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

Abstract

The invention relates to a measuring method for sensors based on polymer nanocomposites. The method involves a measuring device which is connected to the sensor, an analysis module, a parameter-identification module, and a monitoring module, wherein a. in a first step, at least three different frequencies are selected within a specified frequency range, and the impedance of the sensor, which is excited in the selected frequencies, is then measured in the selected frequency range by means of the measuring device, b. in a second step, the measured impedance values are analyzed in order to determine an impedance model of the sensor by means of the analysis module, c. in a third step, the optimal measuring parameter is identified on the basis of the impedance model by means of the parameter-identification module, said optimal measuring parameter having the highest degree of sensitivity and selectivity for the excitation of the sensor, and d. the optimal measuring parameter is used by the monitoring module for a real-time monitoring of the sensor reaction in one or a plurality of the selected frequencies. The invention additionally relates to a sensor based on polymer nanocomposites, comprising a polymer nanocomposite sensor layer, said nanocomposite sensor layer having electrically conductive nanoparticles which are integrated into a polymer matrix. The nanoparticles have at least one dimension which is shorter than 130 nm, and an electrode structure is in contact with the polymer nanocomposite sensor layer. Electric signals which are generated by the sensor in response to applied stimuli can be measured by means of the electrode structure.

Description

[0001]Measurement method for sensors based on polymer nanocomposites and sensor based on polymer nanocomposites. The invention relates to a measurement method for sensors based on polymer nanocomposites and to a sensor based on polymer nanocomposites according to the preamble of the first and eleventh patent claims. In the field of electrochemistry, the evaluation and performance analysis of various systems are based on the use of an equivalent impedance model. This model is typically achieved through the application of electrochemical impedance spectroscopy (EIS), which is widely used in the investigation of sensors for medical applications, gas sensors, and electrochemical systems such as batteries, fuel cells, corrosion processes, and electrode / electrolyte interfaces. Conventionally, EIS is measured over a wide frequency range, typically ranging from a few millihertz to a few megahertz.Based on the resulting Nyquist plot, an equivalent impedance model is developed to represent the system under investigation. Curve fitting is then used to compare the data obtained from the model with the measured data. This allows the model to assess how well it matches the experimental results, allowing the system's behavior and properties to be evaluated. In certain cases, individual model parameters are also analyzed to demonstrate their influence on the measured parameters. This contributes to a deeper understanding of the system response and the role of each parameter. In the field of electrical impedance spectroscopy, which extends beyond the field of electrochemistry, the focus is on analyzing the electrical properties of various systems, including, in particular, resistive, capacitive, and inductive components.This technique provides insights into the electrical behavior, material properties, and response of the system under investigation. Impedance spectroscopy is sometimes used for the electrical characterization of sensors based on polymer nanocomposites, 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, ranging from a few hertz to several megahertz. The literature reports the use of impedance spectroscopy in two ways: as a characterization tool for validating measured data against simulated data using an equivalent circuit model, or as a measurement technique in which the entire impedance spectrum represents the excitation of the sensor. The classic approach of impedance spectroscopy, which involves scanning a wide frequency range, is time-consuming.The measurement process can take several seconds to minutes, making it impractical for real-time monitoring of sensor response. This limitation is due to the frequency sampling required for impedance spectroscopy, which requires sampling over a wide frequency range. This technique performs impedance measurements at numerous points, requiring significant time and resources. The wide frequency range contributes to the overall measurement time. This limitation makes it difficult to capture dynamic changes in sensor behavior. Furthermore, the complexity of implementing impedance spectroscopy in an embedded system is a major drawback. The hardware and software requirements for accurately measuring impedance at multiple frequencies can be extensive.This complexity of development, integration, and maintenance increases the costs and technical requirements associated with integrating such a system into practical applications. Another disadvantage is the limitation to single-frequency measurements. Measuring exclusively at a specific frequency can compromise the sensitivity and selectivity of the sensor. Different sensor parameters can have different frequency responses, and important information can be lost when analyzing sensor performance at a single frequency. A single-frequency measurement cannot fully capture the sensor's behavior. This can lead to inaccurate characterization and suboptimal performance, particularly incomplete characterization and compromised sensor sensitivity and selectivity.Furthermore, the lack of real-time capability hampers application in dynamic environments where immediate and continuous monitoring of sensor response is required. The inability to capture dynamic changes and fluctuations in sensor behavior limits effectiveness in certain applications. Document EP 3242128 A1 describes a method for monitoring a composite material, wherein the composite material consists of an epoxy resin filled with electrically conductive nanoparticles, wherein at least one electrical property, such as the impedance of the composite material, is influenced by mechanical deformation. The composite material is integrated into an electrical circuit that emits an electrical signal whose value depends on the electrical property of the composite material, such that an alert is issued when a certain threshold is exceeded.The measured property of the sensor is, in particular, electrical impedance. A disadvantage is the multiple measurements in a range from 1 mV to 220 V. This requires numerous measurements and therefore a limited possibility of real-time monitoring. Document DE 10018745 A1 discloses a method and device for the rapid measurement of complex electrical resistances or impedance spectra. The method and device are used to record the electrical properties of lipid membranes, enabling the measurement and characterization of non-stationary systems with high temporal resolution. This method is a conventional impedance measurement method for use in the characterization of lipid-protein membranes in the laboratory and for the detection of adsorption processes. The method is not suitable as a measurement method for field sensors outside the laboratory.EP 2902774 B1 describes the continuous or near-continuous monitoring and evaluation of properties, particularly carbonate hardness, of liquids and non-solid materials. This application differs from the present invention. The measurement method proposes using a specific impedance equivalent circuit containing a CPE element in series with a parallel circuit of a resistor and a capacitor. Furthermore, the parameters for measuring the properties of water solutions are defined in this patent. The parameters for the equivalent circuits are selected not according to their sensitivity, but according to their physical significance.The object of the invention is to develop a measurement method for sensors based on polymer nanocomposites and a sensor based on polymer nanocomposites that provides a simple design and a reliable, fast measurement method for time-saving and real-time capability. Furthermore, a suitable sensor for implementing the method is to be provided that can be operated not only under laboratory conditions. This object is achieved by the features of the first and tenth patent claims. Advantageous embodiments are set out in the subclaims.The invention relates to a measuring method for sensors based on polymer nanocomposites, wherein the method comprises a measuring device connected to the sensor, an analysis module, a parameter identification module, and a monitoring module. 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 using the measuring device. However, more frequencies within the frequency range are also possible. In a second method step, the measured impedance values ​​are analyzed to determine an impedance model of the sensor using the analysis module.Subsequently, in a third method step, the parameter identification module is used to identify the optimal measurement parameter based on the impedance model, whereby the optimal measurement parameter has the highest sensitivity and selectivity for excitation of the sensor. In the fourth step, the monitoring module uses an optimal measurement parameter for real-time monitoring of the sensor response at one or more of the selected frequencies. Preferably, the impedance measurement comprises the following steps: a. a time / frequency varying current or voltage signal that is treated using the Discrete Fourier Transform (DFT) to derive frequency-dependent components, b. processing the frequency-dependent components to calculate the frequency-dependent impedance spectrum Z(f), c. analysis of the impedance spectrum Z(f) using a signal processing unit to obtain the measurement parameters of the sensor.The acquisition and analysis of the sensor output is carried out in such a way that the sensor under test is first subjected to a time / frequency varying current or voltage signal without any external excitation signals, and the corresponding voltage or current pulse is measured. These signals are then separated using signal analysis techniques such as the discrete Fourier transform (DFT) to extract the corresponding frequency-dependent voltage U(f) and current I(f), which form the basis for calculating Z(f), or to directly extract the frequency-dependent gain and phase. The typically used frequency range is from 1 Hz to 100 MHz. It can be extended depending on the sensor effect and sensor dimension. This analysis provides insights into the complex electrical behavior of the polymer nanocomposite sensors. The obtained impedance spectrum is then processed using a signal processing unit. This could advantageously be, for example,based on an equivalent circuit model (ECM), a neural network (NN), a distributed relaxation time (DRT) calculation, a differential impedance analysis (DIA) calculation, or a combination of these and other signal processing methods of impedance spectroscopy, e.g., digital filters. Each of these methods can provide various key indicators, such as various electrical parameters from ECM, various features and machine learning models from NN, distribution of time constants from DRT, and a local equivalent circuit model from DIA. These key indicators are then used to track and measure the desired measurement parameters of the sensor. In an advantageous embodiment of the method, the three or more selected frequencies are evenly distributed within the frequency range. The impedance model preferably includes a series resistance (R). s ), a parallel resistor (R p) and a parallel capacity (C p ). In one embodiment of the method, the impedance model can include an element with constant phase (a) as a replacement for the parallel capacitance (C p) in the case of a depressed semicircular Nyquist plot. The optimal measurement parameter is preferably determined by evaluating the sensitivity and selectivity of each parameter in the impedance model. In an advantageous embodiment of the method, the real-time monitoring of the sensor response at the selected frequencies is carried out using an embedded circuit. The admittance and / or the permittivity and / or the dielectric constant and / or the capacitance of the sensor are preferably measured on the basis of polymer nanocomposites in the predetermined frequency range. The sensor according to the invention based on polymer nanocomposites has a polymer nanocomposite sensor layer, wherein the nanocomposite sensor layer comprises electrically conductive nanoparticles embedded in a polymer matrix, wherein the nanoparticles are smaller than 130 nm in at least one dimension.An electrode structure is in contact with the polymer nanocomposite sensor layer, whereby electrical signals generated by the sensor in response to applied stimuli are measurable by means of the electrode structure. Particularly preferably, the nanoparticles have a diameter of less than 100 nm in at least one dimension. These nanoparticles are responsible for providing the desired electrical conductivity. They can be metallic, carbon-based, or a combination of both. The polymer matrix of the polymer nanocomposite sensor layer belongs to one or more of the following polymer groups, in particular thermosetting, thermoplastic, cross-linked, elastomeric, biodegradable, and / or conductive polymers. The selection of the polymer matrix depends on the specific requirements and the desired performance of the sensor.In one embodiment, the electrode structure is designed as a parallel plate electrode structure, in which the sensor layer is arranged between two electrode plates. Alternatively, the electrode structure can be designed as an interdigital electrode structure, in which the sensor layer is attached or deposited on the electrode to establish electrical contact. The two main types of electrode structures mentioned above are commonly used in sensor designs. The first type, the parallel plate electrode structure, is designed such that the sensor layer is arranged between two electrode plates. This configuration ensures that the electric field is evenly distributed across the sensor layer. The second type is the interdigitated electrode structure, in which the electrodes are arranged in an interdigitated pattern.In this configuration, the sensor layer is applied or deposited onto the electrodes to establish electrical contact. Various techniques are used to fabricate these electrode structures, depending on the desired substrate and the sensor's requirements. The sensor's nanocomposite material can be synthesized using techniques such as solution blending, melt blending, in-situ polymerization, electrospinning, layer-by-layer deposition, and inclusion polymerization. In an advantageous embodiment, the nanocomposite sensor is manufactured using techniques such as spin coating, dip coating, spray coating, layer-by-layer deposition, filament winding, drop casting, mold casting, electrospinning, laser reduction, hold pressing, 3D printing, screen printing, and inkjet printing.The electrodes of the electrode structure are preferably fabricated using techniques such as physical vapor deposition, chemical vapor deposition, screen printing, photolithography, inkjet printing, electroplating, or laser ablation. The choice of coating technique depends on factors such as the desired sensor design, substrate compatibility, and manufacturing requirements. The proposed invention offers several advantages over the prior art and, in particular, the classical approach to impedance measurement in sensors based on polymer nanocomposites. First, the classical approach, which involves performing impedance spectroscopy over a broad frequency range, is time-consuming. Data acquisition typically takes several seconds to minutes and is therefore impractical for real-time monitoring of sensor responses.In contrast, the method according to the invention uses a minimum of three selected frequencies. This leads to faster measurement times without compromising accuracy. Furthermore, the conventional approach requires 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 circuits without compromising sensor performance. Furthermore, the applied multi-frequency measurement offers additional advantages. By measuring impedance at multiple frequencies simultaneously, it is possible to determine the optimal measurement parameter that exhibits the highest sensitivity and selectivity to the sensor excitations.This measurement parameter is crucial for the precise characterization and monitoring of the sensor response. The sensor according to the invention is particularly suitable for implementing the method according to the invention. The sensor is used in particular for measuring force, temperature, strain, and humidity. The invention is explained in more detail below using an exemplary embodiment and the associated drawings. Figure 1 shows a polymer nanocomposite sensor layer 1 between a parallel plate electrode structure 2. Figure 2 shows an alternative embodiment of the sensor in the form of a polymer nanocomposite sensor layer 1 in combination with an interlocking electrode structure 3. Figure 3 shows the measurement sequence that provides the various sensor parameters. For this purpose, a calculation is performed in a computing unit 4 and forwarded to a signal processing unit 5.The key indicators 6 are determined by means of the signal processing unit 5. Figure 4 shows a plot showing the correlation between the various impedance components and the excitation signal. Some or all of these parameters are fed to a signal processing unit 5 according to Figure 5, which supplies the measurement parameters 7. Figure 6 shows a typical Nyquist diagram of the sensor based on polymer nanocomposites, with the curves shown for a variant with parallel capacitance and a variant with a constant phase element. A typical equivalent circuit for the variant with parallel capacitance and a constant phase element is shown in Figure 7. Figure 8 shows a representation of a typical real course of the impedance curve, plotted against the logarithm of the frequency, with at least three frequencies chosen that are equidistant in different frequency decades.Figure 9 shows a diagram illustrating the correlation between the various parameters of the equivalent circuit and the measured quantity. Figures 6 to 9 illustrate an example of using ECM as a signal processing unit. The frequency-dependent impedance of the sensor is determined, and a Nyquist diagram is created (Figure 6) that displays the complex impedance of the sensor. The Nyquist diagram shows three parameters of interest: series resistance (R). s ), parallel resistance (R p ), parallel capacity (C p ). In certain cases, the Nyquist plot may have a depressed semicircular shape, which indicates the presence of a constant-phase element (CPE) instead of C pBy carefully analyzing the Nyquist plot and extracting the relevant parameters, a comprehensive ECM can be created (Figure 7) to represent the electrical response of the polymer nanocomposite-based sensor. 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 of the selected frequencies. This procedure significantly improves both the sensitivity and selectivity of the sensor for a specific measurement parameter. This amplification enables a more precise and accurate measurement of the sensor's response to the desired excitation signals.Consequently, the overall performance of the sensor and its ability to detect and distinguish specific excitation signals in real time are significantly improved. Furthermore, the measurement method can be performed with various impedance-relevant quantities. These include complex admittance (G* = 1 / Z*), dielectric modulus (M* = jωZ*), and capacitance (K* = 1 / M*). To increase the effectiveness of the method, two or more frequencies can be selected. By incorporating multiple frequencies, a more robust and accurate model can be achieved, leading to improved accuracy, sensitivity, and selectivity of the sensor. In addition to sensitivity and selectivity, the method enables the analysis and monitoring of various other sensor properties. These properties include linearity, aging characteristics, homogeneity, and much more.By applying the same method, a comprehensive understanding of the sensor's performance and behavior can be achieved, enabling a thorough evaluation and optimization of its overall functionality. The proposed invention provides an optimized measurement method for sensors based on polymer nanocomposites. This method focuses on simultaneous impedance measurements at multiple frequencies within a defined frequency range, with the frequencies tuned to the sensor's excitation. According to the method, a comprehensive set of impedance values ​​is measured that represent various properties of the polymer nanocomposite. By analyzing the measured impedances, various parameters within an impedance model are derived that accurately represent the behavior of the polymer nanocomposite sensor. Each parameter is associated with a specific feature or property of the sensor.By considering the relationship between the parameters and the desired sensor performance, the parameter with the greatest influence on achieving the desired result can be identified. This determined parameter, the so-called optimal measurement parameter, is then used as a key factor for sensor operation and performance optimization. By monitoring the optimal sensor parameter, the disadvantages of conventional measurement methods are greatly minimized and the overall performance of sensors based on polymer nanocomposites is maximized. An example impedance spectroscopic analysis of the polymer nanocomposite-based sensor is described below. The example sensor is a sensor based on a polymer nanocomposite material in combination with a contact electrode structure.In this example, the sensor functions as a force sensor, whose electrical properties change when an external force is applied to the sensor. The sensor is connected to an impedance measurement device to examine its electrical properties. Examples of these 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 measurements with integrated microcontrollers or microprocessors. The device is configured to measure the sensor's impedance over a frequency range of 1 Hz to 100 MHz. Both the real (resistance) and imaginary (reactance) components of impedance are acquired. These measurements correspond to the sensor's response to various weights applied to the sensor.Figures 10 and 11 show the Bode plots of the real and imaginary components of impedance as a function of frequency from 100 Hz to 1 MHz for various applied weights. The data is subsequently plotted as a Nyquist curve, as shown in Figure 12, which plots the real part of the impedance against the imaginary part of the impedance. This plot is particularly useful for visualizing the complex impedance behavior of the sensor. By analyzing the Nyquist curve, characteristic semicircular patterns and other shapes that reflect the electrical properties of the sensor can be identified. Based on the Nyquist curve and the Bode plots, three or more different frequencies can be selected. In this example, the first frequency (500 Hz) is chosen between 100 Hz and 1 kHz, the second frequency (5 kHz) between 1 kHz and 10 kHz and the third frequency (50 kHz) between 10 kHz and 100 kHz.An exemplary measurement device based on an embedded system (embedded solution) is presented below. Taking the selected frequencies into account, a portable solution for measuring using the sensor is developed. The portable solution can be based on c-DAQ, FPGA, or a microcontroller. A microcontroller-based solution is cost-effective, compact, and power-efficient compared to other solutions. The various functional modules of the embedded system are shown in Figure 13 and include a signal processing unit, an offset removal module, an optional multiplexer or matrix switching module, voltage-controlled current sources (VCCS), the device under test, the measurement system, the preamplifier, the signal conditioning, and a microcontroller unit containing the analog-to-digital converter (ADC), the digital signal processor (DSP), and the impedance calculator.The signal processing unit synthesizes the excitation signal at the selected frequency, which is realized by the integrated pulse-width modulation (PWM) or digital-to-analog converter (DAC), or by external chips such as direct digital synthesis (DDS) or arbitrary waveform generators (AWG). Since most signal generation units can only provide positive voltages, an offset voltage (DC bias) is always present. To implement the impedance without DC bias, the offset voltage must be removed from the DDS, DAC, and PWM components, which can be achieved using a subtractor or high-pass filter. The optional multiplexer / switch matrix module is required when using more than one DUT or a DUT as an array or matrix. The VCCS is essential for maintaining a constant current in a circuit by regulating the current to match an input voltage, regardless of the sensor's impedance.Several VCCS architectures can be used, including load-in-the-loop, Howland circuits and derivatives, Tietze circuits, current conveyor (CCII), and operational transconductance amplifiers (OTAs). Howland circuits are particularly suitable for high-frequency measurements. This excitation signal is transmitted to the sensor, which is connected to a measurement system. The measurement system is based on the IU method, bridge mode, resonant method, or self-aligning bridge. - IV method: Relies on the simultaneous measurement of voltage and current, which are subjected to an AC analysis to extract the amplitude and phases of the current and voltage signals and thus the impedance. - Bridge system: Based on the balance of two impedance arms, one containing the reference impedance and the other the device under test. In balance, the reference impedance and the sensor have the same voltage, so no current flows between the arms.- Resonance method: In this method, a sinusoidal signal is injected 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. - Auto-balancing bridge: Uses a reference signal that is automatically phase-shifted to emulate the impedance response. If the signal is symmetrical, it emulates the reference DUT, and the system is matched. Both the IU method and the auto-balancing bridge have very good measurement accuracy and can measure frequencies up to 1 MHz. In a measurement system based on the IU method, the excitation signal generator injects a voltage (potentiostatic mode) or a current (galvanostatic mode) into the sensor. A pre-amplification module is used when signal amplification is required for better detection.The signal conditioning module typically consists of active filters, differential operational amplifiers, instrumentation amplifiers, and amplifiers. Signal conditioning ensures that the microcontroller can read and interpret the signal by reducing noise and amplifying the signal to match the microcontroller's voltage levels (e.g., 0 to 3.3 V). For accurate impedance measurement, it is important to measure both the system response and the realized excitation signal. Synchronization of the timers responsible for the excitation and the voltage and current measurements is crucial. Starting with the current and voltage signals in the time domain, an AC analysis is performed to determine the amplitude relationship and phase shift between the voltage and current signals. This can be done using analog circuits or digital signal processing.In analog circuits such as I / Q demodulation or the gain-phase detector (GPD), analog multiplication circuits demodulate the amplitude and phase of the response signal, followed by a low-pass filter. The real and imaginary values ​​are output as DC voltages using the I / Q demodulator, while the gain and phase are output as DC voltages using the GPD. In digital signal processing, the voltage and current signals are conditioned and then connected directly to an ADC. The microcontroller extracts the magnitude and phase after digital AC analysis. The extracted amplitudes and phases of the voltage and current signals are then analyzed using DFT (Discrete Fourier Transform) solutions, with methods such as the Fast Fourier Transform (FFT) and the Goertzel filter used to accelerate the calculation of the DFT coefficients.Other methods such as discrete-time Fourier transform (DTFT), ordinary linear least squares (OLS), and nonlinear least squares (NLLS) can also be used. Microcontrollers, e.g., those based on ARM technology such as the STM32, utilize dedicated libraries (e.g., CMSIS) for efficient signal processing, supporting operations such as FFT for signal lengths up to 4096, improving computational performance and memory management in impedance analysis applications. Impedance is determined after calculating the real and imaginary parts of the voltage and current signals using AC analysis techniques at excited frequencies. For an excited frequency index (f), this is done by a complex division of the voltage U(f) by the current I(f), as follows: The equivalent circuit model corresponding to the sensor can be used to decompose the measured impedance of the sensor and calculate the various components of the impedance. The microcontroller-based solution can also be connected to ICs specifically designed for impedance measurement, providing a compact and energy-efficient solution. Examples of these impedance measurement ICs are the AFE4300, MAX32600, AD5933, and AduCM350. The sensor is then excited at the three selected frequencies, and the impedance change is determined for the various applied weights. The obtained information is then input into the equivalent circuit model to calculate the various components of the impedance. Understanding the equivalent circuit model is essential for predicting sensor behavior under different conditions and for optimizing the design for improved sensitivity.In this example, the Nyquist diagram in Figure 14 shows that it is not characterized 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 (R). s ), parallel resistance (R p ) and parallel capacitance or a constant phase element (CPE) as shown in Figure 13 and is expressed as: where, R s is the resistance between the contact electrode and the sensor material and the intrinsic resistance of the conductive nanoparticles in the sensor material, R p is the tunneling resistance between the nanoparticles in the polymer matrix, CPE is the frequency-dependent impedance caused by inhomogeneities or distributed time constants. The CPE, which is represented as: ^^ ^^ ^^ = ^^( ^^ ^^) ఈwhere Q is a constant, ω is the angular frequency, α is a parameter ranging from 0 to 1, and for α = 1, CPE behaves like an ideal capacitor. Figure 15 shows the identification of the measurement parameters, where R s -value is significantly smaller than that of the other components. A detailed representation of the R s - value shows that R s is not strongly affected by changes in the applied weight, suggesting that a high-frequency component is not suitable for this sensor. However, both R pBoth CPE and CPE significantly vary with the applied weight at frequencies between 100 Hz and 1 MHz. One or more frequencies at different intervals within the frequency range can be chosen to understand the influence of frequencies on the various electrical parameters. For example, three frequencies are considered: 1. First frequency (500 Hz): Selected between 100 Hz and 1 kHz. At this frequency, it can be seen from Figure 16 and Figure 17 that the real part of the impedance is relatively more sensitive to the applied weights than the imaginary part. 2. Second frequency (5 kHz): Selected between 1 kHz and 10 kHz. At this frequency, it can be seen from Figure 18 and Figure 19 that the real part of the impedance is less affected by changes in the applied weights; however, the imaginary part shows good sensitivity. 3. Third frequency (50 kHz): Selected between 10 kHz and 100 kHz.At this frequency, it can be seen from Figures 20 and 21 that the real part remains virtually unaffected by changes in the applied weight, while the imaginary part exhibits a relatively linear sensitivity to the applied weight. Comparing the sensitivity to the parameters within 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., the CPE, with negligible influence of the real part, i.e., R. s and R p. Thus, the CPE is the optimal measurement parameter for this sensor. Regarding real-time monitoring of the sensor, the measuring device is programmed to measure the optimal measurement parameter of the sensor, specifically the CPE, at a frequency of 50 kHz. To increase stability, additional frequencies close to this selected frequency can be used to measure the sensor. Averaging the CPE of the impedance over these frequencies enables stable real-time monitoring of the sensor response. List of reference symbols 1 Polymer nanocomposite sensor layer 2 Plate electrode structure 3 Electrode structure 4 Computing unit 5 Signal processing unit 6 Key indicators 7 Measurement parameters

Claims

AMENDED CLAIMS received by the International Bureau on 3 December 2024 (03.12.2024) 1. A measuring method for sensors based on polymer nanocomposites, the method comprising a measuring device connected to the sensor, an analysis module, a parameter identification module, and a monitoring module, characterized in that force, temperature, strain, and humidity are measured by means of the measuring method, wherein a. in a first method step, a selection of at least three different frequencies within a predetermined frequency range is made, and subsequently the impedance of the sensor excited at the selected frequencies is measured in the selected frequency range by means of the measuring device, and subsequently a Nyquist diagram is created to display the complex impedance of the sensor, and that b. in a second method step, an analysis of the impedance values ​​measured in the form of series resistance (R s), parallel resistance (R p ) and parallel capacity (C p ) or a constant-phase element (CPE) instead of the parallel capacitance (C p ) and that c. in a third process step, the parameter identification module identifies the optimal measurement parameter in the form of Series resistance (R s ), parallel resistance (R p ) and parallel capacity (C p ) or a constant-phase element (CPE) instead of the parallel capacitance (C p ) is carried out on the basis of the impedance model, whereby the optimal measurement parameter has the highest sensitivity and selectivity for the excitation of the sensor and that d. the optimal measurement parameter is used by a monitoring module for real-time monitoring of the sensor response at one or more of the selected frequencies.

2. Method according to claim 1, characterized in that the impedance measurement comprises the following steps: a. a time / frequency varying current or voltage signal that is treated using the Discrete Fourier Transform (DFT) to derive frequency-dependent components, b. processing of the frequency-dependent components to calculate the frequency-dependent impedance spectrum Z(f), c. analysis of the impedance spectrum Z(f) using a signal processing unit to obtain the sensor measurement parameters for the respective measurement of force, temperature, strain and humidity.

3. The method according to claim 2, wherein the signal processing unit comprises an equivalent circuit model for extracting various electrical parameters and / or a neural network for extracting various features and applying machine learning models or a distributed relaxation time analysis for determining the distribution of the time constants or a differential impedance analysis for deriving a local equivalent circuit model.

4. Method according to claim 1, characterized in that the three or more selected frequencies are evenly distributed within the frequency range.

5. Method according to one of the preceding claims, characterized in that the impedance model comprises an element with a constant phase (a) as a replacement for the parallel capacitance (C P ) in the case of a depressed semicircular Nyquist plot.

6. Method according to one of the preceding claims, characterized in that the optimal measurement parameter is determined by evaluating the sensitivity and selectivity of each parameter in the impedance model.

7. Method according to one of the preceding claims, characterized in that the real-time monitoring of the sensor response in the selected frequencies is carried out using an embedded circuit.

8. Method according to one of the preceding claims, characterized in that the admittance and / or the permittivity and / or the dielectric constant and / or the capacitance of the sensor based on polymer nanocomposites is measured in the predetermined frequency range.

9. Sensor based on polymer nanocomposites for carrying out a measuring method according to claim 1 with a polymer nanocomposite sensor layer, characterized in that 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 that an electrode structure is in contact with the polymer nanocomposite sensor layer, wherein electrical signals generated by the sensor in response to applied stimuli can be measured by means of the electrode structure and that force, temperature, strain and humidity can be measured with the sensor.

10. Sensor according to claim 10, characterized in that the polymer matrix of the polymer nanocomposite sensor layer belongs to one or more of the following polymer groups: thermosetting, thermoplastic, cross-linked, elastomeric, biodegradable and conductive polymers.

11. Sensor according to claim 10 or 11, characterized in that the electrode structure is in the form of a parallel plate electrode structure in which the sensor layer is arranged between two electrode plates.

12. Sensor according to claim 10 or 11, characterized in that the electrode structure is in the form of an interdigital electrode structure in which the sensor layer is applied or deposited on the electrode in order to establish an electrical contact.

13. Sensor according to 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 deposition and inclusion polymerization.

14. Sensor according to one of the preceding claims, characterized in that the nanocomposite sensor is manufactured using techniques such as spin coating, dip coating, spray coating, layer-by-layer deposition, filament winding, drop casting, mold casting, electrospinning, laser reduction, hold pressing, 3D printing, screen printing and inkjet printing.

15. Sensor according to 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.

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