Flexible pressure-temperature dual sensor and preparation method thereof

Abstract

The invention discloses a flexible pressure-temperature dual sensor and a preparation method thereof, and the flexible pressure-temperature dual sensor comprises a flexible temperature sensor which is used for temperature sensing; the sponge-micro hemisphere comprises a PDMS / PU / CNT sponge and a PDMS / CNT micro hemisphere which are arranged in sequence, and is used for sensing pressure; an interdigital electrode; the interdigital electrode is bonded with one side of the PDMS / CNT micro-hemisphere of the sponge-micro-hemisphere, and one side of the sponge-micro-hemisphere far away from the PDMS / CNT micro-hemisphere is bonded with the flexible temperature sensor. The sensor provided by the invention not only can realize simultaneous monitoring of temperature and pressure, but also can solve the problem of thermal drift generally existing in a traditional sensor, and ensures that stable measurement results can be provided under different temperature conditions.

Description

Technical Field

[0001] The present invention relates to the technical field of flexible sensors, and particularly to a flexible pressure-temperature dual sensor and a preparation method thereof. Background Art

[0002] In the technical field of flexible sensors, especially for dual-mode sensors that can simultaneously monitor temperature and pressure, there are obvious limitations in current preparation methods and material selection. Existing sensors usually use single materials or simple composite materials, and these materials are difficult to meet the actual requirements in terms of sensitivity, response speed, stability, and environmental adaptability. At the same time, due to the lack of reasonable structural optimization design, the sensors may exhibit problems such as unstable performance or insufficient response in applications. For example, some flexible sensors have problems with the mismatch between material properties and functions in actual operation. Especially in the monitoring of temperature and pressure dual variables, defects in structural design may lead to limited accuracy and stability.

[0003] The preparation process of traditional flexible dual sensors is relatively complex and costly, making it difficult to achieve large-scale production, which limits their promotion in industrial and commercial fields. In addition, flexible sensors need to face various complex environments in actual applications, such as temperature changes, humidity effects, and mechanical stress, which pose higher requirements for material and structural design. In dual-mode sensors, especially flexible sensors using piezoelectric fillers, they are usually significantly affected by the temperature drift effect. The electrical properties of piezoelectric materials are extremely sensitive to temperature changes and are prone to drift in resistance or charge response due to environmental temperature fluctuations, resulting in a decrease in the measurement accuracy of the sensors. Currently, this problem has not been effectively solved, further limiting the practical application of flexible dual-mode sensors.

[0004] At the same time, existing flexible pressure sensors have limited performance in the monitoring range and are difficult to maintain high sensitivity and reliability over a wide range. Most single-structure designs are difficult to simultaneously achieve high resolution for small-range pressures and linear response for large-range pressures. This limitation restricts the application of sensors in scenarios that require wide-range and high-sensitivity pressure monitoring (such as medical monitoring or smart devices). In addition, the temperature drift effect may be superimposed with mechanical stress, further exacerbating the non-linearity problem of the sensor output. Therefore, in order to optimize the pressure monitoring range and temperature drift effect, comprehensive solutions are urgently needed in aspects such as material design, structural innovation, and signal compensation. Summary of the Invention

[0005] Based on the technical problems existing in the background art, the present invention proposes a flexible pressure-temperature dual sensor and a preparation method thereof. This sensor can not only simultaneously monitor temperature and pressure, but also solve the problem of thermal drift commonly existing in traditional sensors, ensuring stable measurement results under different temperature conditions.

[0006] A flexible pressure-temperature dual sensor proposed by the present invention includes:

[0007] A flexible temperature sensor for temperature sensing;

[0008] A sponge-microhemisphere, including a PDMS / PU / CNT sponge and a PDMS / CNT microhemisphere arranged in sequence;

[0009] Interdigitated electrodes;

[0010] The interdigitated electrodes are adhered to one side of the PDMS / CNT microhemisphere of the sponge-microhemisphere, and the side of the sponge-microhemisphere away from the PDMS / CNT microhemisphere is adhered to the flexible temperature sensor.

[0011] Preferably, the flexible temperature sensor includes a polyimide film, on which an NTC thermistor and electrodes are arranged, and the NTC thermistor and the electrodes are electrically connected.

[0012] Preferably, it further includes a thermal drift compensation module. Using the output pressure data of the flexible pressure-temperature dual sensor under different temperature and resistance conditions as the training set of the BPNN model, the BPNN model after training learns the non-linear relationship between the input data temperature and resistance and the output data pressure to achieve thermal drift compensation.

[0013] Preferably, the BPNN model includes:

[0014] An input layer for inputting temperature and resistance data;

[0015] A hidden layer, which undergoes linear transformation and non-linear activation in sequence, and uses the output of the previous hidden layer as the input of the next hidden layer;

[0016] An output layer for outputting pressure data.

[0017] Preferably, the formula for linear transformation is:

[0018]

[0019] In the formula, z j is the linear transformation output of the j-th neuron in the hidden layer; w ij is the weight connecting the i-th input to the j-th neuron; x i is the i-th feature in the input data; b j is the bias of the j-th neuron.

[0020] The formula for non-linear activation is:

[0021] a j =σ(z j )

[0022] where a j is the output of the j-th neuron; σ(·) is the activation function, which is PeLU, Sigmoid or Tanh;

[0023] The formula for the pressure output by the final hidden layer is:

[0024]

[0025] where P is the predicted pressure value; w j is the weight of the output of the j-th neuron; b is the bias of the hidden layer output; a j is the output of the j-th neuron; m is the number of neurons in the hidden layer.

[0026] Preferably, the hidden layer is three layers.

[0027] A preparation method of a flexible pressure-temperature dual sensor proposed by the present invention, the sensor is as described above, and the method steps are as follows:

[0028] S1: Mix carbon nanotubes and polydimethylsiloxane evenly and apply them evenly on the PU sponge;

[0029] S2: Inject the mixture of carbon nanotubes and polydimethylsiloxane into a mold with a number of evenly distributed concave points, and then put the PU sponge of S1 into it. After curing, a sponge-microhemisphere is obtained;

[0030] S3: Pour a polydimethylsiloxane buffer layer on the side wall of the sponge-microhemisphere, and then bond the temperature sensor and the interdigital electrode on both sides of the sponge-microhemisphere to obtain a flexible pressure-temperature dual sensor.

[0031] The mass ratio of carbon nanotubes to polydimethylsiloxane is 1-2:50.

[0032] The beneficial technical effects of the present invention:

[0033] By integrating the temperature sensor, the buffer layer, the pressure sensor and the bottom electrode together through an accurate packaging process, the present invention forms a stable 3D structure, which not only protects the sensitive part of the sensor, but also realizes the effective spatial separation of the temperature and pressure sensors, avoiding signal interference; the flexible dual-mode sensor of the present invention can not only realize the synchronous monitoring of temperature and pressure, but also has good mechanical stability and environmental adaptability, and is suitable for various application scenarios, including wearable electronic devices, smart home systems, medical diagnosis and industrial monitoring, etc.; the present invention also provides an effective solution for thermal drift compensation, effectively reducing the influence of thermal drift and improving the temperature stability and measurement accuracy of the sensor. The preparation method of this sensor provides new possibilities for the development of flexible sensor technology and broadens its application prospects in multiple fields. Brief Description of the Drawings

[0034] Figure 1 It is a schematic diagram of the flexible temperature sensor proposed by the present invention;

[0035] Figure 2 It is the working principle of the flexible temperature sensor proposed by the present invention;

[0036] Figure 3 It is the manufacturing process and physical image of the flexible pressure sensor proposed by the present invention;

[0037] Figure 4 It is the working principle of the sponge and microsphere structure pressure sensor proposed by the present invention;

[0038] Figure 5 It is the exploded view and physical image of the flexible pressure-temperature sensor proposed by the present invention;

[0039] Figure 6 It is the flow chart of the BPNN algorithm proposed by the present invention;

[0040] Figure 7 It is the neural network structure diagram proposed by the present invention;

[0041] Figure 8 It is the comparison between the true value and the predicted value proposed by the present invention;

[0042] Figure 9 It is the training loss and validation loss proposed by the present invention;

[0043] Figure 10 It is the normal distribution of the prediction error proposed by the present invention.

[0044] In the figure: 1 - NTC thermistor, 2 - polyimide film, 3 - electrode, 4 - conduction band, 5 - electron, 6 - hole, 7 - valence band, 8 - PDMS / CNT solution, 9 - PU sponge, 10 - PDMS / PU / CNT sponge, 11 - mold, 12 - sponge - micro - hemisphere, 13 - PDMS buffer layer, 14 - CNT, 15 - flexible temperature sensor, 16 - PDMS / CNT micro - hemisphere, 17 - interdigital electrode. Detailed Embodiments

[0045] The present invention will be further explained below with reference to specific embodiments.

[0046] Refer to Figure 3 and Figure 5 A preparation method of a flexible pressure - temperature dual sensor proposed by the present invention has the following method steps:

[0047] (1) Mix CNT and PDMS in a mass ratio of 1:25, and stir with an electric stirrer for 10 minutes to ensure uniform dispersion of CNT in PDMS, forming a PDMS / CNT solution.

[0048] (2) Cut the PU sponge into the required size, and evenly apply the mixed PDMS / CNT solution on the PU sponge to obtain a PDMS / PU / CNT sponge.

[0049] (3) Add the PDMS / CNT solution into a mold (11) with a number of evenly distributed concave points to form PDMS / CNT micro - hemispheres. Then place the PDMS / PU / CNT sponge into the mold, and pour PDMS solution on the outer side wall of the PDMS / PU / CNT sponge. After curing and demolding, a sponge - micro - hemisphere containing a buffer layer is obtained, where the buffer layer is obtained by curing the PDMS solution, and its thickness is the total thickness of the PDMS / CNT micro - hemisphere and the PDMS / PU / CNT sponge. The buffer layer is used to provide structural support and protection.

[0050] (4) Refer to Figure 1 , use a laser cutting machine to cut the polyimide (PI) film into a specific shape as the substrate of the sensor. Then deposit gold (Au) electrodes on the PI film by magnetron sputtering technology, and fix the NTC thermistor on the gold electrodes on the PI film by reflow soldering technology to complete the preparation of the temperature sensor. The working principle of this flexible temperature sensor is as Figure 2 shown.

[0051] (5) Bond the flexible temperature sensor and the interdigital electrode on both sides of the sponge - micro - hemisphere, where the interdigital electrode is bonded to one side of the PDMS / CNT micro - hemisphere of the sponge - micro - hemisphere, and the side of the sponge - micro - hemisphere away from the PDMS / CNT micro - hemisphere is bonded to the flexible temperature sensor.

[0052] Figure 4 This is the pressure - sensing principle of the sponge - micro - hemisphere structure of the flexible pressure - temperature dual - sensor of the present invention. Embed the pressure sensor with the sponge - micro - hemisphere structure in the buffer layer to ensure its separation from the temperature sensor and avoid signal interference.

[0053] In addition, the flexible pressure - temperature dual - sensor of the present invention further includes a thermal drift compensation module based on a back - propagation neural network (BPNN) for calibrating and compensating the change in the sensor output caused by temperature changes.

[0054] The thermal drift compensation module uses the output pressure data of the flexible pressure - temperature dual - sensor under different temperature and resistance conditions as the training set of the BPNN model. By training the BPNN model to learn the non - linear relationship between the input data of temperature and resistance and the output data of pressure, thermal drift compensation is achieved.

[0055] Referring to Figure 7 , the BPNN model includes:

[0056] An input layer for inputting temperature and resistance data;

[0057] A hidden layer that undergoes linear transformation and non - linear activation in sequence, and takes the output of the previous hidden layer as the input of the next hidden layer; preferably, the hidden layer has five layers;

[0058] An output layer for outputting pressure data.

[0059] The formula for linear transformation is:

[0060]

[0061] In the formula, z j is the linear transformation output of the j - th neuron in the hidden layer; w ij is the weight connecting the i - th input to the j - th neuron; x i is the i - th feature in the input data; b j is the bias of the j - th neuron.

[0062] The formula for non - linear activation is:

[0063] a j = σ(z j )

[0064] In the formula, a j is the output of the j - th neuron; σ(·) is the activation function, which is PeLU, Sigmoid or Tanh;

[0065] The formula for the pressure output by the final hidden layer is:

[0066]

[0067] In the formula, P is the predicted pressure value; w j is the weight of the output of the j - th neuron; b is the bias of the hidden layer output; a j is the output of the j - th neuron; m is the number of neurons in the hidden layer.

[0068] Referring to Figure 6 , the process of the BPNN algorithm is as follows:

[0069] First, collect the output data of the sensor under different temperature and pressure conditions as the training dataset of the BPNN.

[0070] Then, design the structure of the BPNN, including the input layer (temperature and pressure), the hidden layer (multi-layer perceptron structure), and the output layer (predicted pressure value). The neural network structure diagram is as shown in Figure 7 the figure.

[0071] By training the BPNN model, it learns the non-linear relationship between the input data and the output data, and the model is verified and tested to ensure its prediction accuracy. The comparison between the true value and the predicted value is as shown in Figure 8 the figure.

[0072] Finally, integrate the trained BPNN model into the sensor system to compensate for the influence of temperature change on the sensor output in real time, and improve the accuracy and reliability of the measurement results. During the training process, we focus on the training loss and validation loss of the model, as shown in Figure 9 the figure, and the normal distribution of the prediction error, as shown in Figure 10 the figure.

[0073] Although the embodiments of the present application have been shown and described, for those of ordinary skill in the art, it can be understood that various changes, modifications, substitutions, and variations can be made to these embodiments without departing from the principle and spirit of the present application. The scope of the present application is defined by the appended claims and their equivalents, and all should be included within the protection scope of the present application.

Claims

1. A flexible pressure-temperature dual sensor, characterized in that: include: A flexible temperature sensor (15) for temperature sensing; A sponge-micro-hemispherical (12), comprising a PDMS / PU / CNT sponge and a PDMS / CNT micro-hemispherical (16) arranged in sequence; Interdigitated electrodes (17); The interdigitated electrode (17) is bonded to one side of the PDMS / CNT micro-hemisphere (16) of the sponge-micro-hemisphere (12), and the side of the sponge-micro-hemisphere (12) away from the PDMS / CNT micro-hemisphere (16) is bonded to the flexible temperature sensor (15).

2. The flexible pressure-temperature dual sensor according to claim 1, characterized in that: The flexible temperature sensor (15) comprises a polyimide film (2), on which an NTC thermistor (1) and an electrode (3) are arranged, and the NTC thermistor (1) and the electrode (3) are electrically connected.

3. The flexible pressure-temperature dual sensor according to claim 1, characterized in that: It also includes a thermal drift compensation module, which uses the output pressure data of the flexible pressure-temperature dual sensor under different temperature and resistance conditions as the training set of the BPNN model. The trained BPNN model learns the nonlinear relationship between the input data temperature and resistance and the output data pressure to achieve thermal drift compensation.

4. The flexible pressure-temperature dual sensor according to claim 3, characterized in that: The BPNN model includes: Input layer, used for inputting temperature and resistance data; The hidden layer undergoes linear transformation and nonlinear activation in sequence, and uses the output of the previous hidden layer as the input of the next hidden layer; Output layer, used for outputting pressure data.

5. The flexible pressure-temperature dual sensor according to claim 4, characterized in that: The formula for linear transformation is: Where zj is the linear transformation output of the jth neuron in the hidden layer; wij is the weight connecting the i-th input to the j-th neuron; xi is the i-th feature in the input data; bj is the bias of the jth neuron.

6. The flexible pressure-temperature dual sensor according to claim 5, characterized in that: The formula for nonlinear activation is: aj=σ(zj) Where aj is the output of the jth neuron; σ(·) is the activation function, which can be PeLU, Sigmoid or Tanh; The formula for the pressure of the final hidden layer output is: Where P is the predicted pressure value; wj is the weight of the output of the jth neuron; b is the bias of the hidden layer output; aj is the output of the jth neuron; and m is the number of neurons in the hidden layer.

7. The flexible pressure-temperature dual sensor according to claim 4, characterized in that: The hidden layer is three layers.

8. A method for preparing a flexible pressure-temperature dual sensor, the sensor being as described in any one of claims 1 to 6, characterized in that: The steps are as follows: S1: Mix carbon nanotubes and polydimethylsilane evenly and apply them evenly on the PU sponge; S2: Inject a mixture of carbon nanotubes and polydimethylsilane into a mold with a number of evenly distributed concave points, then put the PU sponge of S1 in, and obtain the sponge-micro hemisphere after curing; S3: A polydimethylsilane buffer layer is poured on the side wall of the sponge-microhemisphere, and then the temperature sensor and the interdigital electrodes are bonded to both sides of the sponge-microhemisphere to obtain a flexible pressure-temperature dual sensor.

9. The method for preparing the flexible pressure-temperature dual sensor according to claim 8, characterized in that: The mass ratio of carbon nanotubes to polydimethylsilane is 1-2:50.

Images