Measuring method for sensors based on polymer nanocomposites, and sensor based on polymer nanocomposites

Abstract

The invention relates to a measuring method for a sensor based on polymer nanocomposites, said method having a measuring device connected to the sensor, an analysis module, a parameter identification module and a monitoring module, in which: a. In a first method step, at least three different frequencies within a predetermined frequency range are selected; in a second method step, the measured impedance values are evaluated by an evaluation module in order to determine an impedance model of the sensor, and in a third method step, the measured impedance values are evaluated by an evaluation module in order to determine an impedance model of the sensor. Identifying, by a parameter identification module, an optimal measurement parameter based on the impedance model, where the optimal measurement parameter has the highest sensitivity and selectivity for excitation of the sensor, and d. Using, by a monitoring module, the optimal measurement parameter for real-time monitoring of the sensor response at one or more of the selected frequencies. Furthermore, the invention relates to a polymer nanocomposite-based sensor having a polymer nanocomposite sensor layer wherein the nanocomposite sensor layer has electrically conductive nanoparticles embedded in a polymer matrix wherein the nanoparticles are less than 130 nm in at least one dimension, and an electrode structure in contact with the polymer nanocomposite sensor layer, wherein an electrical signal generated by the sensor as a response to the application of the stimulus can be measured by the electrode structure.

Description

(19) State Intellectual Property Office (12) Invention Patent Application (10) Application Publication Number (43) Application Publication Date (21) Application Number 202480043123.5 (22) Application Date 2024.06.26 (30) Priority Data 102023117192.5 2023.06.29 DE (85) PCT International Application Entering National Phase Date 2025.12.26 (86) PCT International Application Application Data PCT / DE2024 / 100568 2024.06.26 (87) PCT International Application Publication Data WO2025 / 002505 DE 2025.01.02 (71) Applicant Nanosen GmbH Address Germany (72) Inventor R. Ramalingerme O. Cannon (74) Patent Agency China Council for the Promotion of International Trade Patent & Trademark Office Co., Ltd. 11038 Patent Attorney Lou Zhenyan (51) Int.Cl. G01N 27 / 02 (2006.01) B82Y 30 / 00 (2006.01) B82Y 35 / 00 (2006.01) (54) Title of Invention Measurement Method for Sensor Based on Polymer Nanocomposite Materials and Sensor Based on Polymer Nanocomposite Materials (57) Abstract This invention relates to a measurement method for a sensor based on polymer nanocomposite materials, wherein the method has a measuring device connected to the sensor, an analysis module, a parameter identification module and a monitoring module, wherein, a. in a first method step, at least three different frequencies within a predetermined frequency range are selected, and the impedance of the sensor excited at the selected frequencies is subsequently measured by the measuring device within the selected frequency range, and b. in a second method step, the measured impedance value is analyzed by the analysis module to determine the impedance model of the sensor, and c. in a third method step, the optimal measurement parameter is identified by the parameter identification module based on the impedance model, wherein the optimal measurement parameter has the highest sensitivity and selectivity for the excitation of the sensor, and d. the optimal measurement parameter is used by the monitoring module to monitor the sensor response at one or more frequencies within the selected frequencies in real time. Furthermore, the present invention also relates to a sensor based on a polymer nanocomposite material, the sensor having a polymer nanocomposite material sensor layer, wherein the nanocomposite material sensor layer has conductive nanoparticles embedded in a polymer matrix, wherein the nanoparticles are less than 130 nm in at least one dimension, and an electrode structure is in contact with the polymer nanocomposite material sensor layer.The electrode structure allows for the measurement of electrical signals generated by the sensor in response to applied stimuli. Claims 2 pages, Description 8 pages, Drawings 13 pages, Claims amended according to Article 19 of the Treaty 2 pages. CN 121443937 A 2026.01.30 CN 1 21 44 39 37 A 1. A measurement method for a sensor based on polymer nanocomposite materials, wherein the method comprises a measuring device, an analysis module, a parameter identification module, and a monitoring module connected to the sensor, characterized in that: a. in a first method step, at least three different frequencies within a predetermined frequency range are selected, and the impedance of the sensor excited at the selected frequencies is subsequently measured by the measuring device within the selected frequency range; b. in a second method step, the measured impedance values ​​are analyzed by the analysis module to determine an impedance model of the sensor; c. in a third method step, an optimal measurement parameter is identified by the parameter identification module based on the impedance model, wherein the optimal measurement parameter has the highest sensitivity and selectivity for sensor excitation; and d. the optimal measurement parameter is used by the monitoring module to monitor the sensor response at one or more frequencies within the selected frequencies in real time. 2. The method according to claim 1, wherein the impedance measurement comprises the following steps: a. a time / frequency variable current or voltage signal, which is processed using a Discrete Fourier Transform (DFT) to derive a frequency-dependent component; b. processing the frequency-dependent component to calculate a frequency-dependent impedance spectrum Z(f); c. analyzing the impedance spectrum Z(f) by a signal processing unit to obtain the measurement parameters of the sensor. 3. The method according to claim 2, wherein the signal processing unit comprises: an equivalent circuit model for extracting different electrical parameters, and / or a neural network for extracting different features and applying a machine learning model, or distributed relaxation time analysis for determining the distribution of the time constant, or differential impedance analysis for deriving a local equivalent circuit model. 4. The method according to claim 1, wherein the three or more selected frequencies are uniformly distributed within a frequency range. 5. The method according to any preceding claim, wherein the impedance model comprises a series resistance (Rs), a parallel resistance (Rp), and a parallel capacitance (Cp). 6. The method according to any one of the preceding claims, characterized in that the impedance model includes an element (a) having a constant phase, as an alternative for the parallel capacitance (Cp) in the case of a low-saturation semicircular Nyquist plot. 7. The method according to any one of the preceding claims, characterized in that the optimal measurement parameters are determined by evaluating the sensitivity and selectivity of each parameter in the impedance model. 8. The method according to any one of the preceding claims, characterized in that…Real-time monitoring of the sensor response at a selected frequency is performed using embedded circuitry. 9. The method according to any one of the preceding claims, characterized in that the admittance and / or permittivity and / or dielectric constant and / or capacitance of the sensor are measured based on a polymer nanocomposite material within a predetermined frequency range. 10. A sensor based on a polymer nanocomposite material for performing the measurement method according to claim 1, the sensor having a polymer nanocomposite material sensor layer, characterized in that the nanocomposite material sensor layer has conductive nanoparticles embedded in a polymer matrix, wherein the nanoparticles are less than 130 nm in at least one dimension, and an electrode structure is in contact with the polymer nanocomposite material sensor layer, wherein the electrode structure enables the measurement of an electrical signal generated by the sensor as a response to an applied stimulus. 11. The sensor according to claim 10, characterized in that the polymer matrix of the polymer nanocomposite material sensor layer belongs to one or more of the following polymer groups: thermosetting polymers, thermoplastic polymers, crosslinked polymers, elastomeric polymers, biodegradable polymers, and conductive polymers. 12. The sensor according to claim 10 or 11, characterized in that the electrode structure is configured as a parallel plate electrode structure, wherein the sensor layer is arranged between two electrode plates in the parallel plate electrode structure. 13. The sensor according to claim 10 or 11, characterized in that the electrode structure is configured as an interdigitated electrode structure, wherein the sensor layer is mounted or deposited on the electrodes to establish electrical contact. 14. The sensor according to any preceding claim, characterized in that the nanocomposite material is synthesized using techniques such as solution mixing, melt mixing, in-situ polymerization, electrospinning, layer-by-layer coating, and encapsulation polymerization. 15. The sensor according to any preceding claim, characterized in that the nanocomposite material sensor is manufactured using techniques such as spin coating, dip coating, spray coating, layer-by-layer deposition, fiber winding, drop casting, die casting, electrospinning, laser reduction, holding and pressing, 3D printing, screen printing, and inkjet printing. 16. The sensor according to any preceding claim, characterized in that...The electrodes of the electrode structure are manufactured using techniques such as physical vapor deposition, chemical vapor deposition, screen printing, photolithography, inkjet printing, electroplating, or laser ablation. Claims 2 / 2 pages 3 CN 121443937 A Measurement method for a sensor based on polymer nanocomposite materials and a sensor based on polymer nanocomposite materials

[0001] The present invention relates to a measurement method for a sensor based on polymer nanocomposite materials and a sensor based on polymer nanocomposite materials, as described in the preamble of claims 1 and 11. Background Art

[0002] In the field of electrochemistry, the evaluation and performance analysis of different systems are based on the use of equivalent impedance models. This model is typically achieved by applying electrochemical impedance spectroscopy (EIS), which is widely used in the study of sensors, gas sensors, and electrochemical systems (e.g., batteries, fuel cells, corrosion processes, and electrode / electrolyte interfaces) for medical applications.

[0003] Conventionally, EIS is measured over a wide frequency range, typically from a few millihertz to a few megahertz. Based on the obtained Nyquist plot, an equivalent impedance model is developed to represent the system under study. Subsequently, the data obtained from the model are compared with the measured data using curve fitting. In this way, the consistency between the model and experimental results can be determined, where the behavior and characteristics of the system can be evaluated. In some cases, some parameters of the model are also analyzed to represent their influence on the measured parameters.

[0004] This helps to gain a deeper understanding of the system response and the role of individual parameters.

[0005] In the field of electrical impedance spectroscopy, which extends beyond electrochemistry, the focus is on analyzing the electrical properties of different systems, particularly resistive, capacitive, and inductive components. This technique provides insights into the electrical behavior, material properties, and response of the system under study.

[0006] Impedance spectroscopy is sometimes used for the electrical characterization of sensors based on polymer nanocomposite materials, which can be used to measure physical stimuli such as force, pressure, strain, temperature, and humidity. These sensors are characterized over a wide frequency range extending from a few hertz to several megahertz. The use of impedance spectroscopy has been reported in the literature in two aspects. On the one hand, it serves as a characterization tool for verifying measured data against simulated data using equivalent circuit models, or as a measurement method in which the entire impedance spectrum represents the sensor's excitation.

[0007] The classical impedance spectroscopy method, performed over a wide frequency range, is time-consuming. The measurement process can last from several seconds to several minutes, making it impractical for real-time monitoring of the sensor's response. The reason is frequency sampling, as sampling must be performed over a large frequency range in the impedance spectroscopy method. In this method, impedance measurements are performed at many points,This requires a significant amount of time and resources. A large frequency range contributes to the total duration of the measurement process.

[0008] This limitation makes it difficult to capture dynamic changes in sensor behavior.

[0009] Furthermore, the complexity of implementing impedance spectroscopy in embedded systems is a major drawback. The hardware and software requirements for accurately measuring impedance at multiple frequencies can be very extensive. This complexity from development, integration, and maintenance increases the costs and technical requirements associated with integrating such systems into practical applications.

[0010] Another drawback is the limitation of single-frequency measurements. By measuring only at a specific frequency, the sensitivity and selectivity of the sensor can be affected. Different sensor parameters can have different frequency responses, where important information may be lost when analyzing sensor performance at a single frequency. Single-frequency measurements cannot fully capture the behavior of the sensor. This can lead to inaccurate characterization and suboptimal performance, particularly incomplete characterization and impairment of the sensor's sensitivity and selectivity. Specification 1 / 8 pages 4 CN 121443937 A

[0011] Furthermore, the lack of real-time capability hinders applications in dynamic environments where immediate and continuous monitoring of sensor responses is required. The inability to capture dynamic changes and fluctuations in sensor behavior limits its effectiveness in certain applications.

[0012] A method for monitoring a composite material is disclosed in document EP3242128A1, wherein the composite material consists of epoxy resin filled with conductive nanoparticles, wherein at least one electrical property of the composite material (e.g., the impedance of the composite material) is affected by mechanical deformation. The composite material is integrated into a circuit that transmits an electrical signal whose value depends on the electrical properties of the composite material, such that a warning message is issued when a certain threshold is exceeded. Here, the measurement characteristics of the sensor are particularly the impedance. Disadvantages include multiple measurements in the range of 1 mV to 220 V. This results in numerous measurements and thus limits the possibility of real-time monitoring.

[0013] A method and apparatus for rapidly measuring complex resistance or impedance spectra are known in document DE10018745A1, wherein the electrical properties of lipid membranes are acquired by the method and apparatus, wherein the method is capable of measuring and characterizing non-steady-state systems with high time resolution. This relates to an impedance measurement method in a conventional manner, used for characterizing lipid protein membranes in the laboratory and for identifying adsorption processes. This method is unsuitable as a measurement method for field sensors outside the laboratory.

[0014] Document EP2902774B1 describes continuous or near-continuous monitoring and evaluation of the properties of liquid and non-solid materials (especially carbonate hardness), where the application field differs from that of the present invention. In this measurement method, a specific impedance equivalent circuit is proposed, which includes a CPE element connected in series with a parallel circuit formed by a resistor and a capacitor. Furthermore,The patent specifies parameters for measuring the properties of aqueous solutions. The parameters of the equivalent circuit diagram are selected not based on its sensitivity but on its physical meaning. Summary of the Invention

[0015] The object of the present invention is to develop a measurement method for a polymer nanocomposite-based sensor and a polymer nanocomposite-based sensor that provides a simple construction structure and a reliable and rapid measurement method to save time and achieve real-time capability. In addition, a sensor suitable for performing the method should be provided that can operate not only under laboratory conditions.

[0016] This object is solved by the features of claims 1 and 11.

[0017] Advantageous embodiments are derived from the dependent claims.

[0018] The present invention relates to a measurement method for a polymer nanocomposite-based sensor, wherein the method has a measuring device, an analysis module, a parameter identification module and a monitoring module connected to the sensor, wherein in a first method step, at least three different frequencies within a predetermined frequency range are selected, and the impedance of the sensor excited at the selected frequencies is subsequently measured by the measuring device within the selected frequency range. However, more frequencies within the frequency range are also possible.

[0019] In the second method step, the measured impedance value is analyzed by the analysis module to determine the impedance model of the sensor.

[0020] Subsequently, in the third method step, the optimal measurement parameters are identified by the parameter identification module based on the impedance model, wherein the optimal measurement parameters have the highest sensitivity and selectivity for the excitation of the sensor. In the fourth step, the optimal measurement parameters are used by the monitoring module to monitor the sensor response at one or more selected frequencies in real time.

[0021] Preferably, the impedance measurement includes the following steps:

[0022] a. A time / frequency variable current or voltage signal, processed using Discrete Fourier Transform (DFT) to derive frequency-dependent components,

[0023] b. Processing the frequency-dependent components to calculate the frequency-dependent impedance spectrum Z(f),

[0024] c. Analyzing the impedance spectrum Z(f) by the signal processing unit to obtain the measurement parameters of the sensor. Instruction manual, page 2 / 8, CN 121443937 A

[0025] The sensor output is acquired and analyzed in such a manner that the sensor under test is first loaded with a current or voltage signal that varies by time / frequency without an external excitation signal, and the corresponding voltage or current pulses are measured. These signals are then separated using signal analysis techniques such as Discrete Fourier Transform (DFT) to extract the corresponding frequency-dependent voltage U(f) and current I(f) that form the basis for calculating Z(f).Alternatively, frequency-dependent gain and phase can be directly extracted. The frequency range typically used is from 1 Hz to 100 MHz. This frequency range can be extended depending on sensor effects and sensor size. This analysis provides insight into the complex electrical behavior of polymer nanocomposite sensors.

[0026] The obtained impedance spectrum is then processed using a signal processing unit. This can advantageously be based, for example, on equivalent circuit models (ECM), neural networks (NN), distributed relaxation time (DRT) calculations, differential impedance analysis (DIA) calculations, or combinations of these methods with other impedance spectroscopy signal processing methods (e.g., digital filters). Each of these methods can provide different key metrics, such as different electrical parameters of the ECM, different features and machine learning models of the NN, the time constant distribution of the DRT, and the local equivalent circuit model of the DIA. These key metrics are then used to track and measure the desired measurement parameters of the sensor.

[0027] In an advantageous design of this method, the three or more selected frequencies are uniformly distributed across the frequency range.

[0028] The impedance model preferably includes series resistance (Rs), parallel resistance (Rp), and parallel capacitance (Cp).

[0029] In a design of this method, the impedance model may include elements having a constant phase (a) as an alternative for parallel capacitance (Cp) in the case of a suppressed semi-circular Nyquist diagram.

[0030] Preferably, the optimal measurement parameters are determined by evaluating the sensitivity and selectivity of each parameter in the impedance model.

[0031] In an advantageous design of this method, real-time monitoring of the sensor response at a selected frequency is performed using embedded circuitry.

[0032] The admittance and / or permittivity and / or dielectric constant and / or capacitance of the sensor are preferably measured based on polymer nanocomposite materials within a predetermined frequency range.

[0033] The polymer nanocomposite-based sensor according to the invention has a polymer nanocomposite sensor layer, wherein the nanocomposite sensor layer has conductive nanoparticles embedded in a polymer matrix, wherein the nanoparticles are less than 130 nm in at least one dimension. An electrode structure contacts the polymer nanocomposite sensor layer, wherein the electrical signal generated by the sensor as a response to an applied stimulus can be measured by means of the electrode structure. Particularly preferred is that the nanoparticles have a diameter of less than 100 nm in at least one dimension. These nanoparticles are responsible for providing the desired conductivity. They can be metallic, carbon-based, or a combination of both.

[0034] The polymer matrix of the polymer nanocomposite sensor layer belongs to one or more of the following polymer groups:In particular, thermosetting polymers, thermoplastic polymers, crosslinked polymers, elastomeric polymers, biodegradable polymers, and / or conductive polymers. The choice of polymer matrix depends on the specific requirements and desired performance of the sensor.

[0035] The electrode structure is configured as a parallel plate electrode structure, in which the sensor layer is arranged between two electrode plates.

[0036] Alternatively, the electrode structure can be configured as an interdigitated electrode structure, in which the sensor layer is mounted on or deposited on the electrodes to establish electrical contact.

[0037] The two main types of electrode structures described above are commonly used in sensor construction. In the first type, the parallel plate electrode structure is configured such that the sensor layer is arranged between two electrode plates. This configuration ensures that the electric field is uniformly distributed on the sensor layer. The second type is the interdigitated electrode structure, in which the electrodes are arranged in an interlaced pattern. In this configuration, the sensor layer is coated on or deposited on the electrodes to establish electrical contact. Different techniques are used to manufacture these electrode structures depending on the desired substrate and the requirements of the sensor.

[0038] Nanocomposite materials for sensors can be synthesized using techniques such as solution mixing, melt mixing, in-situ polymerization, electrospinning, layer-by-layer coating, and encapsulation polymerization.

[0039] In advantageous designs, nanocomposite material sensors are fabricated using techniques such as spin coating, dip coating, spray coating, layer-by-layer deposition, fiber winding, drop casting, die casting, electrospinning, laser reduction, hold-pressing, 3D printing, screen printing, and inkjet printing.

[0040] Preferably, electrodes for electrode structures are fabricated using techniques such as physical vapor deposition, chemical vapor deposition, screen printing, photolithography, inkjet printing, electroplating, or laser ablation.

[0041] The choice of coating technique depends on factors such as desired sensor design, substrate compatibility, and manufacturing requirements.

[0042] The proposed invention offers several advantages compared to existing technologies and, in particular, conventional methods for impedance measurement in sensors based on polymer nanocomposite materials. On the one hand, conventional methods are time-consuming, involving impedance spectroscopy over a wide frequency range. Data acquisition typically lasts from a few seconds to a few minutes, making real-time monitoring of sensor response impractical. In contrast, the method according to the invention uses the minimum of three specifically selected frequencies. This results in faster measurement times without compromising accuracy.

[0043] Furthermore, conventional methods require complex embedded systems to perform impedance spectroscopy measurements. This complexity limits the practical implementation of the measurement method, especially in applications requiring real-time monitoring. The solution according to the invention shortens the measurement process and enables the use of less complex embedded circuitry without compromising sensor performance.

[0044] In addition,The applied multi-frequency measurement provides additional advantages. By simultaneously measuring impedance at multiple frequencies, optimal measurement parameters can be determined, which have the highest sensitivity and selectivity for sensor excitation. These measurement parameters are crucial for the accurate characterization and monitoring of the sensor response.

[0045] The sensor of the present invention is particularly suitable for performing the method according to the invention. Here, the sensor is particularly used for measuring force, temperature, strain, and humidity. Detailed Description

[0046] The invention is further described below with reference to embodiments and accompanying drawings.

[0047] Figure 1 shows a polymer nanocomposite sensor layer 1 between parallel plate electrode structures 2. Figure 2 shows an alternative construction of the sensor in the form of a polymer nanocomposite sensor layer 1 combined with an interdigitated electrode structure 3.

[0048] Figure 3 shows a sequence of measurements providing different parameters of the sensor. For this purpose, calculations are performed in a calculation unit 4 and passed to a signal processing unit 5. The signal processing unit 5 is used to determine key parameters 6.

[0049] Figure 4 shows a graph illustrating the correlation between different components of impedance and the excitation signal. According to Figure 5, some or all of these parameters are input to the signal processing unit 5, which provides measurement parameters 7.

[0050] Figure 6 shows a typical Nyquist plot of a sensor based on polymer nanocomposite materials, where the curves are shown in variations with parallel capacitance and with constant phase elements. A typical equivalent circuit diagram for the variation with parallel capacitance and constant phase elements is shown in Figure 7. Figure 8 shows a representation of the typical true progression of the impedance curve in a logarithmic manner relative to frequency, where at least three frequencies are selected that are equidistant at different frequency octaves. Figure 9 shows a graph illustrating the correlation between different parameters of the equivalent circuit diagram and the measured quantity.

[0051] An example using an ECM as a signal processing unit is illustrated in more detail in Figures 6 through 9. The frequency-dependent impedance of the sensor is determined according to the sensor specification page 4 / 8 7 CN 121443937 A, and a Nyquist plot (Figure 6) is created, which shows the complex impedance of the sensor. The Nyquist plot shows three parameters of interest: series resistance (Rs), parallel resistance (Rp), and parallel capacitance (Cp). In some cases, the Nyquist plot can have a depressed semi-circular shape, indicating the presence of a constant-phase element (CPE) instead of Cp. By carefully analyzing the Nyquist plot and extracting relevant parameters, a comprehensive ECM (Figure 7) can be created to represent the electrical response of the polymer nanocomposite-based sensor.

[0052] To ensure a comprehensive analysis of the polymer nanocomposite-based sensor,At least three frequencies are selected for the three measurement parameters (as shown in Figure 8). These measurement parameters (see Figure 9) are then used to measure the sensor's response at one or more selected frequencies. This method significantly improves the sensor's sensitivity and selectivity to specific measurement parameters. This enhancement enables more precise and accurate measurement of the sensor's response to a desired excitation signal. Therefore, the overall performance of the sensor and its ability to identify and distinguish specific excitation signals in real time are significantly improved.

[0053] Furthermore, this measurement method can be implemented with different impedance-related quantities. These include complex admittance (G* = 1 / Z*), dielectric modulus (M* = jωZ*), and capacitance (K* = 1 / M*). To improve the effectiveness of the method, two or more frequencies can be selected. By including multiple frequencies, a more robust and accurate model can be obtained, resulting in improved accuracy, sensitivity, and selectivity of the sensor.

[0054] In addition to sensitivity and selectivity, this method also allows for the analysis and monitoring of various other characteristics of the sensor. These characteristics include linearity, aging characteristics, uniformity, and many other characteristics. By applying the same method, a comprehensive understanding of the sensor's performance and behavior can be achieved, allowing for a thorough evaluation and optimization of its overall functionality.

[0055] With the aid of the proposed invention, an optimized measurement method for polymer nanocomposite-based sensors is provided, focusing on simultaneous impedance measurements at multiple frequencies within a specific frequency range, where the frequencies are matched to the sensor's excitation. According to this method, a comprehensive set of impedance values ​​is measured, representing different properties of the polymer nanocomposite material.

[0056] By analyzing the measured impedances, different parameters within the impedance model are derived, precisely representing the behavior of the polymer nanocomposite sensor. Each parameter is associated with a specific characteristic or property of the sensor. Considering the relationship between the parameters and the desired sensor performance, the parameters that exert the greatest influence on achieving the desired results can be identified.

[0057] The determined parameters (the so-called optimal measurement parameters) are then used as key factors for sensor operation and for performance optimization. By monitoring the optimal sensor parameters, the drawbacks of conventional measurement methods are greatly minimized, and the overall performance of the polymer nanocomposite-based sensor is maximized.

[0058] An exemplary impedance spectroscopy analysis of a polymer nanocomposite-based sensor is described below.

[0059] An exemplary sensor is a sensor based on a polymer nanocomposite material combined with a contact electrode structure. In this example, the sensor is used as a force sensor, and its electrical characteristics change when an external force is applied to it. The sensor is connected to a device for impedance measurement to study its electrical characteristics. Furthermore,Examples of such devices include impedance analyzers, LCR meters, network analyzers, electrochemical impedance spectroscopy devices, frequency response analyzers, oscilloscopes with impedance functions, digital multimeters with impedance functions, and embedded systems based on integrated chips for impedance measurement, which have integrated microcontrollers or microprocessors. The device is configured to measure the impedance of a sensor over a frequency range of 1 Hz to 100 MHz. The real component (resistance) and imaginary component (reactance) of the impedance are detected. These measurements correspond to the sensor's response to different weights applied to the sensor. Figures 10 and 11 show Bode plots of the real and imaginary components of the impedance with respect to frequencies from 100 Hz to 1 MHz for different applied weights.

[0060] The data is then recorded as Nyquist curves, as shown in Figure 12, which represent the real and imaginary parts of the impedance. This representation is particularly useful for visualizing the complex impedance behavior of the sensor. By analyzing the Nyquist curves, characteristic semi-circular patterns and other shapes reflecting the electrical properties of the sensor can be identified. Based on the Nyquist curve and Bode plot, three or more different frequencies can be selected. In this example, a first frequency (500Hz) is selected between 100Hz and 1kHz, a second frequency (5kHz) between 1kHz and 10kHz, and a third frequency (50kHz) between 10kHz and 100kHz.

[0061] An exemplary measurement device based on an embedded solution is shown below.

[0062] Considering the selected frequencies, a portable solution for measurement using sensors has been developed. This portable solution can be based on c-DAQ, FPGA, or microcontroller. Compared to other solutions, the microcontroller-based solution is cost-effective, compact, and power-efficient. The different functional modules of the embedded system are shown in Figure 13 and include a signal processing unit, a bias cancellation module, an optional multiplexer or switch matrix module, a voltage-controlled current source (VCCS), a device under test, a measurement system, a preamplifier, a signal conditioning device, and a microcontroller unit containing an analog-to-digital converter (ADC), a digital signal processor (DSP), and an impedance calculator.

[0063] The signal processing unit is used to synthesize an excitation signal with a selected frequency, which is implemented by an integrated pulse width modulation (PWM) or digital-to-analog converter (DAC) or by an external chip such as a direct digital synthesizer (DDS) or arbitrary signal generator (AWG). Since most signal generation units can only provide positive voltages, a bias voltage (DC bias) is always present. In order to perform impedance measurements without a DC pre-bias, the bias voltage must be removed from the DDS, DAC, and PWM components.This can be achieved using a subtractor or a high-pass filter. When using more than one DUT or a DUT used as an array or matrix, an optional multiplexer / switch matrix module is required. VCCS is essential for maintaining a constant current in the circuit by regulating the current to correspond to the input voltage regardless of the sensor impedance. Various VCCS architectures can be used, including load-in-the-loop, Howland circuits and their derivatives, Tietze circuits, current delivery units (CCII), and operational transconductance amplifiers (OTA), with Howland circuits being particularly suitable for high-frequency measurements.

[0064] The excitation signal is transmitted to the sensor connected to the measurement system. The measurement system is based on the I-U method, bridge mode, resonant method, or self-balancing bridge.

[0065] I-U method: Based on simultaneous measurement of voltage and current, AC analysis is performed to extract the amplitude and phase of the current and voltage signals to extract the impedance.

[0066] - Bridge System: Based on the balance of two impedance arms, one of which contains a reference impedance and the other of which contains the object under test. In the balanced state, the reference impedance and the sensor have the same voltage, so that no current flows between the arms.

[0067] - Resonance Method: In this method, a sinusoidal signal is fed into the system and the response is measured to determine the impedance. The impedance can be calculated by analyzing the frequency at which the maximum response occurs.

[0068] - Self-Balancing Bridge: Uses an automatically phase-shifted reference signal to simulate the impedance response. If the signal is symmetrical, it simulates the reference object under test, and the system is matched.

[0069] Both the I-U method and the self-balancing bridge have very good measurement accuracy and can measure frequencies up to 1 MHz. In an I-U method-based measurement system, an excitation signal generator injects voltage (potential constant mode) or current (current mode) into the sensor. A preamplifier module is used when signal amplification is required for better identification. The signal conditioning module typically consists of an active filter, a differential operational amplifier, an instrumentation amplifier, and an amplifier. The signal conditioning unit ensures that the microcontroller can read and interpret the signal, thereby reducing noise and amplifying the signal to a level corresponding to the microcontroller's voltage level (e.g., 0 to 3.3V).

[0070] For accurate impedance measurement, it is important to measure the system's response and the implemented excitation signal. Synchronization of the timers responsible for excitation and voltage and current measurements is crucial. Starting from the current and voltage signals in the time domain, an AC electrical analysis is performed (see page 6 / 8 of the specification, 9 CN 121443937 A) to determine the amplitude ratio and phase shift between the voltage and current signals.

[0071] This can be accomplished using analog circuitry or digital signal processing. In analog circuitry such as I / Q demodulation or gain phase detectors (GPDs),The amplitude and phase of the response signal are demodulated by an analog multiplication circuit, followed by a low-pass filter. The real and imaginary values ​​are output as DC voltages using an I / Q demodulator, while the gain and phase are output as DC voltages using a GPD. In digital signal processing, the voltage and current signals are conditioned and then directly connected to the ADC. The microcontroller extracts the amplitude and phase after digital AC analysis.

[0072] The extracted amplitude and phase of the voltage and current signals are then analyzed using a DFT (Discrete Fourier Transform) solution, where methods such as Fast Fourier Transform (FFT) and Goertzel filters are used to accelerate the calculation of DFT coefficients. Methods such as Discrete-Time Fourier Transform (DTFT), sine fitting via Ordinary Linear Least Squares (OLS), and Nonlinear Least Squares (NLLS) can also be used. Microcontrollers such as those based on ARM technology (e.g., STM32) utilize dedicated libraries for efficient signal processing (e.g., CMSIS), which support operations such as FFT for signal lengths up to 4096 and improve computational power and memory management in impedance analysis applications.

[0073] After calculating the real and imaginary parts of the voltage and current signals using an AC analysis method, the impedance at the excitation frequency is determined. For the excitation frequency index (f), this is done by a complex division of the voltage U(f) by the current I(f), as follows:

[0074]

[0075] The equivalent circuit model corresponding to the sensor can be used to decompose the sensor's measurement impedance and calculate the different components of the impedance. Microcontroller-based solutions can also be connected to ICs specifically developed for impedance measurement and represent compact and energy-efficient solutions. Examples of these impedance measurement ICs are AFE4300, MAX32600, AD5933, and AduCM350.

[0076] The sensor is then excited with three selected frequencies, and the impedance variation is determined for different applied weights. The obtained information is then input into the equivalent circuit model to calculate the different components of the impedance. Understanding the equivalent circuit model is essential for predicting sensor behavior under different conditions and for optimizing the design in terms of improved sensitivity. In this example, as can be seen from the Nyquist plot in Figure 14, it is not represented by a perfect semicircle, indicating the presence of a constant-phase element (CPE). The resulting equivalent circuit represents the impedance characteristics of the sensor through a combination of electrical components such as series resistance (Rs), parallel resistance (Rp), and parallel capacitance or constant-phase element (CPE), as shown in Figure 13, and is expressed as:

[0077]

[0078] Wherein,

[0079] Rs is the resistance between the contact electrode and the sensor material and the intrinsic resistance of the conductive nanoparticles in the sensor material,

[0080] Rp is the tunneling resistance between nanoparticles in the polymer matrix,

[0081] CPE is the frequency-dependent impedance caused by the time constant of non-uniformity or distribution.

[0082] CPE is expressed as:

[0083]

[0084] where, Specification 7 / 8 pages 10 CN 121443937 A

[0085] Q is a constant,

[0086] ω is the angular frequency,

[0087] α is a parameter extending from 0 to 1, and for α=1, CPE behaves like an ideal capacitor.

[0088] The identification of the measurement parameters is shown in Figure 15, where the Rs value is significantly smaller than the values ​​of the other components. The detailed representation of the Rs value shows that Rs is not significantly affected by changes in the applied weight, indicating that high-frequency components are unsuitable for this sensor.

[0089] However, both Rp and CPE vary significantly with the applied weight at frequencies from 100Hz to 1MHz. One or more frequencies can be selected at different intervals within the frequency range to understand the effect of frequency on different electrical parameters. For example, consider three frequencies:

[0090] 1. First frequency (500Hz): chosen between 100Hz and 1kHz. As can be seen from Figures 16 and 17, at this frequency, the real part of the impedance is more sensitive to the applied weight than the imaginary part.

[0091] 2. Second frequency (5kHz): chosen between 1kHz and 10kHz. As can be seen from Figures 18 and 19, at this frequency, the real part of the impedance is less affected by changes in the applied weight; however, the imaginary part shows good sensitivity.

[0092] 3. Third frequency (50kHz): chosen between 10kHz and 100kHz. As can be seen from Figures 20 and 21, at this frequency, the real part is almost unaffected by changes in the applied weight, while the imaginary part shows a relatively linear sensitivity to the applied weight.

[0093] Comparing the sensitivity of the parameters at these three frequencies from Figures 22, 23, and 24, the third frequency (50 kHz) shows good sensitivity and better linearity for the imaginary part of the impedance, i.e., CPE, with negligible influence from the real part, i.e., Rs and Rp. Therefore, CPE is the optimal measurement parameter for this sensor.

[0094] Regarding real-time monitoring of the sensor, the measuring device is programmed to measure the optimal measurement parameters of the sensor, particularly CPE, at a frequency of 50 kHz. To improve stability,Additional frequencies near the selected frequency can be used to measure the sensor. Averaging the CPE of the impedance at these frequencies allows for stable, real-time monitoring of the sensor response. List of reference numerals:

[0095] 1 Polymer nanocomposite sensor layer

[0096] 2 Plate-shaped electrode structure

[0097] 3 Electrode structure

[0098] 4 Computation unit

[0099] 5 Signal processing unit

[0100] 6 Key indicators

[0101] 7 Measurement parameter specification 8 / 8 pages 11 CN 121443937 A Figure 1 Figure 2 Figure 3 Specification figure 1 / 13 pages 12 CN 121443937 A Figure 4 Figure 5 Specification figure 2 / 13 pages 13 CN 121443937 A Figure 6 Figure 7 Specification figure 3 / 13 pages 14 CN 121443937 A Figure 8 Figure 9 Specification figure 4 / 13 pages 15 CN 121443937 A Figure 10 Figure 11 Specification figure 5 / 13 pages 16 CN 121443937 A Figure 12 Figure 13 of the instruction manual, page 6 / 13, CN 121443937 A; Figure 14 of the instruction manual, page 7 / 13, CN 121443937 A; Figure 15 of the instruction manual, page 8 / 13, CN 121443937 A; Figure 16 of the instruction manual, CN 121443937 A; Figure 18 of the instruction manual, page 9 / 13, CN 121443937 A; Figure 19 of the instruction manual, page 10 / 13, CN 121443937 A; Figure 20 of the instruction manual, page 21 / 11 / 13, CN 121443937 A; Figure 22 of the instruction manual, CN 121443937 A; Figure 23 of the instruction manual, page 12 / 13, CN 121443937 A; Figure 24 of the instruction manual, page 13 / 13, CN 121443937 A. 1. A measurement method for a sensor based on polymer nanocomposite materials, wherein the method comprises a measuring device, an analysis module, a parameter identification module, and a monitoring module connected to the sensor, characterized in that force, temperature, strain, and humidity are measured by the measurement method, wherein: a. in a first method step, at least three different frequencies within a predetermined frequency range are selected, and the impedance of the sensor excited at the selected frequencies is subsequently measured by the measuring device within the selected frequency range, and a Nyquist plot for illustrating the complex impedance of the sensor is subsequently created, and b. in a second method step,The impedance values ​​measured in the equivalent circuit diagram obtained from the Nyquist plot in the form of an impedance model are analyzed in the form of series resistance (Rs), parallel resistance (Rp), and parallel capacitance (Cp), or elements (CPE) with constant phase replacing parallel capacitance (Cp). In the third method step, the parameter identification module identifies optimal measurement parameters in the form of series resistance (Rs), parallel resistance (Rp), and parallel capacitance (Cp), or elements (CPE) with constant phase replacing parallel capacitance (Cp), based on the impedance model. These optimal measurement parameters have the highest sensitivity and selectivity for sensor excitation. The monitoring module uses these optimal measurement parameters to monitor the sensor response at one or more selected frequencies in real time. 2. The method of claim 1, wherein the impedance measurement comprises the following steps: a. a time / frequency variable current or voltage signal, which is processed using a discrete Fourier transform (DFT) to derive a frequency-dependent component; b. processing the frequency-dependent component to calculate a frequency-dependent impedance spectrum Z(f); c. analyzing the impedance spectrum Z(f) by a signal processing unit to obtain measurement parameters of the sensor for corresponding measurements of force, temperature, strain, and humidity. 3. The method of claim 2, wherein the signal processing unit comprises: an equivalent circuit model for extracting different electrical parameters, and / or a neural network for extracting different features and applying a machine learning model, or a distributed relaxation time analysis for determining the distribution of the time constant, or a differential impedance analysis for deriving a local equivalent circuit model. 4. The method of claim 1, wherein the three or more selected frequencies are uniformly distributed within a frequency range. 5. The method of any preceding claim, wherein the impedance model comprises an element (a) with a constant phase, as an alternative for a parallel capacitor (Cp) in the case of a suppressed semi-circular Nyquist plot. 6. The method according to any one of the preceding claims, characterized in that the optimal measurement parameters are determined by evaluating the sensitivity and selectivity of each parameter in the impedance model. 7. The method according to any one of the preceding claims, characterized in that real-time monitoring of the sensor response at a selected frequency is performed using embedded circuitry. 8. The method according to any one of the preceding claims, characterized in that the admittance and / or permittivity and / or dielectric constant and / or capacitance of the sensor are measured based on a polymer nanocomposite material within a predetermined frequency range. 9. A polymer nanocomposite material-based sensor for performing the measurement method according to claim 1, the sensor having a polymer nanocomposite material sensor layer, characterized in that the nanocomposite material sensor layer has conductive nanoparticles embedded in a polymer matrix, wherein the nanoparticles are less than 130 nm in at least one dimension.Furthermore, the electrode structure contacts the polymer nanocomposite sensor layer, wherein the electrode structure enables the measurement of electrical signals generated by the sensor in response to an applied stimulus, and the sensor enables the measurement of force, temperature, strain, and humidity. 10. The sensor according to claim 10, characterized in that the polymer nanocomposite sensor layer, as amended under Article 19 of the Treaty, has a polymer matrix belonging to one or more of the following polymer groups: thermosetting polymers, thermoplastic polymers, crosslinked polymers, elastomer polymers, biodegradable polymers, and conductive polymers. 11. The sensor according to claim 10 or 11, characterized in that the electrode structure is configured as a parallel plate electrode structure, in which the sensor layer is arranged between two electrode plates. 12. The sensor according to claim 10 or 11, characterized in that the electrode structure is configured as an interdigitated electrode structure, in which the sensor layer is mounted or deposited on the electrodes to establish electrical contact. 13. The sensor according to any one of the preceding claims, characterized in that the nanocomposite material is synthesized using techniques such as solution mixing, melt mixing, in-situ polymerization, electrospinning, layer-by-layer coating, and encapsulation polymerization. 14. The sensor according to any one of the preceding claims, characterized in that the nanocomposite material sensor is manufactured using techniques such as spin coating, dip coating, spray coating, layer-by-layer deposition, fiber winding, drop casting, die casting, electrospinning, laser reduction, holding and pressing, 3D printing, screen printing, and inkjet printing. 15. The sensor according to any one of the preceding claims, characterized in that the electrodes of the electrode structure are manufactured using techniques such as physical vapor deposition, chemical vapor deposition, screen printing, photolithography, inkjet printing, electroplating, or laser ablation. Claims amended according to Article 19 of the Treaty, 2 / 2 pages, 26 CN 121443937 A,

Claims

1. A measurement method for a sensor based on a polymer nanocomposite, wherein the method has a measurement device connected to the sensor, an analysis module, a parameter identification module and a monitoring module, characterized in that: a. in a first method step, at least three different frequencies within a predetermined frequency range are selected and subsequently the impedance of the sensor excited at the selected frequencies is measured in the selected frequency range by the measurement device, and b. in a second method step, the measured impedance values are analyzed by the analysis module to determine an impedance model of the sensor, and c. in a third method step, the best measurement parameters are identified by the parameter identification module based on the impedance model, wherein the best measurement parameters have the highest sensitivity and selectivity for the excitation of the sensor, and d. the best measurement parameters are used by the monitoring module for real-time monitoring of the sensor response at one or more of the selected frequencies. The impedance measurement comprises the following steps: a. a time / frequency variable current or voltage signal, which is processed using a discrete Fourier transform (DFT) to derive frequency-dependent components, b. the frequency-dependent components are processed to calculate a frequency-dependent impedance spectrum Z(f), c. the impedance spectrum Z(f) is analyzed by a signal processing unit to obtain measurement parameters of the sensor. The signal processing unit comprises: equivalent circuit models for extracting different electrical parameters, and / or neural networks for extracting different features and applying machine learning models, or distribution relaxation time analysis for determining a distribution of time constants, or differential impedance analysis for deriving a local equivalent circuit model. The three or more selected frequencies are uniformly distributed within the frequency range. The best measurement parameters are determined by evaluating the sensitivity and selectivity of each parameter in the impedance model.

2. The method of claim 1, wherein, The real-time monitoring of the sensor response at the selected frequencies is performed in the case of using an embedded circuit. The admittance and / or the permittivity and / or the dielectric constant and / or the capacitance of the sensor is measured based on the polymer nanocomposite within a predetermined frequency range. The nanocomposite sensor layer has electrically conductive nanoparticles embedded in a polymer matrix, wherein the nanoparticles are smaller than 130 nm in at least one dimension, and an electrode structure is in contact with the polymer nanocomposite sensor layer, wherein the electrical signal generated by the sensor as a response to the applied stimulus can be measured through the electrode structure. The polymer matrix of the polymer nanocomposite sensor layer belongs to one or more of the following polymer groups: thermoset polymers, thermoplastic polymers, cross-linked polymers, elastomeric polymers, biodegradable polymers and conductive polymers.

3. The method of claim 2, wherein, The electrode structure is configured in the form of a parallel plate electrode structure, in which the sensor layer is arranged between two electrode plates.

4. The method of claim 1, wherein, The electrode structure is configured in the form of an interdigital electrode structure, in which the sensor layer is mounted or deposited on the electrodes to establish electrical contact.

5. The method according to any of the preceding claims, characterized in that, The impedance model includes a series resistance (R s ), a parallel resistance (R p ), and a parallel capacitance (C p ).

6. The method according to any of the preceding claims, characterized in that, The impedance model comprises an element (a) with constant phase as a replacement for a parallel capacitance (C p ) in case of a depressed semicircular Nyquist plot.

7. The method according to any of the preceding claims, characterized in that, The nanocomposite is synthesized using techniques such as solution mixing, melt mixing, in-situ polymerization, electrospinning, layer-by-layer coating and encapsulated polymerization.

8. The method according to any of the preceding claims, characterized in that ​ 9. The method according to any of the preceding claims, characterized in that, ​ 10. A sensor based on a polymer nanocomposite for carrying out the measuring method according to claim 1, the sensor having a polymer nanocomposite sensor layer, characterized in that ​ 11. The sensor of claim 10, wherein, ​ 12. The sensor according to claim 10 or 11, characterized in that ​ 13. The sensor of claim 10 or 11, wherein, ​ 14. The sensor according to any of the preceding claims, characterized in that ​ 15. The sensor of any preceding claim, wherein, The nanocomposite sensor is manufactured using techniques such as spin coating, dip coating, spray coating, layer-by-layer deposition, fiber winding, drop casting, mold casting, electrospinning, laser reduction, hold-pressing, 3D printing, screen printing, and inkjet printing.

16. The sensor of any preceding claim, wherein The electrodes of the electrode structure are manufactured using techniques such as physical vapor deposition, chemical vapor deposition, screen printing, photolithography, inkjet printing, electroplating, or laser ablation.

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