Flexible microstructure capacitive pressure sensor, preparation method and application

Abstract

The invention provides a flexible microstructure capacitive pressure sensor, a preparation method and application, and belongs to the technical field of sensors, and the preparation method comprises the following steps: S1, preparing a microstructure electrode layer; s2, preparing a microstructure dielectric layer; and S3, assembling the flexible microstructure capacitive pressure sensor. The flexible micro-structure capacitive pressure sensor is assembled based on a sandwich structure, the upper layer and the lower layer of the flexible micro-structure capacitive pressure sensor are micro-structure electrode layers, the conductive surfaces of the two micro-structure electrode layers are oppositely arranged, and a micro-structure dielectric layer is inserted into a middle layer between the two micro-structure electrode layers. According to the flexible microstructure capacitive pressure sensor, the preparation method and the application, the problems that in the prior art, a traditional sensor is difficult to meet the requirement for complex shape recognition, and an existing flexible pressure sensor is high in preparation cost and complex in preparation process are solved.

Description

Technical Field

[0001] The present invention relates to the field of sensor technology, and in particular to a flexible microstructure capacitive pressure sensor, a preparation method and an application thereof. Background Art

[0002] In the context of the rapid development of robot intelligence and wearable device interaction technology, traditional tactile sensors have been unable to meet the needs of complex shape recognition due to their rigid structure, low resolution and single-dimensional perception capabilities. In order to overcome these limitations, flexible microstructure capacitive pressure sensors have emerged. Through the integration of micro-nanoscale structure design, multi-dimensional signal decoupling algorithms and interdisciplinary technologies, they provide "bionic touch" for robots and "shape perception" capabilities for wearable devices. However, despite the rapid development of the flexible pressure sensor market, its existing technology still has many shortcomings.

[0003] The performance of flexible pressure sensors has not yet reached the requirements of practical applications. Although they have multiple deformation capabilities such as bending, stretching and twisting, they still need to be improved in terms of sensitivity, response speed, cycle stability and detection limit. This limits the wide application of flexible pressure sensors in medical treatment, diagnosis, implantable detection equipment and human-computer interaction.

[0004] Secondly, the preparation cost of flexible pressure sensors is relatively high, and the preparation process is complicated. Traditional technologies such as photolithography, wet etching and chemical etching have problems such as cumbersome steps, environmental pollution and insufficient precision. Photolithography technology requires masks and chemical steps, which not only increases costs, but also easily pollutes the environment. In addition, the 3D printing mold technology required for PDMS molding also has the disadvantages of long cycle and high cost, which further limits the production efficiency and cost control of flexible pressure sensors.

[0005] In summary, the existing flexible pressure sensor technology still has many limitations in terms of performance, preparation cost and process. In order to overcome these challenges, it is necessary to develop new preparation technologies and materials to improve the performance of flexible pressure sensors, reduce preparation costs and simplify preparation processes, thereby promoting their wide application and development in various fields. Summary of the invention

[0006] The purpose of the present invention is to provide a flexible microstructure capacitive pressure sensor, a preparation method, and an application, which solves the problem that traditional sensors in the prior art are difficult to meet the needs of complex shape recognition, and the existing flexible pressure sensors have high preparation costs and complex preparation processes.

[0007] To achieve the above object, the present invention provides a method for preparing a flexible microstructure capacitive pressure sensor, comprising the following steps: S1, preparing a microstructure electrode layer; S2, preparing a microstructured dielectric layer; S3. Assemble the flexible microstructure capacitive pressure sensor.

[0008] Preferably, the preparation of the microstructure electrode layer in S1 comprises the following steps: S11, laser etching the glass substrate, using software to draw a pattern structure and set etching parameters, laser etching the glass substrate to obtain a glass substrate with a microstructure; S12, cleaning and drying the glass substrate, completely immersing the etched glass substrate in a culture dish filled with alcohol, cleaning it with an ultrasonic cleaning machine, and baking the cleaned glass substrate in a blast drying oven; S13, coating a silver nanowire AgNWs solution, uniformly coating the AgNWs solution on a dry glass substrate, heating to fix the silver nanowires on the glass substrate and removing excess AgNWs solution; S14, preparing a polydimethylsiloxane (PDMS) mixture, mixing the PDMS stock solution with the curing agent, stirring, and vacuuming to remove bubbles in the mixture; S15, making an AgNWs / PDMS electrode layer, covering the silver nanowire layer with a PDMS mixture, and heating the layer, and obtaining an AgNWs / PDMS electrode layer with a microstructure on the surface after peeling.

[0009] Preferably, the step of preparing the microstructured dielectric layer in S2 comprises the following steps: S21, cutting the prepared sandpaper according to size, washing it, drying it and setting it aside for later use; S22, preparing a polydimethylsiloxane / multi-walled carbon nanotube PDMS / MWCNTs mixed solution, adding MWCNTs powder to the PDMS stock solution, and stirring the mixed solution in an ultrasonic environment; S23, adding a curing agent to the PDMS / MWCNTs mixture, stirring with a glass rod, and placing in a vacuum box to stand to remove bubbles; S24, preparing PDMS / MWCNTs mixed solutions with different mass fractions; S25, curing and peeling, pouring PDMS / MWCNTs solutions with different mass fractions into the sandpaper template respectively, placing the sandpaper template containing the mixed solution in a vacuum box to stand and remove bubbles, and peeling after curing to obtain the PDMS / MWCNTs sandpaper dielectric layer.

[0010] Preferably, assembling the flexible microstructure capacitive pressure sensor in S3 includes the following steps: assembling the flexible microstructure capacitive pressure sensor based on a sandwich structure, the upper and lower layers of the flexible microstructure capacitive pressure sensor are both microstructure electrode layers, the conductive surfaces of the two microstructure electrode layers are arranged relative to each other, and a microstructure dielectric layer is inserted into the middle layer between the two microstructure electrode layers.

[0011] Preferably, the baking temperature of the glass substrate is 80-120° C., and the baking time is 5-10 minutes.

[0012] Preferably, the temperature of heating the glass substrate in S13 is 100-140° C. and the time is 10-20 minutes.

[0013] Preferably, the heating temperature in S15 is 80-120° C., and the heating time is 2-4 hours.

[0014] Preferably, in S25, the curing temperature is 50-90° C., and the curing time is 1-3 hours.

[0015] The present invention proposes a flexible microstructure capacitive pressure sensor obtained according to the above-mentioned method for preparing the flexible microstructure capacitive pressure sensor.

[0016] The present invention proposes an application of the above-mentioned flexible microstructure capacitive pressure sensor, comprising the following steps: Step 1: Data collection: attach the flexible microstructure capacitive pressure sensor to the manipulator, confirm the grasped objects of different shapes, obtain the physical quantities of the objects of different shapes and convert them into capacitive signals, and mark and classify the capacitive signals through the processor; Step 2: Data preprocessing: extracting the waveform characteristics of the capacitance signal, normalizing the data, and dividing the data into a training set and a test set; Step 3: Evaluate the machine learning model. Select a machine learning model that is suitable for the data, optimize the parameters using the training set, and then evaluate it using the test set to select the optimal model for subsequent recognition functions.

[0017] Therefore, the present invention adopts the above-mentioned flexible microstructure capacitive pressure sensor, preparation method, and application, and the technical effects are as follows: 1. Three-layer microstructure design, including AgNWs / PDMS upper and lower electrode layers with laser-etched micro-convex structures and PDMS / MWCNTs dielectric layers with sandpaper microstructures, significantly improves the sensitivity of the sensor; 2. The preparation process is efficient and flexible. The mold is prepared by laser etching glass, which can accurately control the size and depth of the microstructure. The dielectric layer uses a sandpaper mold, which is easy to obtain and low in cost. The microstructure of the dielectric layer can be adjusted by changing the sandpaper mesh, achieving rapid prototyping and high-precision preparation; 3. Good stretchability. Using flexible PDMS as the substrate, the sensor exhibits excellent stretchability and flexibility. It can be applied to wearable devices for the human body to monitor human physiological signals in real time. 4. A single sensor can sense multiple points. Compared with the traditional method that requires multiple sensors to be assembled into an array, the sensor prepared by the present invention can achieve multi-point tactile perception with a single sensor. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 It is a schematic diagram of the overall structure of the flexible capacitive pressure sensor of the present invention; Figure 2 The flowchart of preparing the microstructure electrode layer of the present invention is as follows; Figure 3 The present invention is a flow chart for preparing a microstructured dielectric layer; Figure 4 The waveform diagram of the capacitance change rate of the triangular prism collected by the flexible capacitive pressure sensor of the present invention; Figure 5 The waveform diagram of the cylinder capacitance change rate collected by the flexible capacitive pressure sensor of the present invention; Figure 6 The waveform diagram of cone capacitance change rate collected by the flexible capacitive pressure sensor of the present invention; Figure 7 This is a waveform diagram of the capacitance change rate of a sphere collected by the flexible capacitive pressure sensor of the present invention; Figure 8 A schematic diagram comparing different sensors in the embodiment; Fig. 9 This is a performance comparison chart of different sensors in the acquisition process of the embodiment; Fig.10 1 is a comparison chart of different acquisition performances of sensor medium layers in the embodiments. DETAILED DESCRIPTION

[0019] The technical solution of the present invention is further described below through the accompanying drawings and embodiments.

[0020] Unless otherwise defined, technical or scientific terms used in the present invention shall have the common meanings understood by one having ordinary skills in the field to which the present invention belongs.

[0021] Embodiment 1 like Figure 1As shown, the present invention provides a flexible microstructure capacitive pressure sensor. The principle of the capacitive flexible pressure sensor is designed based on the principle of parallel plate capacitors. The structure of the flexible capacitive pressure sensor is generally in a sandwich shape, including two flexible electrode layers and a dielectric layer in the middle. The introduction of microstructures can improve the sensitivity of the sensor. The elastic modulus of traditional dielectric layer materials is usually high, which limits the deformation ability of the sensor and thus affects its performance. The dielectric layer with a microstructure reduces the actual load-bearing area of ​​the sensor, reduces the equivalent Young's modulus, and increases the deformation of the sensor when it is subjected to force.

[0022] like Figure 2-Figure 3 As shown, the present invention provides a method for preparing a flexible microstructure capacitive pressure sensor, comprising the following steps: S1, preparing a microstructure electrode layer; S2, preparing a microstructured dielectric layer; S3. Assemble the flexible microstructure capacitive pressure sensor.

[0023] The preparation of the microstructure electrode layer comprises the following steps: S11, laser etching the glass substrate, using software to draw a pattern structure and set etching parameters, laser etching the glass substrate to obtain a glass substrate with a microstructure; S12, cleaning and drying the glass substrate, completely immersing the etched glass substrate in a culture dish filled with alcohol, cleaning it with an ultrasonic cleaner, placing the cleaned glass substrate in a blast drying oven, and baking it at 100° C. for 5 minutes; S13, coating a silver nanowire AgNWs solution, uniformly coating the AgNWs solution on a dry glass substrate, heating the solution in an environment of 120° C. for 15 minutes to fix the silver nanowires on the glass substrate and remove excess AgNWs solution; S14, preparing a polydimethylsiloxane (PDMS) mixture, mixing the PDMS stock solution with the curing agent, and stirring for 10 minutes to ensure uniform mixing, and vacuuming for 10 minutes to remove bubbles in the mixture; S15, making an AgNWs / PDMS electrode layer, covering the silver nanowire layer with a PDMS mixture, and then heating it in a 100° C. environment for 2 hours, and obtaining an AgNWs / PDMS electrode layer with a microstructure on the surface after peeling.

[0024] The preparation of the microstructured dielectric layer comprises the following steps: S21. Prepare sandpaper, cut the sandpaper into rectangular strips of 32.5 mm × 25 mm, clean the sandpaper with alcohol to remove oil and impurities on the surface of the sandpaper, and blow dry the cleaned sandpaper for later use; S22, preparing a polydimethylsiloxane / multi-walled carbon nanotube PDMS / MWCNTs mixed solution, adding MWCNTs powder to the PDMS stock solution, and placing the mixed solution in an ultrasonic environment and stirring for 45 minutes; S23, adding a curing agent to the PDMS / MWCNTs mixture, stirring it thoroughly with a glass rod, and placing it in a vacuum box for 15 minutes to remove bubbles; S24, preparing PDMS / MWCNTs mixed solutions with different mass fractions; S25, curing and peeling, pour different mass fractions of PDMS / MWCNTs solutions into the sandpaper template respectively, place the sandpaper template containing the mixed solution in a vacuum box and let it stand for 10 minutes to remove bubbles, then cure it at 70°C for 2 hours, and use tweezers to peel off the cured PDMS / MWCNTs sandpaper dielectric layer.

[0025] Assembling a flexible microstructure capacitive pressure sensor includes the following steps: assembling a flexible microstructure capacitive pressure sensor based on a sandwich structure, wherein the upper and lower layers are microstructure electrode layers, the conductive surfaces of the two microstructure electrode layers are arranged relatively, and the middle layer is inserted into a microstructure dielectric layer, and the electrical leads are fixed to the electrode terminals using conductive silver paste and copper tape.

[0026] The flexible microstructure capacitive pressure sensor prepared above has a three-layer microstructure, in which the upper and lower electrode layers are AgNWs / PDMS with laser-etched microconvex structures, and the dielectric layer is MWCNTs / PDMS with sandpaper microstructures. Its sensitivity is better than that of traditional sensors. A machine learning model is established to combine the flexible microstructure capacitive pressure sensor with the machine learning model to achieve intelligent recognition and classification of objects of different shapes.

[0027] Building a machine learning model involves the following steps: Step 1: Data collection: Attach the sensor to the robot arm, determine the four different shapes of objects to be grasped (tetrahedron, cylinder, cone and sphere), and then grasp the physical quantity of objects of different shapes and convert them into capacitance signals. After data collection is completed, the processor needs to record the capacitance signal data of each shape and mark the corresponding shape category.

[0028] Step 2: Data preprocessing includes three parts. The first part is feature extraction: the processor extracts features from the data and extracts the capacitance signal waveform features related to material identification; the second part is data normalization: the data is normalized so that the numerical ranges of different features are the same, which is conducive to improving the accuracy of model training; the third part is data partitioning: the data set is divided into training set and test set, with the ratio of training set to validation set being 8:2, which is used for model training and evaluation.

[0029] Step 3: Machine learning model evaluation: select a suitable machine learning model based on the characteristics of the preprocessed data, use the training set data to train the model, and optimize the model parameters to achieve the best machine learning model performance. Finally, use the test set to test and compare the trained model to select the best machine learning model for subsequent recognition function use.

[0030] like Figure 4-Figure 7 The figure shows the capacitance change rate waveform of objects of different shapes collected by the flexible capacitive pressure sensor. It can be seen from the figure that when the sensor contacts objects of different shapes, the output capacitance signal waveform shows obvious differences. By comparing the waveforms corresponding to different object shapes, the sensor's recognition accuracy for objects of different shapes is preliminarily evaluated. From the waveforms, feature information related to the object shape is extracted, such as the peak position of the waveform, the width of the waveform, the symmetry of the waveform, etc. This feature information can be used as the input of the machine learning model to train and optimize the model to improve its recognition ability for objects of different shapes.

[0031] In addition, in terms of data acquisition, unlike other systems that require multiple sensors to be combined into an array to achieve tactile perception, the sensor prepared based on the present invention can realize the function of multi-point tactile perception of a single sensor, greatly reducing the complexity of the system and the design and preparation costs.

[0032] The flexible microstructure capacitive pressure sensor prepared by the present invention has a three-layer microstructure design and is superior to traditional sensors in terms of information collection sensitivity. Figure 8-Figure 9 As shown, Figure 8 It is a comparison chart of different sensor structures, where electrode layer a is a planar structure without microstructure; electrode layer b is a glass mold prepared with a laser power of 5W; electrode layer c is a glass mold prepared with a laser power of 9W; electrode layer d is a glass mold prepared with a laser power of 13W.

[0033] Fig. 9 This is a performance comparison chart of different sensors during the acquisition process. The sensitivity of the sensor is evaluated by observing the slope of the curve under different pressures, and the stability of the sensor is evaluated by observing the fluctuation of the curve under different pressures. SensorVIII with a three-layer microstructure is significantly superior to sensors without microstructures and sensors with a single-layer microstructure in terms of sensitivity and stability.

[0034] Fig.10 This is a performance comparison chart of sensors with the same electrode layer and different dielectric layers during the acquisition process. It can be seen that the sensor with a three-layer microstructure and a larger mass fraction of PDMS / MWCNTs dielectric layer in the dielectric layer has more sensitive acquisition and more stable performance.

[0035] In the field of robot tactile perception, regarding the perception function of the manipulator, most studies use integrated array sensors to achieve multi-point signal perception. Array sensors are subject to different deformations in different directions, and have different sensitivity requirements. In addition, the unit consistency of the sensor is difficult to guarantee, and adjacent units are prone to mutual interference and signal crosstalk. The flexible capacitive pressure sensor of the present invention is prepared using a flexible stretchable substrate (PDMS). Compared with traditional sensors or sensors that sacrifice stretchability and flexibility in exchange for high sensitivity, the sensor produced by the present invention has excellent stretchability, good repeatability, short delay time, and excellent repeatable bending and folding effect. It can be widely used in wearable devices for the human body for real-time monitoring of human physiological signals. Compared with the traditional method that requires multiple sensors to be assembled into an array, the sensor prepared by the present invention can achieve multi-point tactile perception of a single sensor.

[0036] Therefore, the present invention adopts the above-mentioned flexible microstructure capacitive pressure sensor, preparation method, and application, and achieves a significant improvement in sensor sensitivity through a three-layer microstructure design and an efficient and flexible preparation process; by adopting a sandpaper mold, batch and low-cost dielectric layer preparation is achieved; by adopting a flexible PDMS substrate, good tensile properties are achieved; and by a single sensor multi-point tactile perception method, a replacement for the traditional multi-sensor integrated array method is achieved.

[0037] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that they can still modify or replace the technical solution of the present invention with equivalents, and these modifications or equivalent replacements cannot cause the modified technical solution to deviate from the spirit and scope of the technical solution of the present invention.

Claims

1. A method for preparing a flexible microstructure capacitive pressure sensor, characterized in that: The following steps are involved: S1, preparing a microstructure electrode layer; S2, preparing a microstructured dielectric layer; S3. Assemble the flexible microstructure capacitive pressure sensor.

2. The method for preparing a flexible microstructure capacitive pressure sensor according to claim 1, characterized in that: The preparation of the microstructure electrode layer in S1 comprises the following steps: S11, laser etching the glass substrate, using software to draw a pattern structure and set etching parameters, laser etching the glass substrate to obtain a glass substrate with a microstructure; S12, cleaning and drying the glass substrate, completely immersing the etched glass substrate in a culture dish filled with alcohol, cleaning it with an ultrasonic cleaning machine, and baking the cleaned glass substrate in a blast drying oven; S13, coating a silver nanowire AgNWs solution, uniformly coating the AgNWs solution on a dry glass substrate, heating to fix the silver nanowires on the glass substrate and removing excess AgNWs solution; S14, preparing a polydimethylsiloxane (PDMS) mixture, mixing the PDMS stock solution with the curing agent, stirring, and vacuuming to remove bubbles in the mixture; S15, making an AgNWs / PDMS electrode layer, covering the silver nanowire layer with a PDMS mixture, and heating the layer, and obtaining an AgNWs / PDMS electrode layer with a microstructure on the surface after peeling.

3. The method for preparing a flexible microstructure capacitive pressure sensor according to claim 1, characterized in that: The preparation of the microstructured dielectric layer in S2 comprises the following steps: S21, cutting the prepared sandpaper according to size, washing it, drying it and setting it aside for later use; S22, preparing a polydimethylsiloxane / multi-walled carbon nanotube PDMS / MWCNTs mixed solution, adding MWCNTs powder to the PDMS stock solution, and stirring the mixed solution in an ultrasonic environment; S23, adding a curing agent to the PDMS / MWCNTs mixture, stirring with a glass rod, and placing in a vacuum box to stand to remove bubbles; S24, preparing PDMS / MWCNTs mixed solutions with different mass fractions; S25, curing and peeling, pouring PDMS / MWCNTs solutions with different mass fractions into the sandpaper template respectively, placing the sandpaper template containing the mixed solution in a vacuum box to stand and remove bubbles, and peeling after curing to obtain the PDMS / MWCNTs sandpaper dielectric layer.

4. The method for preparing a flexible microstructure capacitive pressure sensor according to claim 1, characterized in that: The assembly of the flexible microstructure capacitive pressure sensor in S3 includes the following steps: assembling the flexible microstructure capacitive pressure sensor based on a sandwich structure, wherein the upper and lower layers of the flexible microstructure capacitive pressure sensor are both microstructure electrode layers, the conductive surfaces of the two microstructure electrode layers are arranged relative to each other, and a microstructure dielectric layer is inserted into the middle layer between the two microstructure electrode layers.

5. The method for preparing a flexible microstructure capacitive pressure sensor according to claim 2, characterized in that: The baking temperature of the glass substrate is 80-120° C., and the baking time is 5-10 minutes.

6. The method for preparing a flexible microstructure capacitive pressure sensor according to claim 2, characterized in that: The temperature of heating the glass substrate in S13 is 100-140° C. and the time is 10-20 minutes.

7. The method for preparing a flexible microstructure capacitive pressure sensor according to claim 2, characterized in that: The heating temperature in S15 is 80-120° C., and the heating time is 2-4 hours.

8. The method for preparing a flexible microstructure capacitive pressure sensor according to claim 3, characterized in that: In the S25, the curing temperature is 50-90° C. and the curing time is 1-3 hours.

9. A flexible microstructure capacitive pressure sensor prepared according to the method for preparing a flexible microstructure capacitive pressure sensor according to any one of claims 1 to 4.

10. The application of a flexible microstructure capacitive pressure sensor according to claim 9, characterized in that: The following steps are involved: Step 1: Data collection: attach the flexible microstructure capacitive pressure sensor to the manipulator, confirm the grasped objects of different shapes, obtain the physical quantities of the objects of different shapes and convert them into capacitive signals, and mark and classify the capacitive signals through the processor; Step 2: Data preprocessing: extracting the waveform features of the capacitance signal, normalizing the data, and dividing the data into a training set and a test set; Step 3: Evaluate the machine learning model. Select a machine learning model that is suitable for the data, optimize the parameters using the training set, and then evaluate it using the test set to select the optimal model for subsequent recognition functions.

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