Etr-based biosensing systems and methods

Abstract

The present invention provides a biological sensing system and method based on electrochemical transistor resonance spectroscopy (ETRS). The system includes a biological sensing module, a signal generator, and a data processor. The biological sensing module is configured to sense one or more target biological analytes and is excited under a plurality of frequency-amplitude combinations provided by the signal generator. The data processor is configured to receive and process time-domain sensing signals from the biological sensing module to predict concentrations of the one or more target biological analytes. The data processor is implemented by one or more artificial neural networks and is configured to analyze the time-domain sensing signals by an ETRS method using the one or more artificial neural networks. The present invention can achieve a detection limit (LoD) as low as 1 fM, which is up to six orders of magnitude lower than conventional electrochemical methods.

Description

Cross-referencing of patent applications

[0001] This application claims priority to U.S. Provisional Patent Application No. 63 / 780,482, filed March 31, 2025, the entire disclosure of which is incorporated herein by reference. Technical Field

[0002] This invention generally relates to biosensing technology. More specifically, this invention relates to biosensing methods and systems based on electrochemical transistor resonance spectroscopy (ETRS). Background Technology

[0003] With the rise of smartphones and mobile devices, wearable sensors have garnered widespread attention for their crucial role in enabling ubiquitous health monitoring. These devices provide valuable information related to individual health and performance, and serve as key tools for collecting datasets to build large-scale health models, thereby supporting digital health and AI-driven healthcare. Early wearable technologies focused primarily on physical sensors for monitoring movement and vital signs such as steps, calories burned, or heart rate. In recent years, wearable devices have evolved rapidly, shifting from tracking readily available physical activity to addressing critical healthcare challenges such as diabetes management and remote monitoring of the elderly. To meet these needs, a new wave of research has emerged, focusing on the development of wearable biosensors capable of monitoring biochemical signals. These devices integrate biorecognition elements, such as enzymes, antibodies, aptamers, or cell receptors, into their operation (e.g., amperometry, cyclic voltammetry (CV), differential pulse voltammetry (DPV), square wave voltammetry (SWV)). The increasing number of proof-of-concept studies demonstrates the significant potential of wearable biosensors, and some devices, such as continuous glucose monitoring (CGM) systems, have already been commercialized. However, further development in this field necessitates the urgent need for novel sensor technologies. Most clinically relevant bioanalytes exist at extremely low concentrations (nanomolar to femtomolar levels), posing significant challenges to current electrochemical methods. For example, glucose concentrations detected by commercial CGMs are typically in the 1–10 mM range, while bioanalytes such as insulin are usually in the picomolar range, and COVID-19 biomarkers are in the femtomolar range. Detection of these extremely low-concentration molecules requires improved sensor technologies.

[0004] Organic electrochemical transistors (OECTs) show great promise in biosensing applications because they can amplify signals directly within the sensor. The gate electrode of an OECT detects the electrochemical signal, while the transistor amplifies it, achieving amplification factors up to 10,000, which is among the highest for transistor-based biosensors. Furthermore, the redox-controlled operating mechanism allows OECTs to operate at low voltages (millivolts) in biological systems while maintaining high amplification capabilities, making them particularly suitable for interfacing with living systems.

[0005] Based on the aforementioned advantages, OECT is increasingly being applied in wearable and implantable biosensing applications. However, existing OECT-based sensors do not perform consistently in detecting biochemical signals. Early research focused on improving sensitivity by increasing the amplification ratio (transconductance, Gm), while other studies analyzed shifts in the transfer curve. Despite these advances, a unified approach is still lacking to demonstrate the achievable sensitivity limits of OECT for specific bioanalytes, and a standardized method for comparing its performance with traditional three-electrode electrochemical systems. Filling this gap is crucial for achieving fair evaluation, large-scale validation, and the commercialization of OECT biosensors.

[0006] Traditional frequency-dependent electrochemical techniques, such as electrochemical impedance spectroscopy (EIS), are limited by their dependence on linear conditions. These methods require small signals, resulting in poor signal-to-noise ratios (SNR). Furthermore, the behavior of the electrochemical double layer is inherently nonlinear, and linear methods cannot directly capture the key nonlinear dynamics information directly related to molecular detection, ultimately limiting detection accuracy.

[0007] Previously reported OECT biosensors analyzed via transfer curves essentially operate in a nonlinear detection mode. However, this type of method relies too heavily on overly simplified mathematical models, limiting its full potential. Summary of the Invention

[0008] To overcome the aforementioned limitations, this invention proposes an ETRS technology based on the concept of integrated sensing, which integrates multiple OECT characterization methods into a single process. Based on this ETRS technology, a comprehensive frequency scan is performed, and complex high-dimensional correlation analysis of the harmonic response is conducted using artificial neural networks (ANNs). These ANNs are pre-trained on a large number of experimental datasets, enabling them to analyze complex harmonic modes in OECT drain-source current data and reveal deep correlations between these harmonic modes and bioanalyte concentrations.

[0009] According to a first aspect of the present invention, an OECT biosensing system based on ETRS is provided. The system includes: a biosensing module configured to detect one or more target bioanalytes and generate a time-domain sensing signal corresponding to the concentration of the one or more target bioanalytes; a signal generator coupled to the biosensing module and configured to generate an excitation signal for application to the biosensing module, and further configured to co-scan the frequency and amplitude of the excitation signal within an operating frequency range and an operating amplitude range, respectively, such that the biosensing module is excited under multiple frequency-amplitude combinations; and a data processor coupled to the biosensing module and configured to receive and process the time-domain sensing signal to estimate the concentration of the one or more target bioanalytes. The data processor is implemented using one or more artificial neural networks and is configured to: receive the time-domain sensing signal from the biosensing module in response to multiple frequency-amplitude combinations of the excitation signal; convert the time-domain sensing signal into a frequency-domain sensing signal; analyze the time-domain sensing signal and the frequency-domain sensing signal using the one or more artificial neural networks and the ETRS method to determine one or more optimal excitation conditions, each optimal excitation condition including an operating frequency and operating amplitude associated with a corresponding target bioanalyte; extract one or more resonant characteristics of the biosensing module from the time-domain sensing signal and the frequency-domain sensing signal; and predict the concentration of each of the one or more target bioanalytes based on the determined one or more optimal excitation conditions and the one or more resonant characteristics.

[0010] According to a second aspect of the present invention, an OECT biosensing method based on ETRS is provided. The method includes: generating an excitation signal for applying to the biosensing module by a signal generator coupled to the biosensing module; co-scanning the frequency and amplitude of the excitation signal by the signal generator within an operating frequency range and an operating amplitude range, respectively, such that the biosensing module is excited under multiple frequency-amplitude combinations; detecting one or more target bioanalytes by the biosensing module; generating a time-domain sensing signal corresponding to the concentration of the one or more target bioanalytes by the biosensing module; and receiving the time-domain sensing signal from the biosensing module by a data processor coupled to the biosensing module in response to multiple frequency-amplitude combinations of the excitation signal. The data processor converts the time-domain sensing signal into a frequency-domain sensing signal; the data processor, implemented using one or more artificial neural networks, analyzes the time-domain and frequency-domain sensing signals using the ETRS method to determine one or more optimal excitation conditions, each optimal excitation condition including an operating frequency and operating amplitude associated with a corresponding target bioanalyte; the data processor extracts one or more resonant characteristics of the biosensing module from the time-domain and frequency-domain sensing signals; and the data processor predicts the concentration of each of the one or more target bioanalytes based on the determined one or more optimal excitation conditions and the one or more resonant characteristics.

[0011] The ETRS technology reflects the maximum sensitivity achievable by OECT-based sensors. This effect is achieved by scanning the gate excitation voltage over a wide frequency range to identify specific resonant frequencies for each bioanalyte. These resonants represent the optimal synergy between the detection electrode and the OECT-based sensor, resulting in detection limitations (LoD) as low as 1 fM (e.g., for Na+). + (This is up to six orders of magnitude lower than the traditional three-electrode electrochemical method).

[0012] The improved sensitivity comes from two aspects: i) the transistor coupled within the sensor amplifies the detection signal at the beginning of the detection, thereby improving the signal-to-noise ratio (SNR); and ii) the numerical frequency domain analysis supported by the ANN can reflect more information about the sensing electrochemical double layer dynamics, which cannot be obtained by existing time domain analysis methods.

[0013] It has been demonstrated that the ETRS platform can be applied to other common scenarios that employ electrochemical sensors, such as overcoming the sensitivity limitations of current biochips in multimodal sensing. Attached Figure Description

[0014] The embodiments of the present invention will be described in more detail below with reference to the accompanying drawings, wherein:

[0015] Figure 1 The workflow of the ETRS process (below) according to various aspects of the present invention is shown, along with a schematic comparison with conventional electrochemical impedance spectroscopy (EIS) (above).

[0016] Figure 2A A schematic diagram of Ids frequency analysis using Fast Fourier Transform is shown.

[0017] Figures 2B to 2G The results of signal reconstruction accuracy assessment using an increasing number of harmonic components (from the fundamental (first harmonic) to the sixth harmonic) are shown respectively.

[0018] Figure 2H A quantitative assessment of the reconstruction error is shown.

[0019] Figure 3A The time-domain representation of the input voltage (Vg above) and the corresponding output current (Ids below) is shown, as well as their use in detecting biomarkers at different input frequencies (0.1 Hz to 10 kHz).

[0020] Figure 3B The frequency domain representation of the corresponding output current Ids obtained using FFT is shown, revealing the harmonic components associated with each input frequency.

[0021] Figure 3C The frequency-dependent response in the ETRS at a fixed bioanalyte concentration is shown, where the amplitude and phase of Ids are plotted from the input frequency from 0.1 Hz to 10 kHz, illustrating how higher-order harmonics can improve the fidelity of the measurement signal.

[0022] Figure 3D The total harmonic distortion (THD) calculated at different input frequencies is shown, illustrating how THD varies with the input frequency, thus characterizing the distortion level in the ECT output current.

[0023] Figure 4A The analytical simulation results show the absolute change in voltage drop across the channel as the input frequency increases. In short, at the resonant frequency; Figure 4B The analytical simulation results show the relative change in voltage drop across the channel as the input frequency increases; Figure 4C The changes in biomarker concentrations are shown as the input frequency increases.

[0024] Figure 5 A flowchart is shown of an ETRS-based OECT biosensing method for monitoring the concentration of one or more bioanalytes according to the present invention.

[0025] Figure 6AA non-FFT method (M1) is shown, which can directly input a time-domain current signal; Figure 6B The FFT method (M2) is shown, which can take harmonic information obtained by FFT from a time-domain signal as input; Figure 6C The combined approach (M3) is shown, which employs a lightweight architecture that combines time-domain signals and FFT harmonic information.

[0026] Figure 7 A comparison of computational efficiency (in floating-point operations per second (kFLOPs)) and accuracy is shown among the three methods.

[0027] Figure 8 A schematic diagram of a wireless ETRS-based OECT biosensing system according to an embodiment of the present invention is shown.

[0028] Figure 9A and Figure 9B The top and bottom views of the prototype biosensing system are shown respectively.

[0029] Figure 10 Experimental data for voltage input and current measurements at different frequencies are shown.

[0030] Figure 11 A schematic diagram of an OECT biosensor according to an embodiment of the present invention is shown.

[0031] Figure 12 The impedance mathematical models of each circuit component of the OECT biosensor are shown.

[0032] Figure 13A The operation of the ETRS-based OECT biosensor is shown; Figure 13B The complete ETRS spectrum generated by the ETRS-based OECT biosensor is shown.

[0033] Figure 14 This is a schematic diagram of the layout of an OECT biochip, which has an OECT sensor unit array.

[0034] Figure 15 This is an exploded view of the OECT biochip components.

[0035] Figure 16 An exemplary manufacturing process for the OECT biochip is shown.

[0036] Figure 17 A schematic diagram of an ion-type biosensor modified from an OECT biosensor is shown.

[0037] Figure 18 The steps for modifying ISM on the channels of an OECT sensor to fabricate an OECT Na+ sensor are shown.

[0038] Figure 19 A schematic structure of an ion-selective OECT biosensor is shown.

[0039] Figure 20 The transfer characteristics of conventional OECT before and after coating with ISM, as well as those of a device with PSSNa as the internal electrolyte, are shown.

[0040] Figure 21 It shows that at a fixed V g = 0 V and V ds Under the condition of -0.4 V, different concentrations of Na + K + and Ca 2+ The current response is compared.

[0041] Figure 22 The method for sensing Na at different frequencies (0.1 Hz, 600 Hz, 10 kHz) is shown. + The signal extracted from the ETRS spectrum of the ions showed superior sensitivity at the resonant frequency (600 Hz).

[0042] Figure 23 This demonstrates an ETRS-based biosensor for sensing Na+. + In terms of ions, LoD is superior to other competing technologies.

[0043] Figure 24 A schematic diagram of an aptamer-type biosensor modified from an OECT biosensor is shown.

[0044] Figure 25 A schematic diagram of the functionalization process for adapting body-sized biosensors.

[0045] Figure 26 The DNA aptamer sequence for target detection is shown, including an insulin-terminal probe and the corresponding two-dimensional structure predicted by Mfold.

[0046] Figure 27 The impedance changes observed on the electrode surface during functionalization are shown, illustrating the transition from a bare gold surface to an aptamer-modified surface followed by 6-MCH closure.

[0047] Figure 28 The results of successful modification using the square wave voltammetry method are shown.

[0048] Figure 29 A schematic diagram of an enzyme-type biosensor modified from an OECT biosensor is shown.

[0049] Figure 30The steps for preparing an enzyme-modified gate electrode for forming the enzyme-type OECT biosensor are shown.

[0050] Figure 31 The electron transfer process on the enzyme-modified electrode of a glucose biosensor in the presence of glucose is shown.

[0051] Figure 32 Cyclic voltammetry curves of the modified electrode in 0.1 M PBS are shown. The presence of redox peaks is attributed to ferrocene, which acts as an electron transport mediator.

[0052] Figure 33 Cyclic voltammetry curves of the modified electrode before and after the addition of 50 mM glucose are shown.

[0053] Figure 34 The response of the OECT glucose sensor to potential interfering substances, including ascorbic acid (AA) and urea (n=5), is shown. Data are presented as mean ± standard deviation (SD). PBS represents phosphate-buffered saline.

[0054] Figure 35 An integrated ETRS-based biosensing system is shown, which integrates an ion-type biosensing unit, an enzyme-type biosensing unit, an aptamer-type biosensing unit, and a cortisol sensing unit.

[0055] Figures 36A to 36D The LoD values ​​of different aptamer-type OECT biosensors for detecting cortisol, insulin, SARS-CoV-2 spike protein, and glucose are shown.

[0056] Figures 37A to 37C The signal changes of the aptamer-type OECT biosensor when detecting different non-specific targets are shown.

[0057] Figure 38 A schematic diagram illustrating the characterization of the OECT device of the present invention and a conventional EC device is shown.

[0058] Figure 39 The SNR comparison under simulated noise conditions is shown.

[0059] Figure 40 An exemplary implementation of an integrated wearable ETRS platform for detecting low concentrations of bioanalytes is shown.

[0060] Figure 41 The sensing mechanism of the OECT biosensor is illustrated. Target molecules (e.g., cortisol) are captured by aptamers, and the signal is amplified by OECT.

[0061] Figure 42 The ETRS spectrum generated after the frequency scan is shown.

[0062] Figure 43 Experimental data on cortisol concentration signals extracted from ETRS spectra are shown. Detailed Implementation

[0063] In the following description, details of the invention are set forth as preferred embodiments. Those skilled in the art will understand that various modifications, including additions and / or substitutions, can be made without departing from the scope and spirit of the invention. To avoid obscuring the invention, certain specific details may be omitted; however, the manner in which this disclosure is written is sufficient to enable those skilled in the art to implement the technical solutions described herein without excessive experimentation. Unless otherwise defined, all technical and scientific terms used in this application have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains. The terminology used in this application is for the purpose of describing particular embodiments only and is not intended to limit the application. The term "and / or" as used in this application includes any and all combinations of one or more of the associated listed items. Some embodiments of this application are described in detail below with reference to the accompanying drawings. Unless otherwise specified, the following embodiments and features described herein can be combined with each other.

[0064] Figure 1 The workflow of the ETRS process according to various aspects of the present invention is shown below, along with a schematic comparison with conventional electrochemical impedance spectroscopy (EIS) (above). The ETRS process employs a large input signal and uniquely integrates a transistor (T) into the sensing circuit to amplify the signal. The amplification ratio at a specific resonant frequency (f) is... Resonate The resonant frequency reaches its maximum at ( ), which characterizes the optimal synergy between the sensing electrode and the transistor amplifier. The amplified nonlinear signal processed by the Fast Fourier Transform (FFT) can be used for pattern recognition and quantitative analysis with the aid of an ANN.

[0065] The ETRS process is similar to the nonlinear EIS process, but its unique feature lies in its combination with an electrochemical transistor to achieve in-sensor amplification. Unlike conventional EIS, nonlinear EIS applies a larger excitation signal and is not constrained by linear response, thus enabling the extraction of richer dynamic information about the electrochemical system, which is of great significance for improving sensitivity.

[0066] In the frequency domain, higher-dimensional information can be obtained, such as the distribution of higher-order harmonics and their power density. Figure 2A This demonstrates the use of FFT to analyze I. dsA schematic diagram of frequency analysis. FFT may generate a large number of secondary harmonics with low information content. Therefore, it is necessary to identify the main harmonic components for subsequent analysis.

[0067] Figures 2B to 2G The evaluation results of signal reconstruction accuracy are shown when using a progressively increasing number of harmonic components (from the fundamental frequency (first harmonic) to the sixth harmonic). Figures 2B to 2G In the middle, the upper panel shows the DC bias and corresponding harmonic waveforms used for reconstruction, and the lower panel shows a comparison between the original measurement signal and the reconstructed waveform. Figure 2H A quantitative assessment of the reconstruction error, defined as |RMS_reconstructed − RMS_original| / RMS_original, is presented. Results show that the error can be reduced to below 1% when the sixth harmonic is included. Therefore, only the first six harmonics require further analysis during the ETRS process.

[0068] It should also be noted that these harmonic distributions and power densities are more sensitive to fluctuations in electrochemical systems, such as changes in the concentration of bioanalytes.

[0069] Figure 3A The input voltage is shown (V above). g ) and corresponding output current (I below) ds The time-domain representation of the data, and how they are used to detect biomarkers at different input frequencies (0.1 Hz to 10 kHz). Figure 3B The corresponding output current I obtained using FFT is shown. ds The frequency domain representation reveals the harmonic components associated with each input frequency. Figure 3C The frequency correlation response of the ETRS at a fixed bioanalyte concentration is shown, where I is plotted from the input frequency of 0.1 Hz to 10 kHz. ds The amplitude and phase of the signal illustrate how higher-order harmonics can improve the fidelity of the measured signal. Figure 3D The total harmonic distortion (THD) calculated at different input frequencies is shown, illustrating how THD varies with input frequency, thus reflecting the distortion level in the ECT output current.

[0070] From a mechanistic perspective, ETRS employs a nonlinear detection method to analyze higher harmonics of electrochemical signals, thereby revealing more profound dynamic characteristics of the electrochemical double layer. Therefore, this method is a powerful molecular detection technique. Integrated OECT, with its high transconductance, can further amplify the electrochemical signal, significantly improving the signal-to-noise ratio (SNR).

[0071] Notably, each tested biosensor exhibited a resonant frequency corresponding to its highest sensitivity, which could be up to six orders of magnitude higher than other reference frequencies. This resonance phenomenon indicates that optimal synergy is achieved between the detection electrode and the transistor at this frequency, and the characteristics of both are frequency-dependent. The unique resonant frequencies of each sensor can be easily identified by performing a complete ETRS frequency sweep.

[0072] In ETRS analysis, biosensors are treated as a unified nonlinear “black box” system. For a given sensor, the gate sensing electrode and the OECT transconductance may exhibit different frequency dependencies. However, there is no need to separate their individual contributions. Instead, the system can be treated holistically, focusing on its overall frequency response. Once the intrinsic resonant frequency of the system is determined through a simple preprocessing frequency scan, the lowest detection limit (LoD) can be obtained by operating the biosensor at that resonant frequency.

[0073] Figures 4A to 4C The analysis results shown demonstrate the existence of a resonant frequency in the ETRS, including the absolute change in voltage drop across the channel as the input frequency increases. Figure 4A ) and relative change ( Figure 4B In short, at the resonant frequency, changes in the concentration of biomarkers lead to a voltage drop (ΔV) across the channel. ch Significant changes occur, thereby consequently exacerbating the detection limitations of ETRS. Figure 4C ).

[0074] Figure 5 A flowchart of an ETRS-based OECT biosensing method for monitoring the concentration of one or more bioanalytes according to the present invention is shown. The method includes the following steps:

[0075] S101: An excitation signal is generated by a signal generator coupled to the biosensing module for application to the biosensing module;

[0076] S102: The signal generator performs co-scanning of the frequency and amplitude of the excitation signal within the operating frequency range and the operating amplitude range, respectively, so that the biosensing module is excited under multiple frequency-amplitude combinations;

[0077] S103: The biosensing module detects one or more target bioanalytes;

[0078] S104: The biosensing module generates a time-domain sensing signal corresponding to the concentration of one or more target bioanalytes;

[0079] S105: A data processor coupled to the biosensing module receives the time-domain sensing signal from the biosensing module in response to multiple frequency-amplitude combinations of the excitation signal;

[0080] S106: The data processor converts the time-domain sensing signal into a frequency-domain sensing signal;

[0081] S107: The data processor, implemented using one or more artificial neural networks (ANNs), uses the ETRS method to analyze the time-domain sensing signal and the frequency-domain sensing signal to determine one or more optimal excitation conditions, each optimal excitation condition including the operating frequency and operating amplitude associated with the corresponding target bioanalyte;

[0082] S108: The data processor extracts one or more resonant characteristics of the biosensing module from the time-domain sensing signal and the frequency-domain sensing signal; and

[0083] S109: The data processor predicts the concentration of each of the one or more target bioanalytes based on the determined one or more optimal excitation conditions and the one or more resonance characteristics.

[0084] Figures 6A to 6C Three different ANN architectures are shown for pattern recognition in nonlinear sensing signal analysis in ETRS. Figure 6A A non-FFT method (M1) is shown, which can directly input a time-domain current signal; Figure 6B The FFT method (M2) is shown, which can take harmonic information derived from a time-domain signal via FFT as input; Figure 6C The combined method (M3) is shown, which employs an architecture that simultaneously combines the time-domain signal and the harmonic information derived by FFT.

[0085] In the non-FFT method M1, the first linear regression model network is used. For time-domain sensing signals Analysis is performed to obtain a set of temporal feature embeddings. Subsequently, a linear classifier was used. Embedding of this time-domain feature Analysis is performed to obtain a set of candidate predictions C i Finally, from the candidate prediction C i The final prediction result (e.g., C3) is identified in the process.

[0086] Specifically, the first linear regression model network is trained / configured to analyze time-domain sensing signals by extracting the resonance characteristics in the time-domain sensing signals as time-domain feature embeddings. ; and quantitatively correlate the resonant characteristics with the concentrations of one or more target bioanalytes to obtain one or more corresponding optimal excitation voltages as candidate predictors C. i Each optimal excitation voltage is determined by positioning the optimal frequency and amplitude of the excitation signal at which the highest sensitivity is obtained for the corresponding target bioanalyte. This resonant characteristic includes at least one of the following: resonant frequency, resonant amplitude, phase response, bandwidth, or quality factor.

[0087] In FFT method M2, the time-domain sensing signal is transformed by performing a Fast Fourier Transform (FFT). Converted to frequency domain sensing signal Subsequently, a second linear regression model network was used. The frequency domain sensing signal is analyzed to obtain a set of frequency domain feature embeddings. Then a linear classifier is used. embedding the frequency domain features Analysis is performed to obtain a set of candidate predictions C i Finally, from the candidate prediction C i The final prediction result (e.g., C3) is identified in the process.

[0088] In the combination method M3, the time-domain sensing signal is converted into a frequency-domain sensing signal by performing an FFT. A first linear regression model network is employed. The time-domain sensing signal is analyzed to obtain a set of time-domain feature embeddings. The second linear regression model network is used. The frequency domain sensing signal is analyzed to obtain a set of frequency domain feature embeddings. Embed the time-domain features Embedded with the frequency domain features Combined to obtain a set of combined feature embeddings Subsequently, a linear classifier was used. Embedding the combined features Analysis is performed to obtain a set of candidate predictions C i Finally, from the candidate prediction C i The final prediction result (e.g., C3) is identified in the process.

[0089] Figure 7 The comparison of the three methods in terms of computational efficiency and accuracy is shown. The results show that the combined method M3 achieves the highest accuracy, up to 96%, compared to methods M1 and M2.

[0090] Figure 8A schematic diagram of an OECT biosensing system 1 based on ETRS according to an embodiment of the present invention is shown, which is used to perform method S100.

[0091] As shown in the figure, the ETRS-based OECT biosensing system 1 includes: a biosensing module 10 configured to detect one or more target bioanalytes and generate a time-domain sensing signal corresponding to the concentration of the one or more target bioanalytes; a signal generator 20 coupled to the biosensing module and configured to generate an excitation signal for application to the biosensing module, and further configured to co-scan the frequency and amplitude of the excitation signal within a working frequency range and a working amplitude range, respectively, so that the biosensing module is excited under multiple frequency-amplitude combinations; and a data processor 30 coupled to the biosensing module and configured to receive and process the time-domain sensing signal to estimate the concentration of the one or more target bioanalytes.

[0092] The data processor 30 is implemented using one or more artificial neural networks (ANNs) and configured to: receive the time-domain sensing signal from the biosensing module in response to multiple frequency-amplitude combinations of the excitation signal; convert the time-domain sensing signal into a frequency-domain sensing signal; analyze the time-domain sensing signal and the frequency-domain sensing signal using the one or more artificial neural networks and the ETRS method to determine one or more optimal excitation conditions, each optimal excitation condition including an operating frequency and operating amplitude associated with a corresponding target bioanalyte; extract one or more resonant characteristics of the biosensing module from the time-domain sensing signal and the frequency-domain sensing signal; and predict the concentration of each of the one or more target bioanalytes based on the determined one or more optimal excitation conditions and the one or more resonant characteristics.

[0093] The biosensing module 10 includes an OECT biochip and a multiplexer (MUX) electrically coupled to the OECT biochip. The OECT biochip includes one or more OECT biosensors, each used to monitor one or more target bioanalytes. The OECT biosensors can be arranged as a biosensing channel array (e.g., 16 OECT biosensors arranged in a 4×4 array).

[0094] When the excitation signal is applied to the biosensing module, each OECT biosensor in the biosensing module is activated by the gate excitation voltage V. gs Driven to generate enhanced drain-source current I ds The enhanced drain-source current I dsThis indicates the concentration of the target bioanalyte sensed by the OECT biosensor.

[0095] Furthermore, the gate-source voltage V applied to each OECT biosensor is measured using ETRS technology. gs The amplitude and frequency are optimized to maximize the affinity efficiency of the corresponding biological receptor for the target bioanalyte. Specifically, V... gs The amplitude and its corresponding optimal frequency (called the resonant frequency) can be determined by adjusting the gate-source voltage V. gs The amplitude and frequency are determined by co-scanning.

[0096] At low frequencies, the capacitor is nearly open-circuited, so most of the gate voltage drops across the channel, which is insensitive to the gate sensing process. At high frequencies, the capacitor is nearly short-circuited, resulting in a minimal cross-channel voltage drop, which weakens the amplification process. In contrast, at the resonant frequency, the greatest synergistic effect is established between the sensing and amplification processes. Changes in biomarker concentration cause significant changes in the voltage drop across the channel region, thereby correspondingly reducing the detection limitation of ETRS and achieving the lowest possible detection limitation.

[0097] The biosensing system 1 may further include a wireless communication module 40, coupled to the signal generator 20 and the data processor 30, and configured to achieve fully wireless data acquisition and transmission. The wireless communication module 40 may include a battery and a Bluetooth Low Energy (BLE) chip. The battery may be a coin cell battery or a rechargeable battery. The wireless communication module may also include a power management unit, such as a low-dropout linear regulator, connected to the battery and effectively regulating and providing a stable voltage output to the BLE chip. For wireless data transmission, the BLE on-chip may employ a miniature ceramic antenna operating at 2.45 GHz.

[0098] Figure 9A and Figure 9B Top and bottom views of the prototype of the biosensing system 1 are shown respectively. To further improve comfort and wearability, a multilayer foldable flexible printed circuit board (fPCB) fabricated on a flexible polyimide (PI) substrate can be used to connect the electronic components and the OECT sensor array, with the components distributed on different sides to simplify the connection between the sensor and the readout system. This stacking strategy reduces the width and length of the system, thereby optimizing its physical dimensions and fit.

[0099] Figure 10Experimental data demonstrating voltage input and current measurements at different frequencies are presented. These results show that the system can generate frequencies ranging from 0.1 Hz to 10 kHz and has high current measurement resolution (down to 1 nA), comparable to laboratory source measurement units (SMUs).

[0100] Figure 11 A schematic diagram of an OECT biosensor according to an embodiment of the present invention is shown. The OECT biosensor includes: a gate electrode G, a source electrode S, a drain electrode D, and an organic channel 11 disposed between the source electrode S and the drain electrode D.

[0101] The channel 11 is formed of an organic conductive material and is in contact with the electrolyte 12. A sensing layer 13 is deposited on the gate electrode G and disposed at the interface between the gate electrode G and the electrolyte 12, and is configured to interact with the target bioanalyte to modulate ion transport within the electrolyte. The sensing layer 13 on the gate electrode G can be modified with specific biological receptors to detect the target bioanalyte, as will be described in more detail later.

[0102] The electrolyte 12 is disposed between the gate electrode G and the channel 11 and is configured to support ion conduction. When the gate-source voltage V gs When applied to the gate electrode G, ions in the electrolyte 12 migrate toward or away from the channel region 11. The migrating ions enter the organic channel 11 or are extracted from the organic channel 11, thereby modulating the doping state of the organic channel.

[0103] The channel 11 is electrically coupled between the source electrode S and the drain electrode D, and is configured to conduct the drain-source current I. ds The drain-source current I ds It depends on the concentration of the target bioanalyte interacting with the sensing layer 13.

[0104] Figure 12 The impedance mathematical models of each circuit component of the OECT biosensor are shown. The coupled mathematical model of the relationship between the voltage drop (Uch) in the channel region and the total equivalent circuit impedance is as follows:

[0105]

[0106] in, The operating frequency is expressed in radians. Gate capacitance, Channel capacitance, and These are the equivalent resistance and equivalent capacitance of the body electrolyte, respectively. This is the equivalent resistance of the sensing layer.

[0107] For details on the working process of the ETRS-based OECT biosensor, please refer to [link / reference needed]. Figure 13A Here, a bioreceptor-modified OECT biosensor is used for the detection of bioanalytes. (i) First, a large gate excitation voltage V is applied to the gate (sensing) electrode of the OECT. gs (e.g., −0.5 V to 0.5 V). (ii) Then, the gate excitation voltage V is... gs The frequency was scanned from 0.1 Hz to 10 kHz to generate the complete ETRS spectrum and locate the resonant frequency value. (iii) Then, after being amplified by the OECT biosensor, the gate excitation voltage V was... gs Converted into an enhanced time-domain sensed current signal I ds (iv) Subsequently, the enhanced sensing current signal I in the time domain is transformed using a Fast Fourier Transform (FFT). ds Transform to the frequency domain. (v) With the support of embedded ANN, transform the frequency domain I at each input frequency. ds The total harmonic distortion (THD) of the signal was analyzed and quantitatively correlated with the concentration changes of relevant bioanalytes.

[0108] Figure 13B The corresponding generated complete ETRS spectrum is shown (in Na). + Taking ions as an example, the resonant frequency can be easily identified (corresponding to the detection limitations shown in the spectrum).

[0109] Figure 14 This is a schematic diagram of the OECT biochip layout, which includes an array of OECT sensing units. Each unit includes concentric electrode pads for the source (S), drain (D), and gate (G), wherein the width of the active channel region is approximately 200 μm.

[0110] Figure 15 An exploded view of the biochip assembly shows the sequential stacking of the electrode layer, channel layer (PEDOT:PSS), insulating layer, and polydimethylsiloxane (PDMS) pore wells on a flexible polyimide substrate.

[0111] See Figure 16An example fabrication process is as follows: (1) Electrodes are patterned by photolithography, and gold is deposited on a polyimide substrate; (2) Poly(3,4-ethylenedioxythiophene):polystyrene sulfonic acid (PEDOT:PSS) is then spin-coated and etched to define channels; and (3) Electrical insulation is achieved using SU8 photoresist, followed by integration of PDMS vias. The final panel shows the assembled biochip array, which is ready for ETRS-based sensing measurements.

[0112] More specifically, the OECT biochip can be fabricated using multilayer photolithography. A flexible substrate can be formed by casting a polyimide solution onto a silicon wafer. Then, source / drain / gate electrodes are constructed by depositing a gold thin film (Cr, 10 nm; Au, 50 nm), and the pattern is defined using a shadow mask.

[0113] Subsequently, an active channel layer was formed by spin-coating PEDOT:PSS onto the device at a rotation speed of 2000 rpm for 1 minute, and then patterned using photolithography and plasma etching processes.

[0114] The device has a channel length of 20 μm and a channel width of 200 μm. The PEDOT:PSS solution was first stirred for 3 minutes, then mixed with 3-(2,3-epoxypropoxy)propyltrimethoxysilane (GOPS, 1%, w / w), glycerol (5%, v / v), and dodecylbenzenesulfonic acid (DBSA, 0.1%, v / v) in a vortex mixer to form a mixed suspension. Glycerol was added to improve the conductivity of the film. DBSA was added to improve the wettability of the film on the substrate.

[0115] Prior to spin-coating the mixed suspension, the suspension was filtered using a polytetrafluoroethylene membrane (0.45 μm pore size) to remove aggregates. Next, SU-8 photoresist was patterned and cured on the gold electrode as an insulating layer to prevent exposure to the aqueous electrolyte. Finally, 3D-printed polydimethylsiloxane (PDMS) vias were transferred onto the device to define the sensing area.

[0116] In some embodiments, such as Figure 17 As shown, the OECT biosensor can be modified into an ion-type biosensor (e.g., Na+) by introducing an ion-selective membrane (ISM) and an ion-conducting polymer (e.g., poly(sodium 4-styrenesulfonate), PSSNa) onto the sensing layer. + A sensor is used to measure changes in ion concentration, which in turn alters the gate response of the OECT.

[0117] Figure 18This demonstrates the modification of ISM on the OECT sensor channel to prepare OECT Na + The sensor steps. As shown in the figure, in step (1), a PSSNa (1.2% w / v, molecular weight approximately 1,000,000 Da) solution is first spin-coated onto the PEDOT:PSS channel at a speed of 1000 rpm. Then, the GOPS-containing polyelectrolyte film is crosslinked by baking at 130 °C for 60 minutes. Subsequently, it is immersed in a 100 mM NaCl solution overnight to allow the Na... + Ions are retained within the film, and excess compounds are removed. Finally, the ISM solution is drop-coated onto the PSSNa film and dried at room temperature.

[0118] The Na + ISM solution through combination of Na + Ion carrier X (1% w / w), bis(2-ethylhexyl) sebacate (DOS, 65.45% w / w), polyvinyl chloride (PVC, 33% w / w), and sodium tetrakis[3,5-bis(trifluoromethyl)phenyl]borate. It was prepared from sodium tetrakis[3,5-bis(trifluoromethyl)phenyl]borate(Na-TFPB)).

[0119] Figure 19 A schematic structure of an ion-selective OECT biosensor is shown. Ion selectivity in the sensor is achieved by employing an ISM containing an ion carrier embedded in a plasticized PVC matrix. To enhance functionality, bulky lipophilic ions, such as Na-TFPB (referred to as "ion sites"), can be introduced into the membrane. These ion sites facilitate the exchange of target ions while simultaneously preventing the passage of ions with opposite charges through the Donnan repulsion effect. DOS, as a commonly used plasticizer, plays an important role in improving the intramembrane carrier mobility. By combining these components, the resulting Na… + ISM combines high ion selectivity with efficient charge transport capabilities.

[0120] Figure 20 The transfer characteristics of a conventional OECT device before and after ISM coating, as well as a device using PSSNa as the internal electrolyte, are shown. The use of an internal electrolyte improves the on / off ratio of the OECT device. Figure 21 It shows that at a fixed V g = 0V、V ds Under the condition of -0.4 V, for different concentrations of Na + K +and Ca 2+ The current response is compared.

[0121] By detecting low concentrations of ionic species (in the form of Na) + Taking ions as an example, the effectiveness of the ETRS-based biosensor was experimentally verified. Figure 22 The method for sensing Na at different frequencies (0.1 Hz, 600 Hz, 10 kHz) is shown. + The signal extracted from the ETRS spectrum of the ions showed that it had better sensitivity at the resonant frequency (600 Hz).

[0122] Figure 23 This demonstrates an ETRS-based biosensor for sensing Na+. + The detection limitations (LoD) of this ETRS sensor compared to other competing technologies are significant. The sensor achieves an optimal LoD of 10 fM at a resonant frequency of 600 Hz, six orders of magnitude lower than conventional EC and ECT sensors.

[0123] In one embodiment, such as Figure 24 As shown, the OECT sensor can be modified (or functionalized) into an aptamer-type biosensor by utilizing aptamer chains anchored to the sensing layer to detect hormones or proteins through high-affinity binding. This conformational change alters the electrochemical properties at the electrolyte-gate interface.

[0124] Figure 25 The diagram illustrates the functionalization process, which includes four steps: (1) electrode preparation; (2) aptamer incubation; (3) blocking with 6-mercaptohexanol (6-MCH); and (4) rinsing.

[0125] Figure 26 The DNA aptamer sequence used for target detection is shown, including the insulin terminal probe and its two-dimensional structure predicted by Mfold. Figure 27 The impedance changes observed on the electrode surface throughout the functionalization process are shown, illustrating the transition from the bare gold surface to the aptamer-modified surface and subsequent 6-MCH closure. Figure 28 The successful modification was verified using square wave voltammetry. A methylene blue redox probe on the aptamer can be used to detect target binding; the binding is reflected by comparing the peak current shift in the absence of cortisol with the presence of 20 μM cortisol.

[0126] In one embodiment, such as Figure 29As shown, the OECT biosensor can be modified (or functionalized) into an enzyme-based OECT biosensor (e.g., a glucose biosensor). The enzyme-based biosensor (green portion) utilizes an enzyme-functionalized (e.g., glucose oxidase) gate electrode (or sensing layer) surface to convert a specific target analyte (e.g., glucose) into an electroactive byproduct that modulates the channel current. More specifically, the functionalized sensing layer surface catalyzes a reaction involving the target bioanalyte to generate one or more electroactive species that modulate the channel current.

[0127] Figure 30 The steps for preparing an enzyme-modified gate electrode for forming an enzyme-type OECT biosensor are shown: (1) The gate electrode is first cleaned with deionized water, acetone and ethanol in sequence and then dried at room temperature; (2) A mediator layer is modified onto the electrode by adding 10 μL of a mixture of aminoferrocene and chitosan (prepared in phosphate-buffered saline (PBS) solution) and drying at room temperature; (3) An enzyme layer is then formed by adding 10 μL of a mixture containing glucose oxidase (500 U / mL) and chitosan and drying at 4°C; (4) Finally, 10 μL of a 0.3% (w / w) glutaraldehyde solution is added at 4°C and allowed to react for 48 hours to crosslink the composite enzyme layer.

[0128] Figure 31 The electron transfer process on the enzyme-modified electrode of a glucose biosensor in the presence of glucose is shown. Figure 32 Cyclic voltammetry curves of the modified electrode in 0.1 M PBS are shown. The presence of redox peaks is attributed to ferrocene, which acts as an electron transfer mediator. Figure 33 Cyclic voltammetry curves of the modified electrode before and after the addition of 50 mM glucose are shown. Figure 34 The response of the OECT glucose sensor to potential interfering substances, including ascorbic acid (AA) and urea, is shown (n = 5). Data are presented as mean ± standard deviation (SD).

[0129] Figure 35 An integrated ETRS-based OECT biosensor system is shown, which integrates an ion-type biosensor unit, an enzyme-type biosensor unit, an aptamer-type biosensor unit, and a cortisol sensing unit to detect insulin, glucose, viral protein (SARS-CoV-2 spike protein), and cortisol, respectively.

[0130] Figures 36A to 36DThis study demonstrates the detection limitations (LoD) of different aptamer-type OECT biosensors in detecting cortisol, insulin, SARS-CoV-2 spike protein, and glucose, further validating the improved sensitivity. All results outperform the best achieved by their respective electrochemical biosensors (i.e., SWV, CV, or EIS). The aptamer-OECT sensors maintained high signal specificity; when faced with non-specific targets, only insignificant signal changes were observed. Figures 37A to 37C ).

[0131] Unique resonant frequencies (600 Hz, 800 Hz, 900 Hz, and 500 Hz) have been identified for each sensor. Specifically, the cortisol biosensor reaches a LoD trough of 10 fM at 600 Hz, two orders of magnitude lower than the reference frequency (1 Hz). The insulin biosensor reaches a LoD trough of 1 fM at 800 Hz, four orders of magnitude lower than the reference frequency. The spike protein biosensor reaches a LoD trough of 0.1 pM at 900 Hz, six orders of magnitude lower than the reference frequency. The enzyme-type glucose biosensor reaches a LoD trough of 1 fM at 500 Hz, three orders of magnitude lower than the reference frequency.

[0132] It should be noted that for all ETRS sensors, the resonant frequency is typically in the range of 1–1000 Hz. This is understandable, as at low frequencies below 1 Hz, longer measurement times are required, making it difficult to establish a stable test environment for analysis. On the other hand, at high frequencies above 1 kHz, the Gm of the OECT drops sharply, thus weakening its amplification capability.

[0133] The ETRS technology proposed in this paper can be immediately applied to advance existing electrochemical methods for detecting low concentrations of bioanalytes. Wearable sensors for detecting disease-related biomarkers are rapidly developing across various applications. These devices not only continuously provide information on individual health and status but are also key tools for collecting datasets needed to build large-scale health models, thereby driving the development of digital health and AI-driven medicine.

[0134] Figure 38 A schematic diagram illustrating the characterization of the OECT device of the present invention and a conventional EC device is shown. Figure 39 The comparison of SNR under simulated noise conditions is shown, where It is worth noting that, under the same excitation voltage mode, the response current of the ETRS sensor is three orders of magnitude higher than that of the reference EIS sensor, resulting in a higher SNR and extremely low LoD.

[0135] To enable wearable applications, a compact and lightweight ETRS readout system is required. According to one embodiment of the invention, a miniaturized ETRS readout system is provided. In one example implementation, the miniaturized ETRS readout system measures 3.5 cm × 3.5 cm × 1.0 cm and weighs only 2.5 g. This compact design allows for seamless integration with smart wearable devices.

[0136] The wearable ETRS platform proposed in this paper can be widely used for the detection of low-concentration bioanalytes. As an example, this paper demonstrates its potential in wearable applications by detecting cortisol, a biomarker related to mental health. Cortisol concentrations can be as low as 1 nM and can be easily obtained from bodily fluids such as sweat. To ensure close contact between the sensor and the skin, the OECT is made of flexible polyimide, allowing for good skin adhesion. The gate electrode (gold) of the flexible OECT is modified with an aptamer to detect cortisol.

[0137] Prior to aptamer immobilization, the gate electrode surface was cleaned. Specifically, the gold surface was first rinsed with ethanol and water. Then, it was treated with piranha solution (H2SO4:H2O2 = 3:1) for 5 minutes, followed by thorough rinsing with water. The treated surface was further incubated in 0.5 M H2SO4 for at least 30 minutes. It was then rinsed with water again and air-dried. 5 µl of the reduced aptamer solution was dropped onto the gold surface and immobilized overnight by self-assembly of Au-S bonds. The surface was then rinsed thoroughly with water to remove unbound aptamers. After drying, it was incubated with 5 µl of 6 mM mercaptohexanol (MCH) for 3 hours to backfill the aptamer monolayer. All incubation steps were performed at room temperature and under controlled humidity to prevent the solution from drying out. The functionalized gate electrode was rinsed with water before use and stored in 1×PBS at 4°C.

[0138] The OECT sensor exhibits stable performance under various bending conditions, thus ensuring its reliability in wearable sweat analysis. The flexible OECT sensor connects to the ETRS readout system via a back-to-back connection and is reinforced with silver paste to prevent disconnection during use.

[0139] Figure 40An example implementation of an integrated wearable ETRS platform for detecting low concentrations of bioanalytes is shown. The device, placed on the user's forearm for sweat-based measurements, includes a multi-channel OECT sensor array and a wearable readout system.

[0140] Figure 41 The sensing mechanism of the OECT biosensor is illustrated. Target molecules (e.g., cortisol) are captured by an aptamer, and the signal is amplified by the OECT.

[0141] Figure 42 The ETRS spectrum generated after frequency scanning is shown, and the resonant frequencies (RF) are identified accordingly, where LF represents low frequency (0.1 Hz) and HF represents high frequency (10 kHz). In wearable measurements, the ETRS is first scanned across the entire frequency range to evaluate sensitivity at different frequencies and identify resonant frequencies. In this experiment, a resonant frequency of 600 Hz was identified, corresponding to the lowest LoD and the highest sensitivity. This frequency was subsequently used in all subsequent experiments.

[0142] Figure 43 Experimental data on cortisol concentration signals extracted from the ETRS spectrum under LF, RF, and HF conditions are presented. The results show that the OECT sensor operating at RF exhibits excellent sensitivity, with a LoD of less than 1 nM, and can adequately cover the concentration range of sweat cortisol.

[0143] See Figure 42 and Figure 43 The wearable ETRS platform accurately monitors cortisol concentration in sweat within a range of 1 nM to 1 µM, and has been validated using reference equipment. The ETRS exhibits the highest sensitivity when operating at its resonant frequency (600 Hz), capable of detecting concentration changes below 1 nM. This sensitivity is not achievable at other reference frequencies (e.g., 0.1 Hz or 10 kHz), thus confirming its feasibility in real-world wearable scenarios.

[0144] It should be understood that the application of ETRS is not limited to wearable scenarios, but can be extended to any application environment that employs electrochemical sensors. For example, with the support of the readout system described herein, ETRS can improve the sensitivity of biochips used for multimodal sensing.

[0145] The functional units and modules according to the embodiments disclosed herein can be implemented using computing devices, computer processors, or electronic circuits, including but not limited to application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), microcontrollers, and other programmable logic devices configured or programmed according to the teachings of this disclosure. Based on the teachings of this disclosure, those skilled in the art of software or electronics can readily write computer instructions or software code that run on the computing devices, computer processors, or programmable logic devices.

[0146] All or part of the methods according to the various embodiments can be executed in one or more computing devices, including server computers, personal computers, laptop computers, and mobile computing devices such as smartphones and tablet computers.

[0147] Various embodiments may further include computer storage media, transient and non-transient storage devices, wherein computer instructions or software code are stored, which can be used to program or configure the computing device, computer processor, or electronic circuit to perform any of the processes of the present invention. The storage media, transient and non-transient storage devices may include, but are not limited to, floppy disks, optical disks, Blu-ray Discs, DVDs, CD-ROMs, magneto-optical disks, ROMs, RAMs, flash memory devices, or any medium or device suitable for storing instructions, code, and / or data.

[0148] The functional units and modules according to various embodiments can also be implemented in a distributed computing environment and / or cloud computing environment, wherein all or part of the machine instructions are executed in a distributed manner by one or more processing devices interconnected by a communication network, such as an intranet, a wide area network (WAN), a local area network (LAN), the Internet, and other forms of data transmission media.

[0149] Although this disclosure has been described and illustrated in conjunction with specific embodiments, such descriptions and drawings are not limiting. The drawings are not necessarily drawn to scale. Differences may exist between the drawings in this disclosure and actual apparatus due to manufacturing processes and tolerances. Other embodiments not specifically shown herein may also exist. Various modifications may be made to adapt particular circumstances, materials, composition, methods, or processes to the purpose and scope of this disclosure. All such modifications are intended to fall within the scope of the appended claims. Although the methods disclosed herein are described in conjunction with specific operations performed in a particular order, it should be understood that these operations may be combined, subdivided, or reordered to form equivalent methods without departing from the teachings of this disclosure. Therefore, unless otherwise specifically stated herein, the order and grouping of operations are not limiting.

Claims

1. An ETRS-based biosensing system, comprising: A biosensing module configured to detect one or more target bioanalytes and generate a time-domain sensing signal corresponding to the concentration of the one or more target bioanalytes; A signal generator, coupled to the biosensing module and configured to generate an excitation signal for application to the biosensing module, is further configured to co-scan the frequency and amplitude of the excitation signal within a working frequency range and a working amplitude range, respectively, so that the biosensing module is excited under multiple frequency-amplitude combinations. as well as A data processor, coupled to the biosensing module and configured to receive and process the time-domain sensing signal to estimate the concentration of one or more target bioanalytes; The data processor is implemented using one or more artificial neural networks and configured as follows: In response to multiple frequency-amplitude combinations of the excitation signal, the time-domain sensing signal from the biosensing module is received; The time-domain sensing signal is converted into a frequency-domain sensing signal; Using the one or more artificial neural networks and the ETRS method, the time-domain sensing signal and the frequency-domain sensing signal are analyzed to determine one or more optimal excitation conditions, each optimal excitation condition including the operating frequency and operating amplitude associated with the corresponding target bioanalyte; Extract one or more resonant characteristics of the biosensing module from the time-domain sensing signal and the frequency-domain sensing signal; and Based on the determined one or more optimal excitation conditions and the one or more resonance characteristics, the concentration of the one or more target bioanalytes is predicted.

2. The ETRS-based biosensing system according to claim 1, wherein, The biosensing module includes an OECT biochip and a multiplexer electrically coupled to the OECT biochip, wherein the OECT biochip includes one or more OECT biosensors, and the one or more OECT biosensors are respectively configured to monitor the one or more target bioanalytes.

3. The ETRS-based biosensing system according to claim 2, wherein, Each OECT biosensor includes: Gate electrode; Source electrode; Drain electrode; An organic channel is disposed between the source electrode and the drain electrode; An electrolyte disposed between the gate electrode and the organic channel; and A sensing layer is deposited on the gate electrode and disposed at the interface between the gate electrode and the electrolyte.

4. The ETRS-based biosensing system according to claim 3, wherein, When the excitation signal is applied to the biosensing module, each OECT biosensor is activated by the gate excitation voltage V. gs Driven to generate enhanced drain-source current I ds The enhanced drain-source current I ds It indicates the concentration of the target bioanalyte sensed by the OECT biosensor.

5. The ETRS-based biosensing system according to claim 4, wherein, The OECT biochip includes an ion-type biosensor, and the sensing layer of the ion-type biosensor is combined with an ion-selective membrane and an ion-conducting polymer.

6. The ETRS-based biosensing system according to claim 4, wherein, The OECT biochip includes an aptamer biosensor with aptamer chains anchored on its sensing layer to detect hormones or proteins through high-affinity binding.

7. The ETRS-based biosensing system according to claim 4, wherein, The OECT biochip includes an enzyme-based OECT biosensor having a functionalized sensing layer surface configured to convert a specific target bioanalyte into an electroactive byproduct to modulate channel current.

8. The ETRS-based biosensing system according to claim 4, wherein, The OECT biochip includes an enzyme-based OECT biosensor having a functionalized sensing layer surface configured to catalyze a reaction involving a target bioanalyte to generate one or more electroactive species, thereby modulating channel currents.

9. The ETRS-based biosensing system according to claim 1 further includes a wireless communication module coupled to the signal generator and the data processor, the wireless communication module being configured to achieve completely wireless data acquisition and transmission.

10. The ETRS-based biosensing system according to claim 1, wherein, The one or more resonant characteristics include at least one of the following: resonant frequency, resonant amplitude, phase response, bandwidth, or quality factor.

11. A biosensing method based on ETRS, comprising: An excitation signal is generated by a signal generator coupled to the biosensing module for application to the biosensing module; The signal generator performs co-scanning of the frequency and amplitude of the excitation signal within the operating frequency range and the operating amplitude range, respectively, so that the biosensing module is excited under multiple frequency-amplitude combinations. The biosensing module detects one or more target bioanalytes; The biosensing module generates a time-domain sensing signal corresponding to the concentration of one or more target bioanalytes. A data processor coupled to the biosensing module receives the time-domain sensing signal from the biosensing module in response to multiple frequency-amplitude combinations of the excitation signal; The data processor converts the time-domain sensing signal into a frequency-domain sensing signal; The data processor, implemented using one or more artificial neural networks, uses the ETRS method to analyze the time-domain sensing signal and the frequency-domain sensing signal to determine one or more optimal excitation conditions, each optimal excitation condition including the operating frequency and operating amplitude associated with the corresponding target bioanalyte; The data processor extracts one or more resonant characteristics of the biosensing module from the time-domain sensing signal and the frequency-domain sensing signal; as well as The data processor predicts the concentration of each of the one or more target bioanalytes based on one or more determined optimal excitation conditions and one or more resonance characteristics.

12. The ETRS-based biosensing method according to claim 11, wherein, The biosensing module includes an OECT biochip and a multiplexer electrically coupled to the OECT biochip, wherein the OECT biochip includes one or more OECT biosensors, and the one or more OECT biosensors are respectively configured to monitor the one or more target bioanalytes.

13. The ETRS-based biosensing method according to claim 12, wherein, Each OECT biosensor includes: Gate electrode; Source electrode; Drain electrode; An organic channel is disposed between the source electrode and the drain electrode; An electrolyte disposed between the gate electrode and the organic channel; and A sensing layer is deposited on the gate electrode and disposed at the interface between the gate electrode and the electrolyte.

14. The ETRS-based biosensing method according to claim 13, wherein, When the excitation signal is applied to the biosensing module, each OECT biosensor is activated by the gate excitation voltage V. gs Driven to generate enhanced drain-source current I ds The enhanced drain-source current I ds It indicates the concentration of the target bioanalyte sensed by the OECT biosensor.

15. The ETRS-based biosensing method according to claim 14, wherein, The OECT biochip includes an ion-type biosensor, and the sensing layer of the ion-type biosensor is combined with an ion-selective membrane and an ion-conducting polymer.

16. The ETRS-based biosensing method according to claim 14, wherein, The OECT biochip includes an aptamer biosensor with aptamer chains anchored on its sensing layer to detect hormones or proteins through high-affinity binding.

17. The ETRS-based biosensing method according to claim 14, wherein, The OECT biochip includes an enzyme-based OECT biosensor having a functionalized sensing layer surface configured to convert a specific target bioanalyte into an electroactive byproduct to modulate channel current.

18. The ETRS-based biosensing method according to claim 14, wherein, The OECT biochip includes an enzyme-based OECT biosensor having a functionalized sensing layer surface configured to catalyze a reaction involving a target bioanalyte to generate one or more electroactive species, thereby modulating channel currents.

19. The ETRS-based biosensing method according to claim 11 further includes a completely wireless data acquisition and transmission achieved by a wireless communication module coupled to the signal generator and the data processor.

20. The ETRS-based biosensing method according to claim 11, wherein, The one or more resonant characteristics include at least one of the following: resonant frequency, resonant amplitude, phase response, bandwidth, or quality factor.

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