Measurement methods for sensors based on polymer nanocomposites and sensors based on polymer nanocomposites

Abstract

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, wherein a. in a first process 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 using the measuring device and b. in a second process step, an analysis of the measured impedance values ​​is carried out to determine an impedance model of the sensor using the analysis module and c. in a third process step, the parameter identification module is used to identify the optimal detection parameter based on the impedance model, wherein the optimal detection parameter has the highest sensitivity and selectivity for the excitation of the sensor and d. an optimal sampling parameter for real-time monitoring of the sensor response at one or more of the selected frequencies is used by means of the monitoring module. Furthermore, the invention relates to a sensor based on polymer nanocomposites with a polymer nanocomposite sensor layer, wherein the nanocomposite sensor layer comprises electrically conductive nanoparticles embedded in a polymer matrix, wherein the nanoparticles have a diameter of less than 130 nm in at least one dimension, and wherein 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.

Description

[0001] The invention relates to a measuring method for sensors based on polymer nanocomposites and a sensor based on polymer nanocomposites according to the preamble of the first and eleventh patent claims.

[0002] In the field of electrochemistry, the evaluation and performance analysis of various systems is 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.

[0003] Conventionally, EIS is measured over a wide frequency range, typically 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 agrees with 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 value.

[0004] This contributes to a deeper understanding of the system response and the role of individual parameters. 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, particularly resistive, capacitive, and inductive components. This technique provides insights into the electrical behavior, material properties, and response of the system under study.

[0005] 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 on 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 sensor's excitation.

[0006] The traditional approach to impedance spectroscopy, which involves sweeping 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 is due to the frequency sweeping nature of impedance spectroscopy, which requires scanning over a wide frequency range. This method performs impedance measurements at numerous points, requiring a significant investment of time and resources. The wide frequency range contributes to the overall measurement time. This limitation makes it difficult to capture dynamic changes in the sensor's behavior.

[0007] Furthermore, the complexity of implementing impedance spectroscopy in an embedded system is a major disadvantage. The hardware and software requirements for accurate impedance measurement 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.

[0008] Another disadvantage is the limitation to single-frequency measurements. Measuring exclusively at a specific frequency can compromise the sensor's sensitivity and selectivity. 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.

[0009] 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.

[0010] From the document EP 3242128 A1 there is disclosed 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 a mechanical deformation. The composite material is integrated into an electrical circuit that emits an electrical signal whose value depends on the electrical properties of the composite material, so that an alert is issued when a certain threshold is exceeded. The sensor's measured property is primarily electrical impedance. The disadvantage is the multiple measurements in a range from 1 mV to 220 V. This requires a large number of measurements and therefore limits the possibility of real-time monitoring.

[0011] The object of the invention is to develop a measuring method for sensors based on polymer nanocomposites and a sensor based on polymer nanocomposites, which provides a simple structural design and a reliable, fast measuring method for time-saving and real-time capability.

[0012] This problem is solved by the features of the first and eleventh patent claims.

[0013] Advantageous embodiments arise from the subclaims.

[0014] 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 the impedance of the sensor excited at the selected frequencies is subsequently measured in the selected frequency range using the measuring device. However, more frequencies within the frequency range are also possible. In a second process step, the measured impedance values ​​are analyzed to determine an impedance model of the sensor using the analysis module. In a third step, the parameter identification module identifies the optimal sensing parameter based on the impedance model. The optimal sensing parameter exhibits the highest sensitivity and selectivity for sensor excitation. In the fourth step, the monitoring module uses an optimal sampling parameter for real-time monitoring of the sensor response at one or more of the selected frequencies.

[0015] The impedance measurement preferably includes the following steps: a time / frequency varying current or voltage signal 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 key parameters of the sensor.

[0016] The acquisition and analysis of the sensor output is performed by first applying a time- and frequency-varying current or voltage signal to the sensor under test without any external excitation signals, and then measuring the corresponding voltage or current pulse. 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 expanded depending on the sensor effect and sensor dimension. This analysis provides insights into the complex electrical behavior of the polymer nanocomposite sensors.

[0017] The obtained impedance spectrum is then processed using a signal processing unit. This could advantageously be based on, for example, 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 from impedance spectroscopy, such as 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 local equivalent circuit model from DIA. These key indicators are then used to track and measure the desired measurand of the sensor.

[0018] In an advantageous embodiment of the method, the three or more selected frequencies are evenly distributed within the frequency range.

[0019] The impedance model preferably includes a series resistance (Rs), a parallel resistance (Rp) and a parallel capacitance (Cp). In one embodiment of the method, the impedance model may comprise a constant phase element (a) as a replacement for the parallel capacitance (Cp) in the case of a depressed semicircular Nyquist plot.

[0020] The optimal detection parameter is preferably determined by evaluating the sensitivity and selectivity of each parameter in the impedance model.

[0021] In an advantageous embodiment of the method, the real-time monitoring of the sensor response in the selected frequencies is carried out using an embedded circuit.

[0022] 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.

[0023] The sensor according to the invention based on polymer nanocomposites comprises a polymer nanocomposite sensor layer, wherein the nanocomposite sensor layer comprises electrically conductive nanoparticles embedded in a polymer matrix, wherein the nanoparticles have a diameter of less than 130 nm in at least one dimension. 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. 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.

[0024] 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 desired performance of the sensor.

[0025] In one embodiment, the electrode structure is designed in the form of a parallel plate electrode structure in which the sensor layer is arranged between two electrode plates.

[0026] Alternatively, the electrode structure may be in the form of an interdigital electrode structure in which the sensor layer is attached or deposited on the electrode to establish electrical contact.

[0027] The aforementioned two main types of electrode structures are commonly used in sensor designs. The first type, the parallel plate electrode structure, is designed such that the sensing layer is sandwiched between two electrode plates. This configuration ensures that the electric field is evenly distributed across the sensing layer. The second type is the interdigitated electrode structure, in which the electrodes are arranged in an interdigitated pattern. In this configuration, the sensing layer is coated or deposited onto the electrodes to establish electrical contact. Different techniques are used to fabricate these electrode structures, depending on the desired substrate and the sensor requirements.

[0028] The nanocomposite material of the sensor can be synthesized using techniques such as solution mixing, melt mixing, in situ polymerization, electrospinning, layer-by-layer deposition, and inclusion polymerization.

[0029] 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.

[0030] 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.

[0031] The choice of coating technique depends on factors such as the desired sensor design, substrate compatibility and manufacturing requirements

[0032] 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, in which impedance spectroscopy is performed 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 inventive method uses a minimum of, in particular, three selected frequencies. This leads to faster measurement times without compromising accuracy.

[0033] Furthermore, the traditional 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 inventive solution shortens the measurement process and enables the use of less complex embedded circuits without compromising sensor performance.

[0034] Furthermore, the applied multi-frequency measurement offers additional advantages. By simultaneously measuring impedance at multiple frequencies, it is possible to determine the optimal measurement parameter that exhibits the highest sensitivity and selectivity to the sensor's excitations. This measurement parameter is crucial for the precise characterization and monitoring of the sensor response.

[0035] The invention is explained in more detail below using an embodiment and associated drawings.

[0036] Fig. 1 shows a polymer nanocomposite sensor layer 1 between a parallel plate electrode structure 2. The Fig. 2 shows an alternative embodiment of the sensor in the form of a polymer nanocomposite sensor layer 1 in combination with an interlocked electrode structure 3. In Fig. Figure 3 shows the measurement sequence that provides the various sensor parameters. This is done by a calculation unit 4 and then forwarded to a signal processing unit 5. The signal processing unit 5 determines the key indicators 6.

[0037] In Fig. Figure 4 shows a plot showing the correlation between the various parameters of the sensor response and the excitation signal. Some or all of these parameters are calculated according to Fig. 5 is fed to a signal processing unit 5, which supplies the measured variable 7.

[0038] In Fig. Figure 6 shows a typical Nyquist diagram of the polymer nanocomposite-based sensor, with the curves shown for a parallel capacitance variant and a constant phase element variant. A typical equivalent circuit for the parallel capacitance and constant phase element variant is shown in Fig. 7 shown. Fig. Figure 8 shows a representation of a typical real impedance curve plotted against the logarithm of frequency, where at least three frequencies were chosen that are equidistant in different frequency decades. Fig. Figure 9 shows a diagram showing the correlation between the different parameters of the equivalent circuit and the measured quantity.

[0039] In the Fig. 6 to 9, an example of using ECM as a signal processing unit is explained in more detail. The frequency-dependent impedance of the sensor is determined, and a Nyquist diagram is created ( Fig. 6) that displays 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 certain cases, the Nyquist plot may exhibit a depressed semicircular shape, indicating the presence of a constant-phase element (CPE) instead of Cp. By carefully analyzing the Nyquist plot and extracting the relevant parameters, a comprehensive ECM can be created ( Fig. 7) to illustrate the electrical response of the polymer nanocomposite-based sensor.

[0040] To ensure a comprehensive analysis of the polymer nanocomposite-based sensor, at least three frequencies are selected for the three parameters (as in Fig. 8). These parameters (see Fig. 9) are then used to measure the sensor's response at one or more of the selected frequencies. This process significantly improves both the sensitivity and selectivity of the sensor for a specific sensing parameter. This gain 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.

[0041] 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, resulting in improved accuracy, sensitivity, and selectivity of the sensor.

[0042] 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.

[0043] The proposed invention provides an optimized measurement method for sensors based on polymer nanocomposites, which focuses on simultaneous impedance measurements at multiple frequencies in a defined frequency range, with the frequencies being tuned to the excitation of the sensor.

[0044] According to the method, a comprehensive set of impedance values ​​is measured, representing various properties of the polymer nanocomposite.

[0045] By analyzing the measured impedances, various parameters are derived within an impedance model 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.

[0046] This determined parameter, the so-called optimal sensing 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. 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 Measured quantity QUOTES CONTAINED IN THE DESCRIPTION

[0000] This list of documents submitted by the applicant was generated automatically and is included solely for the convenience of the reader. This list is not part of the German patent or utility model application. The DPMA assumes no liability for any errors or omissions. Cited patent literature

[0000] EP 3242128 A1

[0010]

Claims

[1] 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 by , that 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 in the selected frequencies is measured in the selected frequency range by means of the measuring device and that b. in a second process step, an analysis of the measured impedance values ​​is carried out to determine an impedance model of the sensor by means of the analysis module and that c. in a third method step, the parameter identification module identifies the optimal detection parameter on the basis of the impedance model, wherein the optimal detection parameter has the highest sensitivity and selectivity for the excitation of the sensor and that d. an optimal sampling parameter is used by the 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 by that the impedance measurement includes the following steps: a time / frequency varying current or voltage signal 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 key parameters of the sensor. [3] The method of 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 time constants or a differential impedance analysis for deriving a local equivalent circuit model. [4] Method according to claim 1, characterized by that the three or more selected frequencies are evenly distributed within the frequency range. [5] Method according to one of the preceding claims, characterized by that the impedance model includes a series resistance (Rs), a parallel resistance (Rp) and a parallel capacitance (Cp). [6] Method according to one of the preceding claims, characterized by that the impedance model includes a constant phase element (a) as a replacement for the parallel capacitance (Cp) in the case of a depressed semicircular Nyquist plot. [7] Method according to one of the preceding claims, characterized by that the optimal detection parameter is determined by evaluating the sensitivity and selectivity of each parameter in the impedance model. [8] Method according to one of the preceding claims, characterized by that real-time monitoring of the sensor response at the selected frequencies is performed using an embedded circuit. [9] Method according to one of the preceding claims, characterized by 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. [10] Sensor based on polymer nanocomposites for carrying out a measuring method according to claim 1 with a polymer nanocomposite sensor layer, characterized by that the nanocomposite sensor layer comprises electrically conductive nanoparticles embedded in a polymer matrix, wherein the nanoparticles have a diameter of less 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 are measurable by means of the electrode structure. [11] Sensor according to claim 10, characterized by 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. [12] Sensor according to claim 10 or 11, characterized bythat the electrode structure is designed in the form of a parallel plate electrode structure in which the sensor layer is arranged between two electrode plates. [13] Sensor according to claim 10 or 11, characterized by 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 electrical contact. [14] Sensor according to one of the preceding claims, characterized by 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. [15] Sensor according to one of the preceding claims, characterized bythat 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. [16] Sensor according to one of the preceding claims, characterized by 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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