A triboelectric sensor in contact-separation mode, its preparation method and application

Abstract

The present invention belongs to the technical field of triboelectric nanogenerators, and particularly relates to a contact-separation mode triboelectric sensor and its preparation method and application. The triboelectric sensor is constructed by using the contact-separation mode, with the Ag interdigital electrode and the PTFE spray coating on the surface of the high-density EVA sponge as the lower friction layer and the multilayer stacked PVA-acrylene fluorescent layer as the upper friction layer. The triboelectric sensor is applied to a triboelectric power generation motion detection system, which can be used to detect the motion characteristics at different joints of the human body. The triboelectric motion detection sensor proposed by the present invention has a faster response speed and better motion intensity resolution. By converting external mechanical stimuli into electrical pulses of corresponding intensity through the triboelectric sensor, it does not require battery power supply, avoiding the dependence of the sensor on battery power.

Description

Technical Field

[0001] The present invention belongs to the technical field of triboelectric nanogenerators, and particularly relates to a contact-separation mode triboelectric sensor, a preparation method thereof, and an application thereof. Background Art

[0002] In recent decades, due to their inherent flexibility and portability, wearable or flexible electronic devices have emerged as a rapidly developing research field. It provides a wide range of applications, such as motion monitoring, rehabilitation therapy, human-machine interface (HMI), disease diagnosis, and so on. Among them, various wearable sensors are attached to the skin or directly worn on the body to collect perceived information and are expected to distinguish different body behaviors. For example, intelligent socks with deep learning signal processing and a multi-degree-of-freedom exoskeleton sensing system have been developed for gait analysis and posture perception respectively. Such wearable sensing systems digitize human activities and establish data-based health status and activity management, thus bridging the gap between humans and machines. At the same time, with the significant progress of the fifth-generation wireless network, the delay of information transmission can be ignored, and all sensory information can be uploaded to the cloud for remote analysis and data visualization through the Internet of Things and big data. Normally, wearable sensors cooperate with seamless data exchange for human motion detection, which will contribute to intelligent healthcare applications.

[0003] As an energy generation unit, in the internal circuit of a triboelectric nanogenerator, due to the triboelectrification effect, charge transfer occurs between two thin layers of triboelectric materials with different polarities, resulting in a potential difference between them; in the external circuit, electrons flow between two electrodes respectively pasted on the back of the triboelectric material layer or between the electrode and the ground under the drive of the potential difference, so as to balance this potential difference. The power sources of triboelectric nanogenerators can be large energy sources such as wind power, hydraulic power, and ocean waves that are already known to people, or environmental random energy sources often ignored by people such as human walking, body shaking, hand touching, and falling raindrops, or the rotation of wheels, the roar of machines, and so on. Therefore, triboelectric nanogenerators (TENGs) are receiving increasing attention due to their two characteristics of self-generated signals and low power consumption, especially in large-scale Internet of Things application scenarios. More importantly, due to the unique operating mechanism, triboelectric sensors have various options in terms of manufacturing technology and materials.

[0004] Recently, people have taken inspiration from nature and developed multi-layer sensors that mimic human skin. Future breakthroughs in this field require higher sensor density and greater integration of different sensor modalities, just like the receptors embedded in human skin. At the same time, manufacturing schemes with less complex structures and integration mechanisms are needed to achieve scalable large-scale sensing. Finally, hardware and data-driven algorithms should also be considered in the sensing system design process to sense large amounts of sensor data and achieve closed-loop tasks. Summary of the invention

[0005] In view of the shortcomings of the prior art, the present invention provides a contact-separation mode triboelectric sensor and a preparation method and application thereof, wherein a microelectronic circuit printer is used to print interdigital electrodes on the surface of a modified EVA sponge as the basic output electrode of a flexible friction nanogenerator; a layer of PTFE film is sprayed on the surface of half of the interdigital electrode as a lower friction layer, and an electrode is printed again on the other half of the electrode layer that has not been sprayed, so that the two parts have the same thickness and have the functions of both a friction layer and a conductive layer; PVA and an acrylic fluorescent layer are spin-coated on the surface of a flexible PET in sequence for a total of six times, and the finished product is used as an upper friction layer; flexible wearable triboelectric sensors of different sizes are prepared based on the same manufacturing process, and are used for monitoring large joint movements of the human body and monitoring digital signals of gestures; combined with a matching multi-input convolutional deep learning network, the sensor can also be used for foot shape, gait and character recognition, with an overall accuracy rate of more than 99%.

[0006] In order to achieve the above-mentioned purpose, the present invention adopts the following technical scheme: a contact-separation mode triboelectric sensor, the basic configuration of which is the contact-separation mode, with Ag interdigital electrodes and PTFE spray coating on the surface of high-density EVA sponge as the lower friction layer and a multi-layered PVA-propylene fluorescent layer as the upper friction layer.

[0007] Further, the contact-separation mode triboelectric sensor comprises, from top to bottom, a PVA film, an propylene fluorescent layer, a PVA film, an propylene fluorescent layer, a PVA film, an propylene fluorescent layer, and an interdigital electrode layer, wherein the interdigital electrode layer comprises a double-layer printed electrode and a PTFE spray layer superimposed on a single-layer printed electrode;

[0008] Alternatively, the maximum output power of the contact-separation mode triboelectric sensor is 286.84 μW.

[0009] Furthermore, charges of opposite nature can be accumulated at different locations on the surface of the same insulating friction layer at the same time.

[0010] Furthermore, a plurality of the sensors can construct a shoe insole type sensing array, the spacing between the upper friction layer and the lower friction layer is 1 mm, and the width and the finger spacing of the interdigital electrodes are 1 mm;

[0011] Alternatively, the double-layer printed electrodes inside the insole-type sensing array are grounded uniformly, and their respective single-layer printed electrodes serve as useful signal output electrodes.

[0012] Furthermore,

[0013] The material used for the printed electrodes is one of Au, Ag, and Cu nanoparticles. The thickness of the double-layer printed electrodes is 20 microns, and the thickness of the single-layer printed electrodes is 10 microns;

[0014] Alternatively, the thickness of the high-density EVA sponge is 1 mm.

[0015] A preparation method of a contact-separation mode triboelectric sensor,

[0016] (1) Prepare a surface-modified high-density EVA sponge substrate;

[0017] (2) Prepare single-layer and double-layer printed electrodes;

[0018] (3) Prepare a multi-layer stacked PVA-acrylene fluorescent layer as the upper friction layer;

[0019] (4) Prepare a triboelectric sensor in contact-separation mode;

[0020] (5) Design and manufacture triboelectric sensors and sensing arrays with corresponding specifications for specific application environments.

[0021] Furthermore,

[0022] The specific steps of step (1) include:

[0023] (1-1) Place the high-density EVA sponge in a beaker, pour 50 mL of ethanol, seal the beaker with plastic wrap, and place it in an ultrasonic cleaner for 30 min of ultrasonic cleaning;

[0024] (1-2) Take out the sponge cleaned in step (1-1) from the beaker, rinse it repeatedly with deionized water 3 times, and place it in a vacuum drying oven at 60 °C for 1 hour;

[0025] (1-3) Lay the sponge obtained in step (1-2) flat in the chamber of an ultraviolet light cleaner, and irradiate and modify one side of the sponge with ultraviolet light for 5 min;

[0026] Alternatively, the specific steps of step (2) include:

[0027] (2-1) Use a DP500 microelectronic printer to first print a layer of interdigital electrodes on the surface of the modified EVA sponge, and then place it in a vacuum drying oven at 60 °C for 1 hour;

[0028] (2-2) Ultrasonically oscillate the PTFE emulsion with a solid content of 60% for 10 minutes. Cover the surface of the printed electrode with a pre-made hollow mold, leaving only half of the electrode exposed. Uniformly spray the PTFE emulsion on the electrode surface once with a spray gun, and place the obtained product in a vacuum drying oven at 60°C for 2 hours;

[0029] (2-3) Place the product obtained in (2-2) back at the original position on the DP500 printing table. Print another layer of electrode on the surface of the electrode where the PTFE emulsion was not sprayed. After completion, place it in a vacuum drying oven at 60°C for 1 hour;

[0030] Or, the specific steps of step (3) include:

[0031] (3-1) Add 3 g of PVA particles to 20 mL of deionized water. Place the mixture in a water bath at 90°C and magnetically stir for 3 hours. After completion, take out the beaker and wait for the temperature to drop to 36°C and remain;

[0032] (3-2) Spin-coat the viscous solution obtained in (3-1) on the surface of flexible PET. In the first stage, rotate at a speed of 1070 revolutions per minute for 18 s. In the second stage, rotate at a speed of 2510 revolutions per minute for 60 s. Then place the obtained product in a vacuum drying oven at 60°C for 1 hour;

[0033] (3-3) Spin-coat a layer of acrylate fluorescence layer on the surface of the PVA film obtained in (3-2). The spin-coating parameters are the same as those in (3-2). Then place the obtained product in a vacuum drying oven at 60°C for 1 hour;

[0034] (3-4) Repeat the operations in (3-2) and (3-3) two more times in sequence;

[0035] Or, the specific steps of step (4) include:

[0036] Connect two Dupont wires to the external output contacts of the two electrodes respectively with conductive silver glue; use a thin strip of rubber with a square cross-section with a side length of 1 mm in the shape of a quadrangular prism to pad up a partition 1 mm high around the interdigital electrodes, and apply a layer of waterproof sealant on the top of the partition; cut out an upper friction layer of the same specification according to the shape surrounded by the partition, stick the friction layer facing the electrode on the partition, and place it in a vacuum drying oven at 60°C for 1 hour;

[0037] Or, the specific steps of step (5) include:

[0038] Fabricate triboelectric sensors of different sizes and specifications according to step (4) according to the different body parts to be sensed.

[0039] An application of a contact-separation mode triboelectric sensor,

[0040] The triboelectric sensor is applied to a triboelectric motion detection system, which can be used to detect the motion characteristics at different joints of the human body and can identify gesture numbers, foot types, walking postures, and individual persons.

[0041] Furthermore, the triboelectric motion detection system includes a triboelectric sensor and its array, a signal processing module, a WIFI data transmission module, a single-chip microcomputer data acquisition module, a host computer data display module, and a CNN deep learning classification network built according to specific input characteristics.

[0042] Furthermore, the current at both ends of the triboelectric sensor obtained in the experiment is connected to the signal processing module. After processing, it is connected to the ADC data acquisition port of the single-chip microcomputer and can be used to observe the pulse waveform of the triboelectric sensor under motion stimulation in real time on the host computer through the WIFI module. By performing collaborative analysis on five signals, the gesture number can be parsed. By performing collaborative analysis on ten signals, the foot type, walking posture, and individual person can be parsed.

[0043] The advantages of a contact-separation mode triboelectric sensor, its preparation method, and its application according to the present invention:

[0044] (1) A triboelectric motion sensor is designed using a PVA-acrylene fluorescent layer / PTFE-Ag contact-separation mode configuration to achieve self-driving of the sensing system. The core part of the PVA-acrylene fluorescent layer / PTFE-Ag triboelectric sensor is the internal interdigital electrode and the multilayer stacked PVA-acrylene fluorescent layer. The sizes of the triboelectric sensors for the wrist, elbow, knee, and hip are all 10 cm × 5 cm × 0.3 cm; the size of the triboelectric sensor for the finger is 5 cm × 1 cm × 0.3 cm; the sizes of the triboelectric sensors for foot type, gait, and individual person recognition are 1.7 cm × 1.6 cm × 0.3 cm. This type of sensor can generate corresponding pulse voltages and currents by itself under external mechanical stimuli, thus eliminating the need for an external power supply device.

[0045] (2) Compared with ordinary motion detection sensors, the triboelectric sensor designed based on the contact-separation mode configuration has a faster response speed to external mechanical stimuli (the response speed to finger bending is 42 ms), which can meet the requirements of high response accuracy in the fields of manipulator training detection and body rehabilitation monitoring.

[0046] (3) The output current at both ends of the sensor is connected to the signal processing circuit. After converting the current to voltage, the signal is amplified and filtered, and then connected to the ADC data acquisition port of the single-chip microcomputer and can be used to observe the voltage pulse waveform of the triboelectric sensor under motion stimulation in real time on the host computer through the WIFI module. After saving and preprocessing various signal data and importing them into the CNN network, the motion part, foot type, walking posture, gesture number, and individual person can be parsed. Brief Description of the Drawings

[0047] Figure 1 (a) is a schematic cross-sectional structure diagram of the triboelectric sensor in the contact-separation mode according to an embodiment of the present invention;

[0048] (b) is a schematic structure diagram of the triboelectric sensor in the contact-separation mode according to an embodiment of the present invention for large joint motion sensing;

[0049] (c) is a physical diagram of the insole-type sensing array constructed by the triboelectric sensor in the contact-separation mode according to an embodiment of the present invention and the corresponding 10-input deep learning CNN model structure;

[0050] Figure 2 is a schematic diagram of the operating mechanism of the contact-separation mode according to an embodiment of the present invention;

[0051] Figure 3 (a) is a test result diagram of the relationship between the output voltage / current of the triboelectric sensor for large joint motion detection and the load resistance according to an embodiment of the present invention;

[0052] (b) is the output power characteristic curve of the triboelectric sensor for large joint motion detection according to an embodiment of the present invention;

[0053] Figure 4 is the circuit layout diagram of the signal processing circuit of the triboelectric sensor in the contact-separation mode according to an embodiment of the present invention;

[0054] Figure 5 (a)-(j) are photos of gesture numbers and their corresponding multi-channel action waveforms according to an embodiment of the present invention;

[0055] Figure 6 (a) is the accuracy confusion matrix of gesture number recognition based on the triboelectric sensor in the contact-separation mode according to an embodiment of the present invention;

[0056] (b) is the CNN network structure of gesture number recognition based on the triboelectric sensor in the contact-separation mode according to an embodiment of the present invention;

[0057] Figure 7 (a)-(e) are the response time curves at a bending frequency of 1 Hz and a bending degree of 45° for finger bending detection according to an embodiment of the present invention;

[0058] Figure 8 is the accuracy confusion matrix of individual person recognition based on the triboelectric sensor in the contact-separation mode according to an embodiment of the present invention;

[0059] Figure 9 is the CNN network structure of individual person recognition based on the triboelectric sensor in the contact-separation mode according to an embodiment of the present invention. Detailed Description of the Invention

[0060] The specific implementation manner will be further described below in conjunction with the accompanying drawings.

[0061] Embodiment 1:

[0062] A contact-separation mode triboelectric sensor based on coplanar interdigitated electrodes.

[0063] The contact-separation mode triboelectric sensor is composed of interdigital electrodes and an insulating upper friction layer on the same plane. The interdigital electrodes on the same plane are obtained by printing on a surface-modified high-density EVA sponge using a DP500 microelectronic printer. After the first printing of the electrode, wait for the electrode to dry and take shape, cover the printed electrode surface with a pre-made hollow mold, leaving only half of the electrode exposed, and spray PTFE emulsion evenly on the electrode surface once with a spray gun. Place the resultant in a vacuum drying oven at 60°C for 2 hours, then place the resultant in the original position on the printer platform, and print another layer of electrode on the electrode surface that is not sprayed with PTFE, so that the thickness of the two parts of the interdigital electrode is almost the same; add 3g of PVA particles, place the mixture in a water bath at 90°C and stir magnetically for 3 hours, take out the beaker after completion and wait for the temperature to drop to 36°C and maintain it, spin-coat the resulting viscous solution on the surface of the flexible PET, rotate at a speed of 1070 rpm for 18s in the first stage, and rotate at a speed of 2510 rpm for 60s in the second stage, then place the resultant in a vacuum drying oven at 60°C for 1 hour, spin-coat a layer of acrylic fluorescent layer on the surface of the resulting PVA film, the spin-coating parameters are the same as the above parameters, and then place the resultant in a vacuum drying oven at 60°C for 1 hour , repeat the above operations twice in sequence, and finally obtain a multi-layered PVA-acrylic fluorescent upper friction layer; use conductive silver glue to connect the two DuPont wires to the external output contacts of the two electrodes respectively; use thin strips of rubber (quadrangular prisms, the cross-section is a square with a side length of 1mm) to pad a 1mm high partition around the interdigital electrodes, and apply a layer of waterproof sealant on the top of the partition; cut out the upper friction layer of the same specification according to the shape surrounded by the partition, stick the friction layer on the partition facing the electrode, and place it in a vacuum drying oven at 60℃ for 1 hour to obtain the required friction electric sensor.

[0064] After the sprayed PTFE emulsion dries, a PTFE film with tiny particles will be formed on its surface, which increases the roughness of the PTFE film and is beneficial to improving the triboelectric performance.

[0065] Figure 3 (a) is the test result diagram of the relationship between the output voltage / current and load resistance of the contact-separation mode triboelectric sensor. In the experiment, the reciprocating motion of a linear motor was used to test the output performance of the sensor as the nanogenerator itself (the size of the triboelectric sensor here is 10cm×5cm×0.3cm).

[0066] The maximum output power of the contact-separation mode triboelectric sensor is 286.84 μW. Figure 3 (b) is the power-load relationship curve;

[0067] The material used for the printing electrode is one of Au, Ag, and Cu nanoparticles. The thickness of the double-layer printed electrode is 20 μm, and the thickness of the single-layer printed electrode is 10 μm.

[0068] The average molecular weight of the PVA particles is 27,000, and the solid content of the PTFE emulsion is 60%.

[0069] After the sprayed PTFE emulsion dries, a PTFE film with tiny particles will be formed on its surface, which increases the roughness of the PTFE film and is beneficial to improving the triboelectric performance.

[0070] The thickness of the high-density EVA sponge is 1 mm.

[0071] Preparation method of a contact-separation mode triboelectric sensor based on a coplanar interdigital electrode

[0072] (1) Prepare an interdigital printed electrode layer on a high-density EVA sponge substrate;

[0073] (1-1) Place the purchased high-density EVA sponge in a beaker, pour 50 mL of ethanol, seal the beaker with plastic wrap, and place it in an ultrasonic cleaner for 30 min of ultrasonic cleaning;

[0074] (1-2) Take out the sponge cleaned in step (1-1) from the beaker, rinse it repeatedly with deionized water 3 times, and place it in a vacuum drying oven at 60 °C for 1 hour;

[0075] (1-3) Lay the sponge obtained in step (1-2) flat in the chamber of an ultraviolet light cleaner, and irradiate and modify one side of the sponge with ultraviolet light for 5 min;

[0076] (1-4) Use a DP500 microelectronic printer to print a layer of interdigital electrodes on the surface of the modified EVA sponge, and then place it in a 60 °C vacuum drying oven for 1 hour;

[0077] (1-5) Ultrasonically vibrate the PTFE emulsion with a solid content of 60% for 10 minutes. Cover the printed electrode surface with a pre-made hollow mold, leaving only half of the electrode exposed. Spray the PTFE emulsion evenly on the electrode surface once with a spray gun, and place the obtained product in a 60 °C vacuum drying oven for 2 hours;

[0078] (1 - 6) Place the product obtained in (1 - 5) back at its original position on the DP500 printing table. Print another layer of electrode on the surface of the electrode without spraying PTFE emulsion. After completion, place it in a vacuum drying oven at 60 °C for 1 hour;

[0079] (2) Prepare the multi - layer PVA - acrylate fluorescence upper friction layer on the PET substrate

[0080] (2 - 1) Add 3 g of PVA particles to 20 mL of deionized water. Place the mixture in a water bath at 90 °C and stir magnetically for 3 hours. After completion, take out the beaker and wait for the temperature to drop to 36 °C and maintain it;

[0081] (2 - 2) Spin - coat the viscous solution obtained in (2 - 1) on the surface of flexible PET. In the first stage, spin at a speed of 1070 revolutions per minute for 18 s. In the second stage, spin at a speed of 2510 revolutions per minute for 60 s. Then place the obtained product in a vacuum drying oven at 60 °C for 1 hour;

[0082] (2 - 3) Spin - coat a layer of acrylate fluorescence layer on the surface of the PVA film obtained in (2 - 2). The spin - coating parameters are the same as those in (2 - 2). Then place the obtained product in a vacuum drying oven at 60 °C for 1 hour;

[0083] (2 - 4) Repeat the operations in (2 - 2) and (2 - 3) two more times in sequence;

[0084] (3) Prepare the triboelectric sensor in contact - separation mode

[0085] Connect two Dupont wires to the external output contacts of the two electrodes respectively with conductive silver glue; Use a thin strip of rubber (quadrangular prism - shaped, with a square cross - section with a side length of 1 mm) to pad up a 1 - mm - high partition around the interdigital electrodes, and apply a layer of waterproof sealant on the top of the partition; Cut out the upper friction layer of the same specification according to the shape surrounded by the partition, attach the friction layer facing the electrode to the partition, and place it in a vacuum drying oven at 60 °C for 1 hour;

[0086] (4) Electrical performance of the triboelectric sensor in contact - separation mode

[0087] The maximum output power generated through triboelectric characteristics can reach 286.84 μW.

[0088] The triboelectric motion detection system in contact - separation mode fabricated in the present invention can be used to detect the motion characteristics at different joints of the human body. Combining with the CNN deep - learning classification network, it can also achieve accurate recognition of the types of moving joints, gesture numbers, foot types, walking postures, and individual persons.

[0089] The system includes a triboelectric sensor and its array prepared by a nanogenerator, a signal processing module, a WIFI data transmission module, a single-chip microcomputer data acquisition module, a host computer data display module, and a CNN deep learning classification network built according to specific input characteristics.

[0090] In the CNN deep learning classification network, a 5-layer convolutional neural network is built, including 2 convolutional layers, 2 fully connected layers, and a SoftMax classification layer.

[0091] (1) Input layer: The normalized motion data is used as the input layer, and the two-dimensional form of the input layer size is 40×1.

[0092] (2) Convolutional layer 1: The convolutional kernel size is set to 8×1, the number is 16, and the stride is 1. After the input layer undergoes convolution operation, local features are batch-normalized in the BN layer to make the feature output of each layer close to the standard normal distribution. Finally, the Leaky ReLU function is used for activation, and a feature matrix of 33×16 is output.

[0093] (3) Pooling layer 1: The type is the maximum pooling layer, the pooling kernel size is 1×1, and the stride is 1. After the feature matrix output by convolutional layer 1 undergoes pooling operation, the output matrix size is 33×16.

[0094] (4) Convolutional layer 2: 32 convolutional kernels with a size of 8×1 and a stride of 1. After the output of pooling layer 1 undergoes convolution, BN layer, and Leaky ReLU operations, the output matrix size is 26×32.

[0095] (5) Pooling layer 2: The maximum pooling layer is adopted, the pooling kernel size is 1×1, and the stride is 1. After this layer, convolutional layer 2 outputs a matrix of 26×32.

[0096] (6) Fully connected layer 1: The number of neurons is 64, and the Leaky ReLU activation function is used. The output feature matrix of pooling layer 2 is converted into a vector with a length of 8320 and sent to 64 fully connected neurons, and a feature vector with a length of 64 is output. The dropout layer is adopted to prevent the model from overfitting, and the parameter is set to 0.5.

[0097] (7) Fully connected layer 2: The number of neurons is 10, and the Leaky ReLU activation function is used. The dropout layer is adopted to prevent the model from overfitting, and a feature vector with a length of 10 is output.

[0098] (8) SoftMax Classification Layer: Since 10 individuals need to be distinguished, the number of neurons in this layer is set to 10. The SoftMax function is used as the activation function to obtain the probabilities of the predicted values being 1, 2, 3, 4, 5, 6, 7, 8, 9, and 10 respectively. The result predicted by the neural network is the category corresponding to the maximum probability value.

[0099] Connect the current at both ends of the triboelectric sensor obtained in the experiment to the signal processing module. After processing, connect it to the ADC data acquisition port of the single-chip microcomputer and through the WIFI module, the pulse waveform of the triboelectric sensor under motion stimulation can be observed in real time on the upper computer. By performing collaborative analysis on five signals, the gesture numbers can be parsed, and by performing collaborative analysis on ten signals, the foot type, walking posture, and individual can be parsed.

[0100] In each motion detection triboelectric sensor, their basic structure is a contact-separation mode triboelectric nanogenerator. The size of the device is enlarged or reduced according to the actual situation at the main detection part.

[0101] Figure 5 (a)-(j) are photos of the gesture numbers in the embodiments of the present invention and their corresponding multi-channel action waveforms.

[0102] The triboelectric motion sensor designed with the PVA-acrylene fluorescent layer / PTFE-Ag contact-separation mode realizes self-driving of the sensing system. The core part of the PVA-acrylene fluorescent layer / PTFE-Ag triboelectric sensor is the internal interdigital electrode and the multi-layer stacked PVA-acrylene fluorescent layer. The sizes of the triboelectric sensors for the wrist, elbow, knee, and hip are all 10 cm × 5 cm × 0.3 cm; the size of the triboelectric sensor for the finger is 5 cm × 1 cm × 0.3 cm; the size of the triboelectric sensor for foot type, gait, and individual recognition is 1.7 cm × 1.6 cm × 0.3 cm;. This type of sensor can generate corresponding pulse voltages and currents by itself under external mechanical stimulation, thus eliminating the need for an external power supply device.

[0103] Compared with ordinary motion detection sensors, the triboelectric sensor designed based on the contact-separation mode configuration has a faster response speed to external mechanical stimulation (the response speed to finger bending is 42 ms), which can meet the requirements of high response accuracy in the fields of robotic hand training detection and body rehabilitation monitoring.

[0104] Compared with traditional motion detection sensors, this motion detection sensor has the characteristics of not consuming extra power and having a faster response speed. The triboelectric power generation motion sensor designed with the PVA-acrylene fluorescent layer / PTFE-Ag contact-separation mode converts mechanical stimulation into electrical pulse signals, does not require battery power supply, avoids the dependence of the sensor on the battery power source, and lays a foundation for the development of self-driving and high-performance motion detection sensing technology.

[0105] Example 2:

[0106] Preparation method of a contact-separation mode triboelectric sensor based on a coplanar interdigital electrode:

[0107] As Figure 1 (a) shows, from top to bottom, a PVA film, a propylene fluorescent layer, a PVA film, a propylene fluorescent layer, a PVA film, and a propylene fluorescent layer; further down are two schematic diagrams in the interdigital electrode layer, where the left one is a double-layer printed Ag electrode and the right one is a PTFE spray coating and a single-layer printed Ag electrode. Connect two DuPont wires to the external output contacts of the two electrodes respectively with conductive silver glue; use a thin strip of rubber (quadrangular prism-shaped, with a square cross-section with a side length of 1 mm) to pad up a 1 mm high partition around the interdigital electrode, and apply a layer of waterproof sealant on the top of the partition; cut out an upper friction layer of the same specification according to the shape surrounded by the partition, stick the friction layer face to the electrode on the partition, and place it in a 60 °C vacuum drying oven for 1 hour; the sizes of the triboelectric sensors for the wrist, elbow, knee, and hip are all 10 cm × 5 cm × 0.3 cm, as Figure 1 (b) shows; the size of the triboelectric sensor for the finger is 5 cm × 1 cm × 0.3 cm; the sizes of the triboelectric sensors for foot type, gait, and human individual recognition are 1.7 cm × 1.6 cm × 0.3 cm, as Figure 1 (c) shows.

[0108] Operating mechanism of a contact-separation mode triboelectric sensor constructed with a coplanar interdigital electrode:

[0109] As Figure 2 shown, (i) in the original situation, there is no triboelectric potential; (ii) when the upper friction layer contacts the lower friction layer, positive triboelectric charges will be induced on the Ag electrode, causing electrons in the electrode to flow from the electrode to the ground. Since the upper friction layer is non-conductive, charges of different natures can be generated and accumulated at both ends of it; (iii) the departure of the upper friction layer causes electrons to flow back to neutralize the positive charges on the electrode. A certain amount of charges remain at both ends of the upper friction layer; (iv) the upper friction layer continues to depart, causing the positive charges on the electrode to be completely neutralized; (v) the backward movement of the upper friction layer will again cause positive charges on the electrode.

[0110] As Figure 4 shown, connect the output end of the contact-separation mode triboelectric sensor to the input end of the signal modulation circuit, and amplify and filter the signal before outputting.

[0111] Figure 6 (a) shows the accuracy confusion matrix for recognizing gesture numbers, Figure 6(b) is the corresponding CNN network structure diagram; on the one hand, it benefits from the accurate detection of finger signals by the sensor array, and on the other hand, it benefits from the adaptive adjustment of the CNN network structure and parameters, enabling the classification system to achieve high-precision recognition of gesture digital signals.

[0112] Figure 7 (a)-(e) are the response / recovery time curves of different fingers at a bending frequency of 1 Hz and a bending angle of 45°. It can be seen that there are obvious mathematical differences in the motion characteristics of different fingers.

[0113] Example 3:

[0114] An application of an insole-type triboelectric motion detection array manufactured based on a contact-separation mode triboelectric sensor

[0115] Sampled and analyzed the voltage data of different individuals stepping on the insole-type sensing array:

[0116] Figure 1 (c) is the disassembly diagram of the insole-type sensing array and the corresponding 10-input deep learning CNN model structure. The double-layer printed electrodes inside the array are uniformly grounded, and their respective single-layer printed electrodes are used as useful signal output electrodes; the electrode signal data can be recognized as foot type, gait, and individual after network analysis and classification.

[0117] Figure 8 It is the test result of individual recognition using the signals detected by the insole-type sensing array. It can be seen from the confusion matrix that the ten-channel CNN can accurately identify different individuals.

[0118] Figure 9 It is the ten-channel CNN structure diagram. After multiple experimental transformations of the network layers and parameter adjustments, high-precision recognition of individuals is finally achieved.

[0119] The contact-separation mode triboelectric sensor proposed by the present invention can be customized according to specific usage scenarios; the motion detection sensors and arrays designed based on the contact-separation mode have the characteristics of not consuming extra power and faster response speed compared with traditional motion detection sensors. This type of triboelectric sensor converts mechanical stimuli into electrical pulse signals and does not require battery power supply, avoiding the dependence of the sensor on battery power. All motion data can achieve high-precision discrimination of motion types after passing through a specific CNN deep learning classification network.

[0120] The above embodiments are only to illustrate the technical concept and characteristics of the present invention, and their purpose is to enable ordinary technicians in the field to understand the content of the present invention and implement it accordingly, and cannot be used to limit the protection scope of the present invention. Any equivalent changes or modifications made according to the essence of the content of the present invention should be covered within the protection scope of the present invention.

Claims

1. Preparation method of a contact-separation mode triboelectric sensor, characterized in that: (1) Prepare a surface-modified high-density EVA sponge substrate; (2) Prepare single-layer and double-layer printed electrodes; (3) Prepare a multi-layer stacked PVA-acrylene fluorescent layer as the upper friction layer; (4) Prepare a triboelectric sensor in contact-separation mode; (5) Design and manufacture triboelectric sensors and sensor arrays with corresponding specifications for specific application environments; The specific steps of step (1) include: (1-1) Place the high-density EVA sponge in a beaker, pour 50 mL of ethanol, seal the beaker with plastic wrap, and place it in an ultrasonic cleaner for 30 min of ultrasonic cleaning; (1-2) Take out the sponge cleaned in step (1-1) from the beaker, rinse it repeatedly with deionized water 3 times, and place it in a vacuum drying oven at 60 °C for 1 hour of drying; (1-3) Lay the sponge obtained in step (1-2) flat in the chamber of an ultraviolet light cleaner, and irradiate and modify one side of the sponge with ultraviolet light for 5 min; The specific steps of step (2) include: (2-1) Use a DP500 microelectronic printer to print a layer of interdigital electrodes on the surface of the modified EVA sponge first, and place it in a 60 °C vacuum drying oven for 1 hour after completion; (2-2) Ultrasonically vibrate the PTFE emulsion with a solid content of 60% for 10 minutes. Cover the printed electrodes with a pre-made hollow mold, only exposing half of the electrodes. Spray the PTFE emulsion evenly on the electrode surface once with a spray gun, and place the obtained product in a 60 °C vacuum drying oven for 2 hours; (2-3) Place the product obtained in (2-2) back at the original position on the DP500 printing table, print another layer of electrodes on the electrode surface where the PTFE emulsion was not sprayed, and place it in a 60 °C vacuum drying oven for 1 hour after completion; The specific steps of step (3) include: (3-1) Add 3 g of PVA particles to 20 mL of deionized water, place the mixture in a water bath at 90 °C and stir magnetically for 3 hours. After completion, take out the beaker and wait for the temperature to drop to 36 °C and remain; (3-2) Spin-coat the viscous solution obtained in (3-1) on the surface of flexible PET. In the first stage, rotate at a speed of 1070 revolutions per minute for 18 s, and in the second stage, rotate at a speed of 2510 revolutions per minute for 60 s. Then place the obtained product in a 60 °C vacuum drying oven for 1 hour; (3-3) Spin-coat a layer of acrylene fluorescent layer on the surface of the PVA film obtained in (3-2). The spin-coating parameters are the same as those in (3-2). Then place the obtained product in a 60 °C vacuum drying oven for 1 hour; (3-4) Repeat the operations in (3-2) and (3-3) two more times in sequence; The specific steps of step (4) include: Use conductive silver glue to connect two Dupont wires to the external output contacts of the two electrodes respectively; use a thin strip of rubber with a square cross-section with a side length of 1 mm for the quadrilateral column to pad up a 1 mm high partition around the interdigital electrodes, and apply a layer of waterproof sealant on the top of the partition; cut out an upper friction layer with the same specifications according to the shape surrounded by the partition, stick the friction layer facing the electrode on the partition, and place it in a 60 °C vacuum drying oven for 1 hour; The specific steps of step (5) include: Fabricating triboelectric sensors of different sizes and specifications according to step (4) based on the different body parts sensed.

2. A contact-separation mode triboelectric sensor prepared by the method according to claim 1, characterized in that: The basic configuration of the triboelectric sensor is the contact-separation mode, with Ag interdigital electrodes on the surface of high-density EVA sponge and a PTFE spray coating as the lower friction layer, and a multilayer stacked PVA-acrylic fluorescent layer as the upper friction layer; The contact-separation mode triboelectric sensor from top to bottom is successively an acrylic fluorescent layer, a PVA film, an acrylic fluorescent layer PVA film, an acrylic fluorescent layer, a PVA film, and an interdigital electrode layer. The interdigital electrode layer includes a double-layer printed electrode and a single-layer printed electrode stacked with a PTFE spray coating.

3. The contact-separation mode triboelectric sensor according to claim 2, characterized in that: The maximum output power of the contact-separation mode triboelectric sensor is 286.84 μW.

4. The contact-separation mode triboelectric sensor according to claim 2, characterized in that: Oppositely charged particles can be simultaneously accumulated at different positions on the surface of the same insulating friction layer.

5. The contact-separation mode triboelectric sensor according to claim 4, characterized in that: Multiple such sensors can form an insole-type sensing array, with a distance of 1 mm between the upper friction layer and the lower friction layer, and a width and finger spacing of 1 mm for the interdigital electrodes.

6. The contact-separation mode triboelectric sensor according to claim 5, characterized in that: In the insole-type sensing array, the double-layer printed electrodes inside are uniformly grounded, and their respective single-layer printed electrodes serve as useful signal output electrodes.

7. The contact-separation mode triboelectric sensor according to claim 2, characterized in that: The material used for the printed electrodes is one of Au, Ag, and Cu nanoparticles. The thickness of the double-layer printed electrode is 20 microns, and the thickness of the single-layer printed electrode is 10 microns.

8. The contact-separation mode triboelectric sensor according to claim 2, characterized in that: The thickness of the high-density EVA sponge is 1 mm.

9. An application of a contact-separation mode triboelectric sensor, where the triboelectric sensor is prepared by the method according to claim 1, characterized in that: The triboelectric sensor is applied to a triboelectric power generation motion detection system, which can be used to detect the motion characteristics at different joints of the human body and can identify gesture numbers, foot types, walking postures, and individual persons.

10. The application of the contact-separation mode triboelectric sensor according to claim 9, characterized in that: The triboelectric motion detection system includes a triboelectric sensor and its array, a signal processing module, a WIFI data transmission module, a single-chip microcomputer data acquisition module, an upper computer data display module, and a CNN deep learning classification network built according to specific input characteristics.

11. The application of the contact-separation mode triboelectric sensor according to claim 9, characterized in that: The current at both ends of the triboelectric sensor obtained in the experiment is connected to the signal processing module. After processing, it is connected to the ADC data acquisition port of the single-chip microcomputer, and through the WIFI module, the pulse waveform of the triboelectric sensor under motion stimulation can be observed in real time on the upper computer. By performing collaborative analysis on the five-way signals, the gesture numbers can be parsed, and by performing collaborative analysis on the ten-way signals, the foot type, walking posture, and individual person can be parsed.

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