, acting as negatively charged dopants, to be spontaneously embedded within the growing three-dimensional PEDOT network structure, forming a conductive polymer composite membrane with uniformly distributed microorganisms. This in-situ electropolymerization process not only achieves gentle immobilization of microorganisms on the electrode surface, avoiding cell damage that may be caused by traditional immobilization methods such as chemical cross-linking, but also provides a direct conduction pathway for electrons generated by microbial metabolism through the PEDOT conductive network, significantly enhancing the transmission efficiency of electrochemical signals. The resulting conductive polymer-microorganism composite membrane exhibits structural stability, high biocompatibility, and excellent electrochemical activity, laying a solid foundation for subsequent acquisition and quantitative analysis of microbial metabolic signals.

[0046] In one example, the concentration of EDOT monomer in the electrolyte was 8 mM–12 mM, the concentration of the supporting electrolyte was 0.05 M–0.15 M, and the concentration range of the microbial suspension was 10 mM. 2 CFU / mL ~10 7 CFU / mL; the supporting electrolyte is selected from LiClO4, NaCl or KCl.

[0047] Specifically, the formulation of the electrolyte has a crucial impact on the electropolymerization process of the conductive polymer-microbial composite membrane. The precise control of the concentration and type of each component directly determines the microstructure, electrochemical performance, distribution and activity of the microorganisms within the membrane.

[0048] The concentration of EDOT monomer in the electrolyte is preferably controlled within the range of 8 mM to 12 mM. As a precursor for forming a conductive polymer network, the concentration of EDOT directly affects the polymerization rate and the thickness and morphology of the polymer film. If the concentration is too low, the polymerization rate is slow, resulting in an insufficient thickness of the PEDOT film, making it difficult to effectively encapsulate and immobilize microorganisms. If the concentration is too high, the polymerization reaction is too vigorous, potentially leading to excessive polymer chain stacking, forming a dense structure that hinders electron transfer between microbial metabolites and the electrode surface, and may also cause mechanical compression or damage to the encapsulated microorganisms. Controlling the EDOT concentration within the range of 8 mM to 12 mM ensures a suitable polymerization rate while forming a PEDOT network with appropriate pore structure and conductivity, achieving uniform encapsulation and good immobilization of microorganisms.

[0049] The concentration of the supporting electrolyte in the electrolyte solution is preferably controlled within the range of 0.05M to 0.15M. The main function of the supporting electrolyte is to provide the ionic conductivity required during polymerization, maintain the electroneutrality of the solution, and influence the growth mode and microstructure of the polymer chains. When the supporting electrolyte concentration is too low, the solution conductivity is insufficient, the polymerization reaction is uneven, and it may lead to excessively high local current density on the electrode surface, affecting the uniformity of the composite membrane. When the supporting electrolyte concentration is too high, excessive ions may interfere with the adsorption and polymerization process of EDOT monomers on the electrode surface, and the high ionic strength may also cause osmotic pressure shock to microbial cells, affecting their activity. Controlling the supporting electrolyte concentration within the range of 0.05M to 0.15M can ensure good ionic conductivity while providing a suitable osmotic pressure environment for microorganisms, maintaining their metabolic activity.

[0050] The supporting electrolyte is selected from LiClO4, NaCl, or KCl, and the choice can be tailored to the specific needs of the target microorganism and the detection system. LiClO4, with its high dissociation constant and high conductivity, is beneficial for forming a dense and highly conductive PEDOT membrane. NaCl, as a physiological salt, has high biocompatibility and minimal impact on the activity of most microorganisms. KCl, on the other hand, excels in maintaining intracellular and extracellular ion balance. The choice of different supporting electrolytes not only affects the microstructure and electrochemical performance of the PEDOT membrane but may also influence the activity of microorganisms during electropolymerization through factors such as ionic strength and ion types. Therefore, optimization is necessary based on the specific target microorganism.

[0051] The concentration range of the microbial suspension in the electrolyte is preferably controlled within the range of 10² CFU / mL to 10² CFU / mL. 7The concentration range is between 10² CFU / mL. This range covers typical detection scenarios from low to high concentrations, ensuring the method's universality. If the microbial suspension concentration is too high, a large number of microorganisms will compete for limited embedding sites during electropolymerization, potentially leading to uneven distribution of microorganisms within the composite membrane, or even multilayer stacking, affecting electron transfer efficiency. If the microbial concentration is too low, the number of microorganisms immobilized on the electrode surface is insufficient, making it difficult to generate detectable characteristic electrochemical signals. The microbial suspension concentration is controlled between 10² CFU / mL and 10² CFU / mL. 7 Within the CFU / mL range, it can achieve uniform distribution and stable embedding of microorganisms in the PEDOT network while ensuring sufficient signal strength.

[0052] By precisely controlling the concentration of EDOT monomer, the concentration and type of supporting electrolyte, and the concentration of microbial suspension in the electrolyte, the formation of conductive polymer network and the encapsulation effect of microorganisms can be synergistically optimized during electropolymerization. This results in a conductive polymer-microorganism composite membrane with stable structure, excellent electrochemical activity, and uniform microbial distribution, providing an ideal working interface for the accurate acquisition and quantitative analysis of subsequent microbial metabolic signals.

[0053] In one example, the anti-interference layer is a Nafion film, which is modified onto the surface of a conductive polymer-microbial composite film by spin coating or electrochemical deposition. The process parameters for spin coating are: 5% w / w Nafion ethanol solution, spin speed 2500 rpm to 3000 rpm, and spin coating time 60 s to 90 s.

[0054] Specifically, the modification of the anti-interference layer is a key step in the preparation of carbon-based modified electrodes. Its purpose is to endow the electrode with tolerance to complex detection media and effectively shield non-specific interference signals, thereby ensuring the accuracy and reliability of subsequent electrochemical detection. This anti-interference layer uses Nafion film, which is modified onto the surface of a conductive polymer-microorganism composite membrane by spin coating or electrochemical deposition.

[0055] Nafion is a perfluorosulfonic acid resin material whose molecular structure consists of a hydrophobic polytetrafluoroethylene backbone and hydrophilic sulfonic acid side chains. This unique microscopic phase-separated structure endows it with excellent film-forming properties, chemical stability, and superior ion-selective permeability. The sulfonic acid groups in Nafion membranes can form nanoscale hydrophilic ion channels, allowing cations and water molecules to pass through while strongly repelling anions. This characteristic enables it to effectively block common anionic interferences in the analyte medium, such as phosphate ions and chloride ions, allowing them to approach the electrode surface and undergo non-specific electrochemical reactions, thereby significantly reducing background noise and improving the signal-to-noise ratio of the target metabolite characteristic signal.

[0056] Spin coating is one of the preferred methods for preparing Nafion anti-interference layers. Specific process parameters are as follows: A 5% w / w Nafion ethanol solution is used as the film-forming material. The solution is uniformly dropped onto the electrode surface with a conductive polymer-microbial composite membrane, followed by spin coating at 2500 rpm to 3000 rpm for 60 to 90 seconds. Within this speed range, centrifugal force drives the Nafion solution to rapidly spread and cover the entire electrode surface, while excess solution is ejected from the electrode, forming a uniform liquid film. As the ethanol solvent rapidly evaporates, Nafion solidifies on the electrode surface. The 2500 to 3000 rpm speed balances film uniformity and thickness control: too low a speed may result in an excessively thick or uneven film, affecting ion transport efficiency; too high a speed may result in an excessively thin film, reducing the anti-interference effect. The 60 to 90-second spin coating time ensures sufficient solvent evaporation, forming a dense and strongly adherent Nafion film. A 5% w / w Nafion ethanol solution has suitable viscosity and solid content, which can form a uniform film with moderate thickness and few defects during spin coating. This ensures sufficient ion selective permeability while avoiding the obstruction of the diffusion of microbial metabolites by an excessively thick film.

[0057] Electrochemical deposition, as another alternative method for modifying the anti-interference layer, involves applying a specific potential to electrodeposit Nafion from solution onto the surface of the composite film, forming a uniform and dense film. This method is suitable for electrode surfaces with complex shapes and can achieve more uniform coverage, but the process control is relatively complex. Spin coating, on the other hand, is simple to operate, has a fast film formation rate, and allows for controllable thickness, making it suitable for the mass production of planar or regularly shaped electrodes.

[0058] The modified Nafion anti-interference layer forms a dense protective barrier on the surface of the conductive polymer-microbial composite membrane. Its functions are multifaceted: First, by utilizing ion-selective permeability, it effectively blocks the diffusion of anionic interfering substances such as phosphates and chloride ions from the analyte to the electrode surface, suppressing their non-specific electrochemical responses and improving the detection signal-to-noise ratio to over 20 dB. Second, the nanoscale pore size of the Nafion membrane allows small-molecule electroactive substances (such as certain reducing metabolites) produced by microbial metabolism to pass smoothly to the electrode surface and undergo redox reactions, ensuring the normal acquisition of characteristic signals. Third, the Nafion membrane possesses excellent biocompatibility and chemical stability, protecting the microorganisms in the inner composite membrane from direct impact from complex external media and maintaining their activity and metabolic stability during the detection process.

[0059] By modifying the surface of a conductive polymer-microbe composite membrane with a Nafion anti-interference layer using spin coating or electrochemical deposition, effective shielding against non-specific interference signals in complex detection media was achieved, while maintaining normal mass transfer and detection of target microbial metabolites. The introduction of this anti-interference layer significantly improved the adaptability and reliability of the carbon-based modified electrode in the detection of complex real-world samples, providing strong support for accurate acquisition of subsequent electrochemical signals and precise quantification of microbial numbers.

[0060] In one example, a porous carbon substrate was prepared by chemical vapor deposition using methane as the carbon source and CH4:H2 volume ratio of 1:3. The substrate was deposited at 800℃~900℃ and 40kPa~60kPa for 1~2 hours to obtain a nanoporous structure with a specific surface area greater than 1200m² / g.

[0061] Specifically, the porous carbon substrate is the bottom layer structure of the carbon-based modified electrode. It is preferably prepared by chemical vapor deposition. By precisely controlling the process parameters, a nanoporous structure with a high specific surface area is obtained, which provides an ideal working interface for subsequent microbial loading and electron transfer.

[0062] In the chemical vapor deposition (CVD) process, methane is used as the carbon source, and hydrogen is used as both the carrier gas and the reducing gas. The two gases are mixed at a volume ratio of 1:3 and introduced into the reaction chamber. Methane decomposes under high temperature conditions, releasing carbon atoms that deposit on the substrate surface, gradually forming the carbon material framework. Hydrogen plays a role in regulating the decomposition rate of methane, inhibiting the excessive formation of amorphous carbon, promoting the formation of an ordered carbon structure, and simultaneously removing unstable amorphous carbon through etching, thereby improving the purity and graphitization degree of the deposited carbon layer. The CH4:H2 volume ratio of 1:3 is an optimized process parameter that balances the carbon source supply rate and the etching effect of hydrogen, ensuring a stable deposition process and forming a uniform, defect-free nanoporous carbon material.

[0063] The deposition temperature was controlled within the range of 800℃ to 900℃. Temperature is a key factor affecting the methane decomposition rate and carbon atom migration behavior. Within this temperature range, methane can fully decompose to produce carbon atoms, and these carbon atoms have sufficient migration ability on the substrate surface to arrange themselves in an orderly manner to form a stable carbon framework structure. If the temperature is too low, methane decomposition is incomplete, the deposition rate is slow, and the resulting carbon material has a loose structure and insufficient specific surface area; if the temperature is too high, it may lead to excessive graphitization of carbon atoms, causing the pore structure to collapse and reducing the specific surface area. The temperature range of 800℃ to 900℃ can ensure a sufficient deposition rate while obtaining nanoporous carbon materials with well-developed pore structures and high specific surface areas.

[0064] The reaction chamber pressure is controlled within the range of 40 kPa to 60 kPa. Pressure affects the mean free path and collision frequency of gas molecules, thus influencing the deposition behavior of carbon atoms on the substrate surface. Within this pressure range, gas molecules have a moderate concentration and diffusion rate, enabling them to be uniformly distributed on the substrate surface, forming a carbon layer of consistent thickness and uniform pore distribution. If the pressure is too low, the gas molecule concentration is insufficient, the deposition rate is slow, and the resulting carbon layer is too thin; if the pressure is too high, it may lead to gas-phase nucleation, producing a large number of carbon particles rather than a uniform thin film structure, affecting the structural integrity of the porous carbon substrate.

[0065] The deposition time is controlled within the range of 1 to 2 hours. The deposition time determines the thickness of the porous carbon layer and the degree of pore structure development. Within this time range, carbon atoms continuously deposit and self-assemble to form a three-dimensional nanoporous network. As time increases, the pore structure gradually develops and matures, and the specific surface area continuously increases. One hour is sufficient to form a basic nanoporous framework, while two hours results in a more developed pore structure and the maximum specific surface area. After two hours, an excessively thick deposition layer may clog surface pores, thus reducing the effective specific surface area.

[0066] Through the synergistic control of the aforementioned process parameters, a nanoporous carbon substrate with a specific surface area greater than 1200 m² / g was finally obtained. This high specific surface area originates from its well-developed three-dimensional nanoporous network, with pore sizes distributed within the nanoscale range, forming a large number of micropores and mesoporous structures. The high specific surface area has multiple functions: firstly, it provides sufficient attachment sites for the subsequent loading of conductive polymer-microorganism composite membranes, ensuring efficient immobilization of microorganisms on the electrode surface; secondly, the abundant pore structure increases the contact area between the electrode and the electrolyte, shortens the ion diffusion path, and improves the electrochemical reaction efficiency; thirdly, the nanoporous carbon framework itself has good conductivity, providing an efficient conduction path for electrons generated by microbial metabolism, significantly enhancing the acquisition efficiency of electrochemical signals.

[0067] The porous carbon substrate prepared by chemical vapor deposition under optimized process parameters has the characteristics of large specific surface area, well-developed pore structure and excellent conductivity. It provides a solid underlying support for carbon-based modified electrodes and lays an important foundation for the in-situ electropolymerization of conductive polymer-microbial composite membranes and the efficient immobilization and electron transfer of microorganisms.

[0068] The prepared porous carbon electrode was ultrasonically cleaned in 0.1M PBS buffer (pH 7.4) for 3 minutes and then dried with nitrogen gas for later use.

[0069] In one example, processing the electrochemical response signal includes: The collected electrochemical response signals are filtered. The characteristic peak current value and / or characteristic peak integral area are extracted from the filtered signal and used as characteristic electrochemical parameters.

[0070] Specifically, processing the acquired electrochemical response signals is a crucial intermediate step in the quantitative assessment of microbial abundance. Its core objective is to effectively extract characteristic information related to microbial metabolic activity from the raw signals and transform it into characteristic electrochemical parameters that can be used for model calculations. This processing mainly includes two steps: filtering and characteristic parameter extraction.

[0071] First, the acquired electrochemical response signal is filtered. During electrochemical detection, the original signal is inevitably affected by factors such as instrument electronic noise, environmental electromagnetic interference, and solution fluctuations, generating high-frequency noise components. This noise may mask or interfere with the characteristic signals of the target microbial metabolites, affecting the accuracy of subsequent quantitative analysis. To address this issue, this invention preferably employs the Savitzky-Golay filtering method to smooth the original signal. Savitzky-Golay filtering is a convolutional smoothing algorithm based on local polynomial fitting. Its advantage lies in its ability to effectively remove high-frequency noise while better preserving the peak shape, peak width, and peak height of the original signal, avoiding signal distortion that may be caused by traditional smoothing methods. The signal-to-noise ratio of the filtered signal is significantly improved, and the redox characteristic peaks of the metabolites are more clearly distinguishable, laying a good foundation for the accurate extraction of subsequent characteristic parameters.

[0072] Subsequently, characteristic peak current values ​​and / or characteristic peak integrated areas are extracted from the filtered signal as characteristic electrochemical parameters. The characteristic peak current value typically corresponds to the peak current of the oxidation or reduction peak in the cyclic voltammetry curve, reflecting the instantaneous current response intensity when microbial metabolites undergo redox reactions at a specific potential. This parameter is directly proportional to the concentration of electroactive substances on the electrode surface. Therefore, in short-term detection scenarios where the microbial metabolic state is relatively stable, the characteristic peak current value can directly reflect the amount of metabolites participating in the reaction, thus indirectly indicating the number of microorganisms. Accurate identification of the potential location of the characteristic peak is crucial during extraction, and the corresponding current value must be recorded. Baseline correction can be performed if necessary to improve accuracy.

[0073] The integral area of ​​the characteristic peak corresponds to the area enclosed by the characteristic peak and the baseline within a certain potential range in the differential pulse voltammetry curve, representing the total charge involved in the redox reaction during the scan. Compared to the peak current value, the integral area comprehensively considers the duration and intensity of the reaction, providing a more comprehensive reflection of the cumulative effect of microbial metabolism, and is particularly suitable for situations where the number of microorganisms changes dynamically in long-term monitoring scenarios. During integration, the onset and termination potentials of the characteristic peak must first be determined (usually within ±50mV of the peak potential). The current-potential curve is then integrated within this range to obtain the charge value Q in microcoulombs.

[0074] The selection of characteristic peak current value and characteristic peak integral area can be flexibly determined according to actual detection needs: when using cyclic voltammetry for rapid detection, the characteristic peak current value is preferentially extracted as the input parameter of the linear model; when using differential pulse voltammetry for long-term dynamic monitoring, the characteristic peak integral area is preferentially extracted as the input parameter of the dynamic metabolic model. Both characteristic parameters can accurately reflect the intensity of microbial metabolic activity and have a clear mathematical correlation with the number of microorganisms.

[0075] Through the filtering and feature parameter extraction steps described above, the original electrochemical response signal is transformed into characteristic electrochemical parameters with clear physical meaning. This process removes noise interference and highlights the target information, providing accurate and reliable input data for subsequent calculation of microbial numbers based on the selected model. The design of this signal processing workflow balances detection efficiency and data quality, and is a crucial technical guarantee for the accurate quantitative assessment of microorganisms in this invention.

[0076] In one example, the expression for the linear model is: ; ; in, Microbial count (CFU / mL); Characteristic peak current (μA); Sensitivity coefficient (μA / CFU·mL) - ¹); This represents the background current (μA).

[0077] Specifically, the linear model is one of the core mathematical models used in this invention for rapid quantitative assessment of microbial numbers, and is suitable for scenarios with short detection times and relatively stable microbial metabolic states. This model establishes a linear relationship between characteristic peak current and microbial numbers, expressed as: From this, the formula for calculating the number of microorganisms can be derived: This model is simple in form and easy to calculate, enabling rapid quantification of microbial numbers.

[0078] In the model, This represents the number of microorganisms in the test medium, expressed in CFU / mL (colony forming units per milliliter), and is the final target value that needs to be determined. The characteristic peak current, measured in microamperes (μA), is a characteristic electrochemical parameter extracted after electrochemical scanning and filtering. In cyclic voltammetry, the characteristic peak current typically corresponds to the peak current value generated when microbial metabolites undergo redox reactions at a specific potential. Its magnitude directly reflects the concentration of metabolites involved in the reaction, and is thus correlated with the number of active microorganisms immobilized on the electrode surface.

[0079] This is the sensitivity coefficient, measured in microamps per CFU·mL. - ¹(μA / CFU·mL) - ¹), is a key parameter characterizing the sensitivity of an electrode to changes in microbial abundance. This coefficient is obtained through calibration using standard concentration bacterial suspensions: a series of standard microbial suspensions of known concentrations (typically covering 10) are prepared. 2 CFU / mL ~10 6 (CFU / mL range), electrochemical scanning was performed using the same carbon-based modified electrode under the same detection conditions, and the characteristic peak current values ​​corresponding to each concentration were recorded, based on the microbial quantity. The horizontal axis represents the characteristic peak current. Plot a standard curve on the ordinate, and obtain the slope of the curve through linear fitting using the least squares method, which is the sensitivity coefficient. . The value is affected by factors such as electrode material, type of microorganism, and detection conditions. Once calibrated in a specific detection system, it can be used for the quantitative detection of unknown samples under the same conditions.

[0080] Background current, measured in microamps (μA), refers to the background current response measured by a blank electrode under the same scanning conditions in the absence of microorganisms. The background current originates from the capacitive current of the electrode material itself, the weak response of residual electroactive impurities in the electrolyte, and the inherent noise of the instrument system. Accurate measurement... This is crucial for removing background interference and obtaining the net signal generated by microbial metabolism. The measurement method is as follows: using a blank electrode identical to the detection electrode but without microbial doping, electrochemical scanning is performed under the same electrolyte and scanning parameters. The current value at the characteristic peak potential is recorded as the result. Substitute into the model for calculation.

[0081] The characteristic peak current obtained from the actual sample detection Subtract the pre-measured background current The net current signal contributing to microbial metabolism is obtained; then the net current signal is divided by the sensitivity coefficient. The number of microorganisms in the test medium can then be calculated. This calculation process enables the quantitative conversion from electrochemical signals to the number of microorganisms.

[0082] The linear model is applicable when the detection time is short (usually less than a preset time, such as 1 hour), the microbial metabolic state remains relatively stable, and there is a good linear relationship between the rate of metabolite production and the number of microorganisms. This model is suitable for rapid screening scenarios and can meet the needs of rapid on-site detection.

[0083] The advantages of linear models lie in their simplicity, rapid calculation, and convenient parameter calibration, making them particularly suitable for rapid screening of large batches of samples and on-site real-time detection applications. However, this model also has its limitations: when the detection time is long and significant changes occur in microbial metabolism (such as growth, decay, or activity fluctuations), the linear relationship between characteristic peak current and microbial quantity may deviate. In such cases, a dynamic metabolic model is needed to obtain more accurate quantitative results. Therefore, this invention combines linear and dynamic metabolic models, flexibly selecting the appropriate model based on the detection timescale, ensuring both the efficiency of short-term detection and the accuracy of long-term monitoring.

[0084] In one example, the expression for the dynamic metabolic model is: ; After integration, we get: ; ; in, Microbial count (CFU / mL); The integral area of ​​the characteristic peak (μC); Maximum metabolic rate of microorganisms (h - ¹); Metabolic decay constant (h) - ¹); The detection time is in hours (h). The background integral area (μC) is the area without microorganisms.

[0085] Specifically, the dynamic metabolic model is one of the core mathematical models used in this invention for dynamic assessment of microbial populations. It is suitable for scenarios with long detection times and significant changes in microbial metabolic states. Based on the principles of microbial metabolic kinetics, this model establishes a functional relationship between the integral area of ​​characteristic peaks and time, achieving accurate quantification of microbial populations by fitting the dynamic metabolic process. The differential form of the model is: After integration, we get This leads to the derivation of a formula for calculating the number of microorganisms. This model fully considers the dynamic changes in microbial metabolic activity and can more accurately reflect the true number of microorganisms during long-term monitoring.

[0086] In differential equations center, left end This represents the rate of change of the integral area of ​​the characteristic peak over time, i.e., the amount of charge generated by microbial metabolism per unit time, reflecting the instantaneous change in metabolic rate. (Right end) The theoretical maximum metabolic rate at the initial moment is given by: The number of microorganisms in the test medium. This represents the maximum metabolic rate of microorganisms, expressed in hours (h). - ¹, characterizing the ability of a unit number of microorganisms to generate charge per unit time under optimal conditions. It is an exponential decay factor. This is the metabolic decay constant, in hours. - ¹ reflects the rate at which metabolic activity decays over time. This differential equation describes a dynamic process in which the microbial metabolic rate approaches its maximum value in the initial stage of detection. Over time, due to nutrient consumption, accumulation of metabolic products, or natural decline in microbial activity, the metabolic rate gradually decreases exponentially, with the rate of decline increasing from... Decide.

[0087] Integrating the differential equation, we obtain This integral form expresses the functional relationship between the cumulative charge Q generated by microbial metabolism and time t from the start of detection to time t. When t approaches zero, Q approaches zero; when t approaches infinity, Q approaches... This represents the theoretical maximum accumulated charge. The curve exhibits a typical exponential saturation pattern: Q increases rapidly initially, then the growth rate gradually slows down, eventually plateauing. This characteristic closely matches the metabolic accumulation process of actual microorganisms in a closed system, validating the model's rationality.

[0088] The formula for calculating the number of microorganisms can be derived from the integral expression. Where Q is the integral area of ​​the characteristic peak obtained from the actual sample detection, in microcoulombs, which is a characteristic electrochemical parameter obtained by differential pulse voltammetry and after filtering and integration. The background integral area in the absence of microorganisms is obtained by measuring the blank electrode under the same detection conditions and is used to subtract the system background contribution; t is the detection time in hours. The measured Q value is then subtracted from the background area. The cumulative charge amount resulting from the net contribution of microbial metabolism is obtained, and then divided by... The number of microorganisms N can be calculated from this item.

[0089] Dynamic parameters in the model and Calibration needs to be performed through preliminary experiments. The maximum metabolic rate of microorganisms, reflecting their metabolic potential under optimal conditions, can be obtained by fitting the growth curve (e.g., OD600 value versus time) of the microorganism during the logarithmic growth phase. The growth rate during the logarithmic growth phase can be determined by correlating it with parallel electrochemical signals. The value. The metabolic decay constant characterizes the rate at which metabolic activity decays over time. It can be determined by exponential fitting of a long-term DPV signal decay curve: under a fixed number of microorganisms, the change in the integral area of ​​the characteristic peak over time is continuously monitored, and the decay constant is obtained by exponential fitting of the decay phase. Once these two parameters are calibrated in a specific detection system, they can be used as fixed values ​​for the quantitative detection of unknown samples under the same conditions.

[0090] The advantage of dynamic metabolic models lies in their ability to accurately describe the dynamic changes in microbial metabolism during long-term monitoring. In real-world monitoring scenarios, microorganisms may undergo processes such as nutrient consumption, metabolite accumulation, and natural activity decline over time, leading to changes in metabolic rates. Traditional linear models cannot reflect these dynamic changes and can introduce biases during long-term monitoring. Dynamic metabolic models address this by introducing a maximum metabolic rate. and attenuation constant It accurately characterizes the dynamic features of metabolic processes and transforms the nonlinear relationship between accumulated charge Q and microbial number N into a computable function, significantly improving the quantitative accuracy in long-term monitoring scenarios.

[0091] The dynamic metabolic model and the linear model together constitute the dual-model quantitative system of this invention: the linear model is suitable for short-term steady-state detection, is simple in form, and is quick to calculate; the dynamic metabolic model is suitable for long-term dynamic monitoring, providing detailed characterization and high accuracy. Both can be flexibly selected based on preset detection duration thresholds, satisfying the need for rapid screening while ensuring accuracy for long-term monitoring, significantly improving the universality and practicality of the method of this invention.

[0092] In one example, the filtering process is Savitzky-Golay filtering, and the filtering parameters include window width and polynomial order; The window width is 15, and the polynomial order is 3.

[0093] Specifically, Savitzky-Golay filtering is the core filtering method used in this invention for preprocessing electrochemical response signals. Its purpose is to effectively remove high-frequency noise components from the original signal while preserving the morphological information of characteristic peaks of microbial metabolites to the maximum extent, providing a high-quality signal foundation for the accurate extraction of subsequent characteristic parameters. The specific parameters of this filtering method are set to a window width of 15 and a polynomial order of 3. This parameter combination was optimized and determined based on a balance between noise suppression and peak shape preservation.

[0094] Savitzky-Golay filtering is a convolutional smoothing algorithm based on local polynomial fitting. Its basic principle is to move a fixed-width window point-by-point across the original signal, perform low-order polynomial least-squares fitting on the data points within the window, and use the value of the fitted polynomial at the center point of the window as the smoothed output value. Compared to traditional moving average filtering, the core advantage of Savitzky-Golay filtering lies in its ability to better preserve the high-frequency characteristics of the signal, such as peak height, width, and shape, while smoothing noise, avoiding signal distortion caused by over-smoothing. This characteristic is particularly important for electrochemical detection signal processing, because the characteristic peak shape, position, and width of microbial metabolites contain crucial qualitative and quantitative information; any distortion can affect the accuracy of subsequent analysis.

[0095] The window width in the filtering parameters is set to 15. The window width determines the number of data points included in each fit, and is one of the key parameters controlling the filtering effect. When the window width is small, the filtered signal is close to the original signal, and the noise suppression effect is limited; when the window width is large, more data points participate in the fitting, and the smoothing effect is enhanced, but it may lead to over-smoothing of the true peak shape, causing signal distortion such as reduced peak height and broadened peak width. Setting the window width to 15 is a balance point determined through experimental optimization under typical electrochemical scan rates and data sampling frequencies: this width can cover enough data points to achieve effective noise suppression, while not over-smoothing the fine structure of metabolite characteristic peaks, ensuring the accuracy of peak current value and peak integral area extraction.

[0096] The polynomial order in the filtering parameters is set to 3. The polynomial order determines the complexity of the polynomial function used to fit the data points within the window. A low order (such as 0th or 1st order) is equivalent to local constants or linear fitting, offering strong smoothing capabilities but potentially failing to accurately fit peak regions with curvature variations; a high order may overfit noise, losing its smoothing effect. Setting the polynomial order to 3 means using a cubic polynomial to fit the data within the window. Cubic polynomials have good curve fitting capabilities, accurately describing the single-peak or multi-peak morphology of electrochemical characteristic peaks, smoothing noise while preserving the true shape and position of the peaks. This order selection, combined with the window width of 15, ensures sufficient fitting freedom while avoiding the risk of overfitting.

[0097] In practical applications, the Savitzky-Golay filtering process is as follows: First, the original electrochemical response signal (such as a cyclic voltammetry curve or a differential pulse voltammetry curve) is input as a data sequence sampled at equal potential intervals. Then, starting from the beginning of the data sequence, 15 consecutive data points are taken sequentially to form a window, and a cubic polynomial least squares fit is performed on these points. The calculated value of the fitted polynomial at the center point of the window replaces the original value as the filtered output. Subsequently, the window is moved forward by one data point, and the above process is repeated until the entire data sequence is covered. Edge data points, which cannot form a complete window, can be handled by symmetrical filling or gradually reducing the window size to ensure that the length of the output sequence is consistent with that of the input sequence.

[0098] After Savitzky-Golay filtering, high-frequency random noise in the original signal is effectively suppressed, and the signal-to-noise ratio is significantly improved. Simultaneously, the peak position, height, and shape of the characteristic peaks of microbial metabolites are well preserved. The filtered signal curve is smoother, and the boundary between the characteristic peaks and the baseline is clearer, laying a reliable foundation for subsequent location extraction of characteristic peak current values ​​and calculation of the characteristic peak integral area. If the filtering effect is not ideal, manifested as excessive noise residue or peak shape distortion, it can be optimized by fine-tuning the window width and polynomial order. However, the combination of a window width of 15 and a polynomial order of 3 has been experimentally verified to be suitable for most microbial electrochemical detection scenarios, exhibiting good universality and stability.

[0099] This filtering step plays a crucial role in the entire detection process: upstream, it receives the raw response signal acquired by the electrochemical workstation, and downstream, it provides high-quality input data for feature parameter extraction. Through precise processing by the Savitzky-Golay filter, the effective information in the raw signal is preserved to the maximum extent and clearly presented, while noise interference is minimized and eliminated. This ensures the accurate representation of the characteristic peak current values ​​upon which subsequent linear or dynamic metabolic model calculations depend. The accuracy and reliability of the integral area Q of the characteristic peak provide an important guarantee for the precise quantitative assessment of the final number of microorganisms.

[0100] In one example, cyclic voltammetry or differential pulse voltammetry is used for scanning.

[0101] Specifically, scanning using cyclic voltammetry or differential pulse voltammetry is the core step in electrochemical signal acquisition in this invention. Its purpose is to stimulate redox reactions of microbial metabolites on the electrode surface by applying a specific potential scanning program, and to record the corresponding current response signals, providing raw data for subsequent quantitative analysis of microbial numbers. Both methods have their own characteristics and can be flexibly selected according to detection requirements, target microbial species, and time scale.

[0102] Cyclic voltammetry is a widely used electrochemical scanning technique. Its basic principle is to apply a linearly varying potential scanning signal to the working electrode, scanning from the initial potential at a constant rate to the termination potential, and then scanning back to the initial potential at the same rate, forming a complete triangular potential cycle. During the scanning process, the curve of current changing with potential is recorded, i.e., the cyclic voltammetric curve. The curve is characterized by the following: during the forward scan, when the potential reaches the oxidation potential of the microbial metabolite, an oxidation reaction occurs, producing an oxidation peak; during the reverse scan, the oxidized product is reduced, producing a reduction peak. By analyzing parameters such as the oxidation peak potential, reduction peak potential, peak current value, and peak potential difference, the redox characteristics, reaction reversibility, and concentration information of the metabolite can be obtained. The advantage of cyclic voltammetry is its large information capacity; a single scan can simultaneously obtain complete information on both oxidation and reduction processes, making it suitable for preliminary exploration of microbial metabolic characteristics, optimization of detection conditions, and short-term rapid detection scenarios. In this embodiment of the invention, for the rapid quantitative detection of Escherichia coli, cyclic voltammetry was used to scan at a scan rate of 50 mV / s in the potential range of -0.3V to +0.4V. The characteristic oxidation peak was successfully identified at +0.25V, and the peak current value was extracted and substituted into the linear model to calculate the number of microorganisms.

[0103] The differential pulse voltammetry method superimposes a series of fixed-amplitude pulse potentials onto a linear stepwise scan, sampling the current before and after each pulse and calculating the difference as the output signal. Specifically, a linearly increasing base potential in a stepwise manner is applied to the working electrode, with a constant-amplitude pulse potential superimposed on each step. The current is measured before and after each pulse, and the difference between the two measurements is plotted against the base potential to obtain the differential pulse voltammetry curve. This curve is characterized by a symmetrical peak shape, a linear relationship between peak height and electroactive substance concentration, and peak potentials corresponding to the characteristic oxidation or reduction potentials of metabolites. The core advantages of differential pulse voltammetry are high sensitivity and excellent background current subtraction. The differential approach effectively reduces interference from capacitive current and background noise, significantly improving the signal-to-noise ratio, making it particularly suitable for the detection of low-concentration microbial metabolites and long-term dynamic monitoring scenarios. In this embodiment of the invention, for the 4-hour dynamic monitoring of sulfate-reducing bacteria, the differential pulse voltammetry was used to scan in the range of -0.6V to +0.3V, and the characteristic peak signal at +0.15V was successfully acquired. The characteristic peak area Q was obtained by integration and substituted into the dynamic metabolic model to calculate the number of microorganisms.

[0104] The parameter settings for both methods need to be optimized based on the target microorganism species and the detection system. The potential range should cover the characteristic redox potentials of the target microorganism's metabolites, usually determined by pre-scanning pure bacterial solutions to ensure that characteristic peaks appear completely within the scanning range. The choice of scanning rate affects signal intensity and resolution: too high a rate may lead to increased double-layer charging current and wider peaks; too slow a rate will prolong the detection time and may miss metabolic dynamics. In this invention, for sulfate-reducing bacteria with slow metabolic rates, a scanning rate of 5mV / s to 20mV / s is preferred, with 10mV / s used in a specific embodiment; for highly metabolically active Escherichia coli, a scanning rate of 50mV / s can be used. The setting of the settling time is also crucial. A settling time of 30 to 180 seconds should be set before the scan begins to allow the electrode surface to reach a stable state and reduce the impact of initial current fluctuations on the signal.

[0105] The two methods in this invention have a clear division of labor and cooperation: Cyclic voltammetry, due to its rich information and simple operation, is suitable for verifying electrode preparation quality, preliminary identification of metabolite characteristics, and short-term rapid detection scenarios; Differential pulse voltammetry, due to its high sensitivity and low background interference, is suitable for detecting low-concentration samples and long-term dynamic monitoring scenarios requiring precise integration to calculate the characteristic peak area. In the actual detection process, the method can be selected according to the preset detection time threshold: when the detection time is short and results need to be obtained quickly, cyclic voltammetry is preferred to acquire signals, and the peak current value is extracted and substituted into the linear model; when the detection time is long and metabolic dynamic changes need to be tracked, differential pulse voltammetry is preferred to acquire signals, and the peak integral area is extracted and substituted into the dynamic metabolic model.

[0106] Regardless of the scanning method used, the acquired raw electrochemical response signal must undergo subsequent filtering and feature parameter extraction steps to ultimately transform it into characteristic electrochemical parameters that can be used for model calculations. The organic combination of these two methods enables this invention to adapt to the detection needs of different microbial species, concentration ranges, and time scales, significantly improving the method's universality and practicality.

[0107] The present invention will be further described below with reference to the accompanying drawings and specific embodiments, but this is not intended to limit the invention. It should be noted that, unless otherwise specified, the embodiments and features described in the embodiments of the present invention can be combined with each other.

[0108] Example 1

[0109] like Figure 2 As shown, this embodiment provides a method for dynamic assessment of microbial numbers based on carbon-modified electrodes, used to detect the number of SRB (sulfate-reducing bacteria) in oilfield produced water. The method includes: 1. Sample pretreatment Take 500 mL of produced water from the oilfield and filter it through a 0.22 μm filter membrane to remove suspended particles; After centrifuging the filtrate at 4000 rpm for 10 minutes under anaerobic conditions (nitrogen protection), it was resuspended in sterile 0.1M PBS buffer (pH 7.4) for later use.

[0110] 2. Preparation of carbon-based modified electrodes

[0111] Substrate preparation: The porous carbon electrode with a pore size of approximately 100 nm, which has been prepared by chemical vapor deposition, was placed in 0.1 M PBS buffer (pH 7.4) and ultrasonically cleaned for 3 minutes, and then dried with nitrogen gas for later use.

[0112] In-situ polymerization and doping: The pretreated porous carbon electrode was used as the working electrode and placed in a nitrogen-protected electrolytic cell along with an Ag / AgCl reference electrode and a platinum wire counter electrode. The electrolyte was a mixture of 10 mM PEDOT monomer, 0.1 M LiClO4 (supporting electrolyte), and pretreated SRB suspension. Polymerization was carried out at a constant potential of 1.0 V (vs. Ag / AgCl) for 900 s, forming a PEDOT / SRB composite film with a thickness of approximately 0.8 μm on the working electrode surface. After polymerization, the electrode surface was gently rinsed with ultrapure water to remove physically adsorbed impurities.

[0113] Anti-interference layer modification: The electrode with the PEDOT / SRB composite film was fixed on a spin coater, and 5% (w / w) Nafion ethanol solution was added. The electrode was spin-coated at 3000 rpm for 60 seconds, and then allowed to evaporate and dry at room temperature to form a complete carbon-based modified electrode. Figure 3 As shown.

[0114] 3. Electrochemical signal acquisition

[0115] Detection system: Three-electrode system (working electrode: carbon-based modified electrode; reference electrode: Ag / AgCl; counter electrode: platinum wire); Detection conditions: The electrode was placed in a phosphate buffer solution containing 10 mM sulfate (pH 7.0), and cyclic voltammetry was performed in the range of -0.6 V to +0.3 V (scan rate 10 mV / s).

[0116] Signal processing flow: Baseline correction: Fitting the background current curve; Characteristic peak identification: Identify redox characteristic peaks of SRB metabolites in the range of +0.15V ± 0.05V; Data filtering: Apply Savitzky-Golay filtering (window width 15, polynomial order 3) to eliminate high-frequency noise; Integral calculation: Integrate within the range of ±50mV (i.e. +0.1V to +0.2V) of the characteristic peak potential to obtain the Q value.

[0117] 4. Model Calculation

[0118] To compare the goodness of fit between the linear and dynamic models, rapid detection results were compared over 4 hours and 30 minutes, respectively.

[0119] The 4-hour test results are calculated using a dynamic model: Parameters determined: μ max = 0.45 h - ¹(Obtained by fitting the OD600 growth curve of SRB during the logarithmic growth phase); k d = 0.12 h - ¹(Determined by exponential fitting of the 48-hour DPV signal attenuation curve); Q 0 = 0.08μC (baseline integral area of ​​blank electrode without SRB); = ;

[0120] Table 1. Comparison of Dynamic Model Calculation Results and Plate Count Results

[0121] Table 1 lists three oilfield produced water samples with different concentration gradients. The dynamic model of this invention was used to calculate the microbial counts, while the traditional plate count method was used as a control. The results of the two methods were highly consistent.

[0122] Looking at the specific data, the dynamic model calculation result for sample 1 was 1.2 × 10² CFU / mL, and the plate count result was 1.1 × 10² CFU / mL, with an absolute deviation of only 0.1 × 10² CFU / mL and a relative deviation controlled within 10%. The dynamic model calculation result for sample 2 was 3.6 × 10³ CFU / mL, and the plate count result was 3.7 × 10³ CFU / mL, with an absolute deviation of 0.1 × 10³ CFU / mL and a relative deviation of approximately 2.7%. The dynamic model calculation result for sample 3 was 9.9 × 10² CFU / mL. 4 CFU / mL, plate count result: 1.0 × 10⁻⁶ 5 CFU / mL, absolute deviation 0.1×10 5 CFU / mL, with a relative deviation of 10%. The detection results for the three concentration gradients all showed good consistency, indicating that the dynamic model can effectively detect concentrations from low (10² CFU / mL) to high (10... 5 It maintains stable quantitative accuracy over a wide dynamic range (CFU / mL level).

[0123] To further quantify the goodness of fit of the model, the coefficient of determination (R²) is used to evaluate the consistency between the measured data and the calculated values ​​of the model. The formula for calculating R² is as follows: ,in, y i These are measured values. y p Calculate values ​​for the model. y aThe value is the measured mean. The closer the R² value is to 1, the better the agreement between the model's calculated value and the measured value. Calculations show that the R² value between the dynamic model and the slab counting results in this embodiment reaches 0.998, extremely close to the theoretical maximum value of 1. This statistical result mathematically confirms that the dynamic model's explanatory power for the measured data reaches 99.8%, with only a small deviation of 0.2% attributable to random error or inherent fluctuations in the method itself.

[0124] The 30-minute rapid detection results are calculated using a linear model: Parameters determined, μA (measured by a microbial-free blank electrode) k =0.023μA / CFU·mL -1 (Using a concentration gradient of 10) 3 CFU / mL ~10 6 (CFU / mL standard bacterial culture calibration) = ;

[0125] Table 2. Comparison of linear model calculation results and plate count results

[0126] As shown in Table 2, the comparison between the linear model calculation results and the plate count results reveals the applicability and limitations of this model in short-term rapid detection scenarios. Table 2 lists three oilfield produced water samples with different concentration gradients, which were calculated using the linear model of this invention, while the traditional plate count method was used as a control. The microbial quantity results obtained by the two methods showed a certain correlation, but there were also observable biases.

[0127] Looking at the specific data, the linear model calculation result for sample 1 was 0.8 × 10² CFU / mL, and the plate count result was 1.1 × 10² CFU / mL, with an absolute deviation of 0.3 × 10² CFU / mL and a relative deviation of approximately 27.3%. For sample 2, the linear model calculation result was 3.3 × 10³ CFU / mL, and the plate count result was 3.7 × 10³ CFU / mL, with an absolute deviation of 0.4 × 10³ CFU / mL and a relative deviation of approximately 10.8%. The linear model calculation result for sample 3 was 8.6 × 10² CFU / mL. 4 CFU / mL, plate count result: 1.0 × 10⁻⁶ 5 CFU / mL, absolute deviation 0.14×10 5CFU / mL, relative deviation 14%. Among the three samples, the deviation of low-concentration sample 1 was relatively large, while the deviations of medium- and high-concentration samples 2 and 3 were relatively small, indicating that the quantitative accuracy of the linear model was affected to some extent in the low-concentration region.

[0128] To further quantify the goodness of fit of the model, the coefficient of determination (R²) was used to evaluate the consistency between the measured data and the model's calculated values. The calculated R² = 0.78, indicating that the model can explain 78% of the variation in the measured data. The remaining 22% of the variation cannot be explained by the model; this deviation mainly stems from the limitations of the method itself and random errors in the experimental process.

[0129] The fitting effect of R²=0.78 contrasts sharply with the R²=0.998 achieved by the dynamic model, a difference with significant technical implications. In the 30-minute rapid detection scenario, microorganisms have not yet reached metabolic homeostasis, especially in low-concentration samples. Microorganisms need time to adapt to the new microenvironment and initiate metabolic activity, resulting in the linear relationship between characteristic peak current and microbial quantity not being fully established. The basic assumption of the linear model—that the rate of metabolite production is proportional to the microbial quantity—is not fully satisfied in the early stages of detection, thus producing observable bias. The bias is greatest in low-concentration sample 1 precisely because at low bacterial density, the population effect is weak, metabolic initiation is slower, and the time required to establish a linear relationship is longer.

[0130] This result precisely verifies the scientific validity and necessity of the dual-model adaptation strategy employed in this invention. Linear models are simple in form and computationally fast, suitable for scenarios where microbial metabolism has entered a relatively stable phase and the detection time is short; however, when the detection time is insufficient for metabolism to reach steady state, or when the microbial population is at a low concentration, the accuracy of linear models is challenged. Dynamic metabolic models, by introducing a time factor and a decay constant, can more accurately characterize the dynamic establishment process of metabolism, thus exhibiting significant advantages in scenarios involving long-term monitoring or when metabolism is not yet stable.

[0131] The strategy of selecting a model based on a preset detection duration threshold is based on this understanding: when the detection time is less than or equal to the preset duration (e.g., 30 minutes), a linear model can meet the needs of rapid screening; when the detection time is greater than the preset duration, a dynamic metabolic model is used to obtain higher quantitative accuracy. This dual-model adaptation design leverages the advantages of the linear model's simplicity and speed while compensating for its shortcomings in long-term monitoring with the dynamic model, enabling the method of this invention to obtain reliable quantitative results across different time scales and concentration ranges.

[0132] The experimental result of R²=0.78 also points the way for further optimization of the method. For applications requiring high-precision quantification, the detection time can be appropriately extended to ensure that metabolism reaches a stable state, or a dynamic model can be used for calculation; for scenarios requiring only rapid semi-quantitative screening, the performance of the linear model within 30 minutes is sufficient. Users can make a reasonable trade-off between detection efficiency and quantitative accuracy according to their specific detection objectives, which is precisely the embodiment of the flexibility and practicality of this method.

[0133] like Figure 4 As shown, sulfate-reducing bacteria (SRB) on the surface of the carbon-modified electrode, extracted after detection, were observed using fluorescent staining. The different colors of fluorescence clearly revealed the survival status and distribution of the microorganisms on the electrode surface. In the figure, green fluorescence indicates active SRB, yellow fluorescence indicates damaged SRB, and red fluorescence indicates dead SRB. The coexistence and distribution of these three colors on the electrode surface visually reflect the changes in microbial activity during the detection process.

[0134] The green fluorescence represents active SRBs, which are bacteria with intact cell membranes and normal metabolic activity. In the principle of fluorescence staining, active bacteria can enter cells through specific nucleic acid dyes (such as SYTO9) and bind to DNA, emitting green fluorescence, while being excluded by another dye that cannot penetrate the intact cell membrane (such as propidium iodide). As shown in the figure, the green fluorescent areas are mainly distributed inside the conductive polymer-microbial composite membrane on the electrode surface and near the substrate. Microorganisms in these areas are effectively embedded during electropolymerization and well protected throughout the detection process, maintaining cell membrane integrity and metabolic activity. The presence of active SRBs is the biological basis for the accurate quantification of microbial numbers achieved by the method of this invention, because only active microorganisms can continuously metabolize and generate characteristic electrochemical signals.

[0135] Yellow fluorescence represents damaged SRBs, which are bacteria with partially damaged cell membranes and in a sub-lethal state. These bacteria exhibit altered cell membrane permeability but retain some metabolic activity or repair capabilities. In fluorescent staining, damaged bacteria may simultaneously take up parts of both dyes or exhibit yellow or orange fluorescence, somewhere between green and red. Yellow fluorescent areas in the image often appear in the transition zone between active and dead bacterial communities, as well as in more exposed areas of the electrode surface. The damaged state represents an intermediate stage in the transition from active to dead microorganisms. Its presence suggests the presence of certain environmental stresses during detection, such as oxygen exposure, osmotic pressure changes, or the slight influence of electrochemical scanning. However, the overall proportion of damage is low, indicating that the influence of electrode structure and detection conditions on microbial activity is within a controllable range.

[0136] The red fluorescence represents dead SRBs, which are bacteria whose cell membranes are completely destroyed and have lost their metabolic activity. The cell membranes of these bacteria can no longer prevent propidium iodide from entering the cell, where it binds to DNA and emits red fluorescence. In the image, the red fluorescent areas are mainly distributed on the outermost layer and edges of the electrode surface. Microorganisms in these locations may be directly exposed to the environmental medium and subject to stronger external influences. The presence of dead SRBs reminds us that it is necessary to optimize conditions as much as possible during electrode preparation and detection to minimize adverse effects on microbial activity.

[0137] Of particular note is the distribution pattern of the three fluorescence signals in the figure, which reveals the important biological characteristic of SRB's sensitivity to oxygen. Sulfate-reducing bacteria are strict anaerobic microorganisms, whose growth and metabolic activities are strictly dependent on an anaerobic environment. Once removed from the anaerobic environment and exposed to air, SRBs are rapidly subjected to oxygen stress, resulting in impaired cell membrane integrity, decreased metabolic activity, and ultimately damage or even death. This process manifests temporally as follows: in the initial stage of exposure, the proportion of active bacteria decreases while the proportion of damaged bacteria increases; as the exposure time prolongs, damaged bacteria further transform into dead bacteria, and the red fluorescent area gradually expands.

[0138] In this embodiment of the detection process, from sample pretreatment and electrode preparation to electrochemical signal acquisition, the entire process is carried out under anaerobic conditions (such as nitrogen protection) as much as possible to maximize the activity of SRB. However, brief oxygen exposure is still unavoidable in steps such as electrode transfer and spin-coating of the anti-interference layer. Figure 4 The staining results shown precisely demonstrate the effectiveness of this method in maintaining activity: although a certain proportion of damaged and dead SRBs exist on the electrode surface after detection, a large number of active SRBs are still successfully embedded in the PEDOT network and continue to generate metabolic signals, ensuring the feasibility of detection. Simultaneously, the proportion of damaged and dead bacteria is controlled within an acceptable range, indicating that the influence of the electropolymerization process, electrode materials, and detection conditions on microbial activity is minimized.

[0139] The fluorescence staining results provide direct evidence of the biocompatibility of the method of this invention: the porous carbon substrate and the PEDOT conductive polymer network provide a suitable three-dimensional microenvironment for microorganisms, while the Nafion anti-interference layer buffers the impact of the external environment to a certain extent. The synergistic effect of these three components enables the anaerobic-sensitive SRB to maintain a high survival rate and metabolic activity during the detection process, thereby ensuring the stable generation of electrochemical signals and the reliability of the detection results.

[0140] Figure 4The staining observations also suggest directions for further optimization: by shortening the electrode exposure time to air, optimizing the anaerobic operation process, and adjusting the electropolymerization parameters to reduce the transient electrical stimulation to microorganisms, it is expected that the proportion of damaged and dead bacteria can be further reduced, and the fixation efficiency of active bacteria can be improved, thereby further enhancing the detection sensitivity and accuracy. This visualization result not only verifies the feasibility of the current method but also provides an important reference for subsequent technical improvements.

[0141] Example 2

[0142] like Figure 2 As shown, this embodiment provides a method for dynamic assessment of microbial abundance based on carbon-modified electrodes, used to detect the number of Escherichia coli in water. The method includes: 1. Sample pretreatment Take 100 mL of the water sample to be tested and filter it through a 0.45 μm filter membrane to remove suspended particles and impurities; The filtrate was centrifuged at 3000 rpm for 15 minutes under aseptic conditions to enrich bacterial cells. After discarding the supernatant, the filtrate was resuspended in sterile PBS buffer (pH 7.4) and concentrated to the target concentration range (10). 2 CFU / mL ~10 7 (CFU / mL). The resuspension should be used for subsequent testing within 30 minutes to avoid degradation of microbial activity.

[0143] 2. Preparation of carbon-based modified electrodes

[0144] Substrate material: 100 nm porous carbon electrode deposited by chemical vapor deposition, with pretreatment steps the same as in Example 1; Electropolymerization and doping: The pretreated carbon-based modified electrode was used as the working electrode and placed in a suspension containing E. coli (concentration gradient 10). 3 CFU / mL ~10 7 In an electrolyte solution containing CFU / mL and EDOT monomer (10 mM) (volume ratio 1:3), with 0.1 M KCl as the supporting electrolyte, polymerization was carried out under nitrogen protection at a constant potential of 1.05 V (vs. Ag / AgCl) for 900 s to form a 0.8 μm thick PEDOT-E. coli composite membrane. Anti-interference layer modification: A 5% Nafion ethanol solution was spin-coated onto the surface of the composite film (3000 rpm, 60 s) to form a Nafion film of about 200 nm.

[0145] 3. Electrochemical signal acquisition

[0146] Detection system: Three-electrode system (working electrode: carbon-based modified electrode modified with PEDOT-E. coli composite membrane; reference electrode: Ag / AgCl; counter electrode: platinum wire). Detection conditions: The electrode was placed in an electrolytic cell containing 0.1M PBS buffer (pH 7.4), and signal acquisition was performed using cyclic voltammetry (CV). Scan parameters: potential range -0.3V to +0.4V, scan rate 50 mV / s. The scan began after a 60-second settling time, and the redox peak current was recorded. I p ).

[0147] Signal processing flow: Baseline correction: A linear baseline subtraction method is used (subtracting the baseline current from -0.3V to -0.1V).

[0148] Peak current extraction: E. coli metabolites (such as cytochromes) produce characteristic oxidation peaks at +0.25V. Therefore, the oxidation peak is identified at +0.25V, and the peak current value is taken as the peak current. I p .

[0149] Filtering parameters: Adjust the Savitzky-Golay filter window width to 9 (a narrower window preserves peak sharpness).

[0150] 4. Model Calculation

[0151] For detection times of less than 1 hour, microbial metabolic homeostasis was calculated using a linear model.

[0152] parameter: Sensitivity coefficient k = 0.098 μA / (CFU·mL) - ¹) (Obtained by fitting the slope of the calibration curve using the least squares method); Background current I0 = 0.03 μA (measured by a blank electrode free of microorganisms); .

[0153]

[0154] Table 3. Comparison of linear model calculation results and plate count results

[0155] As shown in Table 3, the comparison between the linear model calculation results and the plate count results fully verifies the accuracy and reliability of the linear model constructed in this invention in the scenario of rapid detection of microorganisms with high metabolic activity and easy steady state. Table 3 lists three E. coli samples with different concentration gradients, which were calculated using the linear model of this invention, while the traditional plate count method was used as a control. The microbial quantity results obtained by the two methods showed a high degree of consistency.

[0156] Looking at the specific data, the linear model calculation result for sample 1 was 3.1 × 10³ CFU / mL, and the plate count result was 3.2 × 10³ CFU / mL, with an absolute deviation of only 0.1 × 10³ CFU / mL and a relative deviation of approximately 3.1%. The linear model calculation result for sample 2 was 8.6 × 10³ CFU / mL, and the plate count result was 8.7 × 10³ CFU / mL, with an absolute deviation of 0.1 × 10³ CFU / mL and a relative deviation of approximately 1.1%. The linear model calculation result for sample 3 was 1.1 × 10³ CFU / mL. 5 CFU / mL, plate count result: 1.2 × 10⁻⁶ 5 CFU / mL, absolute deviation 0.1×10 5 The relative deviation was approximately 8.3% (CFU / mL). The detection results for all three concentration gradients showed very small deviations, indicating that the linear model is effective from the 10³ CFU / mL level to 10... 5 It maintains excellent quantitative accuracy over a wide dynamic range at the CFU / mL level.

[0157] To further quantify the goodness of fit of the model, the coefficient of determination R² was used to evaluate the consistency between the measured data and the calculated values ​​of the model. The calculation showed that the R² value of the linear model and the plate counting results in this embodiment reached 0.99, which is very close to the theoretical maximum value of 1. This statistical result mathematically confirms that the linear model has an explanatory power of 99% for the measured data, and only 1% of the small deviation can be attributed to random error or the inherent fluctuation of the method itself.

[0158] The excellent fit of R²=0.99 has multiple implications: First, it proves that the linear model can accurately describe the metabolic characteristics of E. coli within a 30-minute rapid detection period, and the model assumption (that the rate of metabolite production is proportional to the number of microorganisms) is fully valid in this detection system; second, it verifies the sensitivity coefficient k=0.098μA / (CFU·mL) calibrated using standard concentration bacterial solutions. - ¹) The applicability and stability of the detection system and the scientific reliability of the calibration method; furthermore, it shows that the entire detection process, from the preparation of carbon-based modified electrodes, the acquisition and processing of electrochemical signals to model calculations, also has good reproducibility and system stability in the detection of Escherichia coli, a typical microorganism.

[0159] This example provides a useful comparison with the 30-minute detection results of sulfate-reducing bacteria in Example 1 (R²=0.78). This difference precisely reveals the applicability of the linear model and its correlation with microbial metabolic characteristics. *Escherichia coli*, as a model microorganism with high metabolic activity, strong adaptability, and the ability to quickly reach metabolic homeostasis, can establish stable metabolic activity in a short time, and the characteristic peak current shows a good linear relationship with the number of microorganisms. In contrast, sulfate-reducing bacteria, as anaerobic bacteria, have a relatively slow metabolic rate and have not fully reached metabolic homeostasis in the short 30-minute detection, resulting in a relatively low linear model fit. This comparison clearly demonstrates the universality and flexibility of this method: for different types of microorganisms with different metabolic characteristics, the detection time and model type can be reasonably selected based on their metabolic characteristics and detection needs, while ensuring accuracy.

[0160] The experimental results fully demonstrate that the proposed linear model can be effectively applied to the rapid quantitative detection of microorganisms with high metabolic activity and easy steady-state. By completing the entire process from sample pretreatment to result output within 30 minutes, accurate quantification of E. coli counts is achieved, with detection efficiency more than 50 times higher than the traditional plate count method (which requires 24-48 hours). The excellent fit result of R²=0.99 not only provides strong experimental support for the scientific validity and practicality of the linear model, but also lays a solid foundation for the promotion of this method in applications requiring rapid microbial detection results, such as food safety, medical diagnosis, and water quality monitoring.

[0161] This embodiment, together with the dynamic model (R²=0.998) and linear model (R²=0.78) in Embodiment 1, constitutes a complete validation of the dual-model adaptation strategy of this method: the dynamic model performs excellently in long-term monitoring of sulfate-reducing bacteria (R²=0.998); the linear model also performs excellently in short-term detection of metabolically active Escherichia coli (R²=0.99); while the linear model has a relatively low fit in short-term detection of slower-metabolizing sulfate-reducing bacteria (R²=0.78), which illustrates the importance of selecting an appropriate model based on microbial characteristics and detection duration. The three sets of experimental data corroborate each other, comprehensively demonstrating the scientific validity, universality, and reliability of this method.

[0162] The various embodiments of the present invention have been described above. These descriptions are exemplary and not exhaustive, nor are they limited to the disclosed embodiments. Many modifications and variations will be apparent to those skilled in the art without departing from the scope and spirit of the described embodiments.

Claims

1. A method for dynamic assessment of microbial populations based on carbon-modified electrodes, characterized in that, include: Prepare carbon-based modified electrodes doped with target microorganisms; The carbon-based modified electrode was used as the working electrode, forming a three-electrode system with the reference electrode and the counter electrode. The system was placed in the test medium containing the target microorganism for electrochemical scanning, and the electrochemical response signal was collected. The electrochemical response signal is processed to obtain the characteristic electrochemical parameters of the target microbial metabolites; The model is selected based on a preset detection duration threshold: when the detection time is less than or equal to the preset duration, a linear model is used; when the detection time is greater than the preset duration, a dynamic metabolic model is used. By substituting the characteristic electrochemical parameters into the selected model, the number of microorganisms in the test medium can be calculated.

2. The method for dynamic assessment of microbial populations based on carbon-modified electrodes according to claim 1, characterized in that, The carbon-based modified electrode comprises, from bottom to top: Porous carbon substrate; A conductive polymer-microorganism composite membrane is formed on the surface of the porous carbon substrate, wherein the composite membrane is a PEDOT membrane doped with target microorganisms; An anti-interference layer is formed on the surface of the conductive polymer-microorganism composite membrane, wherein the anti-interference layer is a Nafion membrane.

3. The method for dynamic assessment of microbial populations based on carbon-modified electrodes according to claim 2, characterized in that, The conductive polymer-microorganism composite membrane is prepared via the following electropolymerization process: The pretreated porous carbon substrate was used as the working electrode and placed in an electrolyte; the electrolyte contained EDOT monomer, supporting electrolyte and suspension containing target microorganisms. Polymerization was carried out at a constant potential of 1.0V to 1.2V (vs. Ag / AgCl) for 800s to 1000s. During the polymerization process, the PEDOT chains formed were positively charged in the oxidized state and were electrostatically attracted to the negatively charged microorganisms on the surface. This allowed the microorganisms to be embedded in the growing PEDOT network as dopants, forming a conductive polymer-microorganism composite film.

4. The method for dynamic assessment of microbial populations based on carbon-modified electrodes according to claim 3, characterized in that, The concentration of EDOT monomer in the electrolyte is 8 mM to 12 mM, the concentration of supporting electrolyte is 0.05 M to 0.15 M, and the concentration range of the microbial suspension is 10 mM. 2 CFU / mL ~10 7 CFU / mL; the supporting electrolyte is selected from LiClO4, NaCl or KCl.

5. The method for dynamic assessment of microbial populations based on carbon-modified electrodes according to claim 2, characterized in that, The anti-interference layer is a Nafion film, which is modified onto the surface of a conductive polymer-microbial composite film by spin coating or electrochemical deposition. The process parameters for spin coating are: 5% w / w Nafion ethanol solution, spin speed 2500 rpm to 3000 rpm, and spin coating time 60 s to 90 s.

6. The method for dynamic evaluation of microbial populations based on carbon-modified electrodes according to claim 2, characterized in that, The porous carbon substrate was prepared by chemical vapor deposition, using methane as the carbon source, with a CH4:H2 volume ratio of 1:3, and was deposited at 800℃~900℃ and 40kPa~60kPa for 1~2 hours to obtain a nanoporous structure with a specific surface area greater than 1200m² / g.

7. The method for dynamic assessment of microbial populations based on carbon-modified electrodes according to claim 1, characterized in that, The processing of the electrochemical response signal includes: The collected electrochemical response signals are filtered. The characteristic peak current value and / or characteristic peak integral area are extracted from the filtered signal and used as the characteristic electrochemical parameters.

8. The method for dynamic assessment of microbial populations based on carbon-modified electrodes according to claim 1, characterized in that, The expression for the linear model is: ； ； in, Microbial count (CFU / mL); Characteristic peak current (μA); Sensitivity coefficient (μA / CFU·mL) - ¹); This represents the background current (μA).

9. The method for dynamic assessment of microbial populations based on carbon-modified electrodes according to claim 1, characterized in that, The expression for the dynamic metabolic model is: ； After integration, we get: ； ； in, Microbial count (CFU / mL); The integral area of ​​the characteristic peak (μC); Maximum metabolic rate of microorganisms (h - ¹); Metabolic decay constant (h) - ¹); The detection time is in hours (h). The background integral area (μC) is the area without microorganisms.

10. The method for dynamic assessment of microbial populations based on carbon-modified electrodes according to claim 7, characterized in that, The filtering process is Savitzky-Golay filtering, and the filtering parameters include window width and polynomial order. The window width is 15, and the polynomial order is 3.

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