Performance-programmable pressure sensor and preparation method and application thereof

Abstract

The invention relates to a performance-programmable pressure sensor and a preparation method and application thereof.The preparation method of the performance-programmable pressure sensor comprises the steps that a PDMS soft template is prepared firstly, then an electrode layer base layer and an ion layer are prepared based on the PDMS soft template, a conductive film is deposited on the surface of the electrode layer base layer to obtain an electrode layer, and the electrode layer base layer and the ion layer are prepared. And finally, assembling the two electrode layers and one ion layer to prepare the pressure sensor with programmable performance. According to the structural design of the pressure sensor with the programmable performance, a gradient modulus and microstructure comprehensive strategy is provided, microstructures with different heights and rigid-flexible mixed materials are adopted to regulate and control the sensitivity and the detection range of the sensing device, and high-sensitivity sensing in a wide pressure range is achieved; meanwhile, the pressure sensor can adapt to a handwriting recognition application scene, the sensitivity or the sensing range can be improved, and accurate information is extracted and recognized through a machine learning model.

Description

Technical Field

[0001] The present invention belongs to the technical field of flexible sensors, and in particular relates to the design of a pressure sensor with programmable performance, a preparation method and an application thereof. Background Art

[0002] Flexible pressure sensor devices can simulate the perception function of human skin by monitoring changes in electrical signals. They can be divided into resistive, capacitive, piezoelectric and triboelectric types. Resistive sensing has high sensitivity, but there is low-frequency response and high hysteresis. Triboelectric and piezoelectric types can only detect dynamic signals. Capacitive pressure sensors have a simple structure, stable response, and low hysteresis, but the deformation of the dielectric layer is small, resulting in low sensitivity. The new pressure sensing mechanism - ionization sensing, uses ionic materials instead of dielectric layers to show ultra-high sensitivity. The high sensitivity of the ionization sensing mechanism comes from the double layer effect formed at the interface between the electrolyte and the electrode. When the ionic material contacts the electrode, the carriers in the electrode will repel ions of the same charge, and ions of opposite charge are attracted to the electrode surface, thereby forming a double layer at the interface between the ionic material and the electrode.

[0003] Since the interfacial double-layer capacitance is proportional to the contact area between the ionic material and the electrode, microstructures, porous structures, and multilayer designs have been used in the sensitive layer of sensor devices to improve performance, but the overall modulus reduction of the microstructure layer caused by these strategies will limit the pressure sensing range. For example, the research group of Professor Guo Chuanfei of the Southern University of Science and Technology reported an ion-type pressure sensor based on hierarchical microstructures, which achieved high sensitivity within a pressure range of 485 kPa (~49 kPa). -1 ). Human skin is mainly composed of three layers: epidermis, dermis and subcutaneous fat. The modulus gradually decreases, effectively transmitting external stimuli. Inspired by the gradient modulus of human skin from epidermis to dermis, a friction electric pressure sensor based on a layered nanoporous interlocking micro-ridge structure with gradient stiffness was proposed in the literature, with a maximum pressure sensitivity of 0.55VkPa -1 , the pressure detection range is only 100kPa.

[0004] Intelligent systems use tactile sensing signals to obtain precise information to understand and infer interactive information or perform complex tasks in different scenarios. As the amount of data obtained from continuous monitoring increases, the efficiency of traditional data evaluation supervised by individuals is limited. Nowadays, machine learning algorithms can achieve effective data analysis, accurate recognition and direction guidance, but general models require high-quality big data, which requires pressure sensors to provide sufficient pressure resolution for various human motion detection.

[0005] In summary, existing flexible sensors have problems of narrow detection range and low sensitivity, and there is an urgent need to develop a flexible pressure sensor device that can achieve comprehensive improvement in detection range and sensitivity. Summary of the invention

[0006] In order to solve the above problems existing in the prior art, the present invention provides a design of a pressure sensor with programmable performance, a preparation method and an application thereof.

[0007] The structural design of the programmable performance pressure sensor described in the present invention proposes a comprehensive strategy of gradient modulus and microstructure, and adopts microstructures of different heights and rigid-flexible hybrid materials to regulate the sensitivity and detection range of the sensor device, thereby achieving high-sensitivity sensing in a wide pressure range. At the same time, in order to adapt to specified application scenarios, the sensitivity or sensing range can be improved respectively, and machine learning models can be used to extract and identify accurate information.

[0008] The technical solution adopted by the present invention is:

[0009] A flexible pressure sensor with programmable performance comprises a flexible substrate, an electrode layer, a flexible ion layer and a spacer layer. Two materials with gradient modulus are provided in the planar structure of the flexible substrate, and a gradient microstructure is provided on the surface of the flexible substrate. The electrode layer is provided on the surface of the flexible substrate, and the surface of the flexible ion layer is provided with a gradient microstructure. Frames are provided around the flexible substrate and the flexible ion layer, and the frame is at the same height as the surface microstructure. The spacer layer is located between the flexible substrate and the flexible ion layer and contacts the frame of the flexible substrate and the flexible ion layer, and is assembled into a "sandwich" structure with interlocking microstructures through lamination.

[0010] The two materials with gradient modulus in the flexible substrate are hard TPU and soft TPU respectively;

[0011] The difference in Young's modulus between the hard TPU and the soft TPU is 0.1 MPa-0.3 MPa, and the preferred modulus difference is 0.222 MPa; the thickness ratio of the hard TPU to the soft TPU is 1:1, 1:1.5, 1:2, and the preferred thickness ratio is 1:1.5.

[0012] The gradient microstructure on the surface of the flexible ion layer is a mixed cylinder array composed of hemispheres and cylinders of different heights, and the material of the gradient microstructure is hard TPU;

[0013] In the gradient microstructure, the radius of the hemisphere is 200 μm, and the height of the cylinder is 0, 200 μm, and 400 μm, respectively, and they are named HC 0 HC 200 and HC 400 .

[0014] The programmable performance feature of the pressure sensor is achieved by regulating the material modulus gradient and gradient microstructure density, wherein the pressure sensor performance mainly refers to sensitivity and pressure sensing range, and the gradient microstructure density mainly refers to HC 400 Density. In the gradient microstructure, when the HC 400 The density is 1-10 cm -2 When the density is low, when the HC 400 The density is 11-20 cm -2 , is medium density; when the HC 400 The density is 21-30 cm -2 , which is high density.

[0015] The method for preparing the programmable performance flexible pressure sensor comprises the following steps:

[0016] (1) Design 3D printing mold

[0017] Draw a 3D model in a 3D design software, slice the 3D model using slicing software, and print using photo-curing technology to obtain a 3D printed mold;

[0018] (2) Preparation of PDMS soft template

[0019] A PDMS solution is dripped onto the 3D printing mold, and after heating and curing, the PDMS layer is peeled off to obtain a PDMS soft template for preparing an electrode layer or an ion layer;

[0020] (3) Preparation of flexible substrate

[0021] Dispersing TPU in an organic solvent and mixing it thoroughly to obtain a TPU solution; dripping the TPU solution on a PDMS soft template for preparing an electrode layer, heating it for a first curing, and forming a hard TPU layer; based on the coffee ring effect produced by the evaporation of the solvent in the solution, dripping a soft TPU solution on the concave part of the back side of the hard TPU layer, heating it for a second curing to form a soft TPU layer, and peeling it off from the PDMS template to obtain a flexible substrate;

[0022] (4) Depositing a conductive film on the surface of the electrode layer substrate

[0023] First, masking the frame portion of the electrode layer substrate with a tape, then depositing a conductive film on the electrode layer substrate, and removing the tape after completion, thereby obtaining the electrode layer;

[0024] (5) Preparation of ion layer

[0025] Adding an ionic liquid (IL) to the hard TPU solution, heating and fully mixing to obtain an ionic solution; drop-coating the ionic solution on the PDMS mold of the ionic layer, heating and curing, and peeling off after cooling to room temperature to obtain an ionic layer;

[0026] (6) Assembling and preparing pressure sensor

[0027] The tape is cut into a frame shape as a spacer layer between the electrode layer and the ion layer, and then the two electrode layers are bonded and assembled on both sides of the ion layer respectively to obtain the programmable performance pressure sensor.

[0028] In step (3), the TPU solution used in the first thermal curing is prepared by mixing TPU particles with an organic solvent, so as to form a cavity filled during the second thermal curing by evaporation of the solvent, and the organic solvent is N,N-dimethylformamide (DMF) or tetrahydrofuran (THF);

[0029] The mass ratio of the TPU to the organic solvent is in the range of 1:3-1:10, with a preferred ratio of 1:5;

[0030] In step (5), different weights of ionic liquids are added to the TPU solution, with a weight ratio ranging from 30 to 60 wt%, preferably 55 wt%; the ionic liquid is an aprotic ionic liquid, preferably [EMIM] + [TFSI] - ;

[0031] In step (6), the spacer layer prevents the electrode layer microstructure and the ion layer microstructure from contacting each other, and the thickness of the spacer layer is in the range of 20-200 μm. Preferably, the spacer layer material is polyimide and has a thickness of 50 μm.

[0032] The method can prepare a pressure sensor with programmable performance.

[0033] The programmable performance pressure sensor is used in intelligent robots and flexible wearable devices.

[0034] The present invention has the following specific beneficial effects:

[0035] The method for preparing a programmable performance pressure sensor described in the present invention comprises the following steps: first preparing a PDMS soft template, then preparing an electrode layer base and an ion layer based on the PDMS soft template, and depositing a conductive film on the surface of the electrode layer base to obtain the electrode layer, and finally assembling two electrode layers and an ion layer to prepare a programmable performance pressure sensor; the structural design of the programmable performance pressure sensor described in the present invention proposes a gradient modulus and microstructure comprehensive strategy, and uses microstructures of different heights and rigid-flexible hybrid materials to regulate the sensitivity and detection range of the sensor device to achieve high-sensitivity sensing within a wide pressure range; at the same time, in order to adapt to specified application scenarios, the sensitivity or sensing range can be improved respectively, and a machine learning model can be used to extract and identify accurate information. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0037] Figure 1 The structure design of the pressure sensor device is shown; among them, Figure 1 a is a schematic diagram of the human skin structure; Figure 1 b is an enlarged view of the electrode and substrate; Figure 1 c is a schematic diagram of the gradient strategy; Figure 1 d is the structural diagram of the ionization pressure sensor;

[0038] Figure 2 Shown is a flow chart of the preparation of the programmable performance pressure sensor of the present invention;

[0039] Figure 3 Shown is a schematic diagram of EDL based on the Gouy-Chapman-Stern model and a schematic diagram of improving capacitance response through electrical optimization; where: Figure 3 a is a schematic diagram of EDL based on the Gouy-Chapman-Stern model; Figure 3 b is a schematic diagram of improving the capacitance response through electrical optimization;

[0040] Figure 4 Shown are the electrical properties of ion gels with different IL contents; Figure 4 a is the Nyquist plot of ion gel, Figure 4 b is the ionic conductivity of the ion gel;

[0041] Figure 5 The ionization pressure sensor is shown as a planar structure; wherein, Figure 5a is the equivalent circuit diagram of the pressure sensor; Figure 5 b is the Young’s modulus of the TPU and TPU / IL used; Figure 5 c is the performance curve of the plane structure pressure sensor with different gradient moduli;

[0042] Figure 6 It is shown that the pressure sensing performance regulation of the pressure sensor is achieved by introducing the TPU (SH-50) planar structure into the microstructure; Figure 6 a is a schematic diagram of the composition of the mixed cylinder, which is composed of a hemisphere and cylinders with different heights of 0, 200 μm, and 400 μm, respectively. 0 HC 200 HC 400 ; Figure 6 b is a schematic diagram of the initial state and the pressure state of the pressure sensor; Figure 6 c is the performance curve of ionization pressure sensor with different microstructures;

[0043] Figure 7 Displayed as HC in the pressure sensor 0 HC 200 and HC 400 The pressure sensing performance regulation of the gradient microstructure is introduced in combination; Figure 7 a is a schematic diagram of the gradient microstructure with different hybrid cylinder densities; Figure 7 b is a schematic diagram of different pressure states of the pressure sensor; Figure 7 c is the performance comparison of pressure sensors based on different microstructure densities;

[0044] Figure 8 Shown is the repeatability of the pressure sensor under a cyclic pressure load of 900 kPa;

[0045] Fig. 9 The pressure resolution test result diagram of the pressure sensor is shown;

[0046] Fig.10 A schematic diagram showing the workflow of machine learning-assisted handwriting recognition;

[0047] Fig.11 The capacitance signal curves show five volunteers writing "Beihang";

[0048] Fig.12 Shown is the confusion matrix of the classification model built based on the content written by the same volunteer and the classification accuracy of identifying the same content written by five volunteers; Fig.12 a is the confusion matrix for classifying a volunteer writing “Beihang”, “A”, “B”, and “C”; Fig.12b is the classification accuracy of identifying “Beihang”, “A”, “B” and “C” written by five volunteers respectively;

[0049] Fig.13 Shown is a schematic diagram of the decoding process for a handwriting recognition and encryption system. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work. DETAILED DESCRIPTION

[0052] To make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be described in detail below. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other implementation methods obtained by ordinary technicians in this field without creative work belong to the scope of protection of the present invention.

[0053] The present invention provides a programmable performance flexible pressure sensor, which structure includes a flexible substrate, an electrode layer, a flexible ion layer, and a spacer layer. The planar structure of the flexible substrate is provided with two materials with gradient modulus, and the surface of the flexible substrate is provided with a gradient microstructure; the electrode layer is provided on the surface of the flexible substrate, and the surface of the flexible ion layer is provided with a gradient microstructure, and the flexible substrate and the flexible ion layer are surrounded by a frame, and the frame is at the same height as the surface microstructure, and the spacer layer is located between the flexible substrate and the flexible ion layer and the spacer layer is in contact with the frame of the flexible substrate and the flexible ion layer, and is assembled into a "sandwich" structure with interlocking microstructures through lamination.

[0054] As an optional implementation, the planar structure of the flexible substrate is provided with a hard TPU and a soft TPU with a gradient modulus, the difference in Young's modulus between the hard TPU and the soft TPU is 0.1 MPa-0.3 MPa, and the preferred modulus difference is 0.222 MPa; the thickness ratio of the hard TPU to the soft TPU is 1:1, 1:1.5, 1:2, and the preferred thickness ratio is 1:1.5.

[0055] As an optional embodiment, a gradient microstructure is provided on the surface of the flexible substrate, wherein the gradient microstructure is a mixed cylinder array composed of hemispheres and cylinders of different heights, and the material of the gradient microstructure is hard TPU; in the gradient microstructure, the radius of the hemisphere is 200 μm, and the heights of the cylinders are 0, 200 μm and 400 μm, respectively, and are named HC 0 HC 200 and HC 400 The center spacing between adjacent microstructures in the gradient microstructure array is 600 μm, and the total area of ​​the gradient microstructure array is 1 cm 2 .

[0056] The programmable performance feature is achieved by adjusting the modulus gradient and gradient microstructure density of the above-mentioned material, wherein the pressure sensor performance mainly refers to the sensitivity and pressure sensing range, and the gradient microstructure density mainly refers to HC 400 Density. In the gradient microstructure, when the HC 400 The density is 1-10 cm -2 When the density is low, when the HC 400 The density is 11-20 cm -2 , is medium density; when the HC 400 The density is 21-30 cm -2 , which is high density.

[0057] Table 1 - Distribution relationship between the number and density of gradient microstructures of different pressure sensors

[0058]

[0059] Inspired by the modulus gradient in human skin ( Figure 1 a), in the pressure sensor described in the present invention, the TPU modulus gradient in the electrode layer substrate is conducive to the transmission of pressure signals and improves the sensitivity of the sensor. Sensory neurons can sense slight stimulation, static pressure, dynamic pressure or high-frequency vibration through different types of corpuscles, such as hair follicles and Merkel corpuscles. In addition, the interlocking structure between the epidermis and the dermis further enhances the tactile sensitivity. Therefore, the introduction of gradient microstructures can respond to pressure stimuli of different ranges through microstructures of different heights ( Figure 1 b). The response amplitude of the sensor (ΔC / C) is calculated by combining the gradient modulus and microstructure. 0 ) can achieve a balance between sensitivity and detection range ( Figure 1 c).

[0060] Example 1

[0061] This embodiment provides a programmable pressure sensor, whose structure is as follows: Figure 1d, comprising a flexible substrate, an electrode layer, a flexible ion layer, and a spacer layer. The planar structure of the flexible substrate is provided with a hard TPU and a soft TPU with a gradient modulus, and the difference in Young's modulus between the hard TPU and the soft TPU is 0.222MPa; the thickness ratio of the hard TPU to the soft TPU is 1:1.5. A low-density gradient microstructure is provided on the surface of the flexible substrate, and the center spacing between adjacent microstructures in the array of the gradient microstructure is 600μm, and the total area of ​​the gradient microstructure array is 1cm 2 HC 0 The number is 266, HC 200 The number is 14, HC 400 The quantity is 9.

[0062] Furthermore, this embodiment provides a method for preparing a pressure sensor with programmable performance, and the process flow chart is as follows: Figure 2 As shown, the specific steps include:

[0063] (1) Preparation of PDMS soft template

[0064] Draw a 3D model in the 3D design software 3dmax, the 3D model contains HC 0 HC 200 and HC 400 Three kinds of microstructures, the center spacing between adjacent microstructures in the gradient microstructure array is 600 μm, and the total area of ​​the gradient microstructure array is 1 cm 2 HC 0 The number is 266, HC 200 The number is 14, HC 400 The number is 9. The 3D model is sliced ​​using slicing software, and a 3D printing mold is obtained by printing using a light-curing technology, wherein the material of the 3D printing mold is a photosensitive resin;

[0065] The 3D printing mold is sequentially immersed in deionized water and an organic solvent for ultrasonic cleaning to remove surface impurities, and then the mold is blown dry with nitrogen to obtain a pretreated 3D printing mold; the material of the 3D printing mold is a photosensitive resin, and the organic solvent is ethanol and / or acetone;

[0066] The PDMS solution is dripped on the pretreated 3D printing mold, and heated and cured at 80°C for 1 hour. After complete curing, the PDMS layer is peeled off to obtain a PDMS soft template for preparing an electrode layer or an ion layer.

[0067] (2) Preparation of electrode layer substrate

[0068] TPU and DMF were mixed thoroughly at a mass ratio of 1:5, and magnetically stirred at 80°C for 3 hours to obtain a TPU / DMF solution; the TPU / DMF solution was dripped into the PDMS soft template of the electrode layer and filled with the solution, and heated at 80°C for the first curing for 3 hours to form a hard TPU layer containing a microstructure, and the thickness of the hard TPU excluding the microstructure part was 200 μm; components A and B of Smooth On ClearFlex 50 were taken, mixed thoroughly at a mass ratio of 1:2, vacuum degassed for 5 minutes to remove bubbles in the solution, and dripped onto the back of the hard TPU layer with a filling thickness of 300 μm, and heated at 60°C for 18 hours, and cured for the second time to form a soft TPU layer, which was peeled off from the PDMS soft template to obtain the electrode layer substrate;

[0069] (3) Depositing a conductive film on the surface of the electrode layer substrate

[0070] First, the frame portion of the electrode layer substrate is covered with tape, and then Ti and Ag are deposited on the electrode layer substrate by magnetron sputtering at a working power of 25 W. The deposition time of the Ti film is 10 minutes, and the deposition time of the Ag film is 1 hour. After the deposition is completed, the tape is removed to obtain the electrode layer; wherein the Ag film is used as a conductive material, and the Ti film enhances the bonding force between TPU and Ag;

[0071] (4) Preparation of ion layer

[0072] Add 55wt% of ionic liquid [EMIM] to TPU / DMF solution + [TFSI] - , and then magnetically stirred at 80°C for 2 hours to obtain a TPU / DMF / IL ion solution; the ion solution was drop-coated on the PDMS mold of the ion layer, heated and cured at 80°C for 2 hours, and peeled off after cooling to room temperature to obtain an ion layer. The thickness of the ion material except the microstructure part is 500μm, which can be used as a sensing layer of the pressure sensor;

[0073] (5) Assembling and preparing pressure sensor

[0074] The polyimide tape is cut into a frame shape as a bonding layer between the electrode layer and the ion layer. Before assembly, the surface of the electrode layer is treated with argon plasma at 150 W for 20 seconds to clean the surface of the electrode layer. Then, the two electrode layers are bonded face to face and assembled on both sides of the ion layer to obtain the programmable performance pressure sensor.

[0075] Example 2

[0076] This embodiment provides a method for preparing a pressure sensor with programmable performance. The difference from the embodiment 1 is that: when designing the 3D model in step (1), the quantity distribution of the three microstructures is different. 0 The number is 264, HC 200 The quantity is 12, HC 400 The number is 13.

[0077] Example 3

[0078] This embodiment provides a method for preparing a pressure sensor with programmable performance. The difference from the embodiment 1 is that: when designing the 3D model in step (1), the quantity distribution of the three microstructures is different. 0 The number is 208, HC 200 The number is 56, HC 400 The quantity is 25.

[0079] Example 4

[0080] This embodiment provides a method for preparing a pressure sensor with programmable performance, which differs from Embodiment 1 only in that when preparing the flexible substrate in step (2), the soft TPU is different. In this embodiment, during the second thermal curing, the modulus of the soft TPU added is different, namely, Smooth On Clear Flex 30.

[0081] Experimental example

[0082] The ionization pressure sensing mechanism is the electric double layer effect (EDL), based on the Gouy-Chapman-Stern model. The double layer includes the Helmholtz layer and the diffusion layer. The available capacitance element C H and C D Indicates that Figure 3 a. The corresponding double layer capacitance C EDL It can be calculated as:

[0083]

[0084] where η A is the roughness ratio between the actual surface area and the ideal smooth surface area, is the thickness of the Helmholtz layer (d H ), dielectric constant (ε), ionic conductivity (σ) and ambient temperature (T). When the device unit area capacitance (UAC) remains unchanged, C EDL It is proportional to the contact area (A) between interfaces. By adjusting UAC through electrical optimization of ion gel, the capacitance change can be controlled, thereby affecting the pressure sensitivity of the sensor device ( Figure 3 b). The relative change in capacitance (ΔC / C 0) is nonlinear with respect to pressure (dark colors, Figure 3 b), by increasing UAC, the capacitance response amplitude can be increased, showing higher pressure sensitivity (light color, Figure 3 b, the upper curve).

[0085] In the programmable performance flexible pressure sensor of the present invention, due to the pre-designed spacer layer, the "sandwich structure" pressure sensor has no initial contact between the electrode and the ion layer. The capacitance of the pressure sensor (C) under external pressure (P) can be derived as:

[0086]

[0087] Where d is defined as the distance between the two electrodes. From the formula, we can see that the capacitive response of the ionization pressure sensor to different pressures is mainly determined by the electrical characteristics The pressure sensor of the present invention can realize the programming design of the pressure sensor performance by combining electrical optimization with gradient strategy.

[0088] In order to optimize the pressure sensing performance of the sensor, ion gels with IL mass fractions of 40%, 50%, 55%, and 60% were prepared, and the changes of σ of the four ion gels were studied according to the above formula. σ was characterized by electrochemical impedance spectroscopy ( Figure 4 a), the results show that when the IL content increases from 40wt% to 55wt%, σ increases gradually, reaching a maximum of 21.14×10 -3 S cm -1 ( Figure 4 b). The improvement of ionic conductivity is attributed to the increase in the number of carriers, which forms new ion transport paths. It should be noted that further increasing the IL content will lead to a decrease in conductivity. Therefore, since UAC is positively correlated with σ, in order to obtain higher sensitivity, 55wt% ion gel was selected as the sensitive material to prepare the ionized pressure sensor.

[0089] The comprehensive gradient strategy provides a method to program the sensing performance of ion-type pressure sensors. The performance characteristics of pressure sensors are usually evaluated using sensitivity and sensing range, where sensitivity is defined as

[0090]

[0091] C is the capacitance corresponding to the pressure change ΔP, C 0 is the initial capacitance when there is no pressure. When the microstructure is not introduced, the ionization pressure sensor is a planar structure. Due to the presence of two spacing layers, the initial state is a parallel plate capacitor. Its equivalent circuit diagram is as follows Figure 5 a. The two electrodes form a parallel plate capacitor (C 电极 ), where two EDL capacitors (C EDL1 , C EDL2 ) in series with a bulk resistor (R 体 ) on the ion electronic sensor. The total capacitance (C S ) can be expressed as:

[0092]

[0093] C 电极 In parallel with two double layers, its value is higher than C EDL Much smaller, so C S Mainly composed of C EDL Decide, and C EDL It mainly depends on the contact area between the electrode layer and the ion layer. Figure 5 b shows the Young's modulus of the material used in the sensor, which significantly affects the deformation of the material and thus changes the contact area under pressure. The planar gradient modulus substrate includes hard TPU and soft TPU, such as Figure 5 As shown in the inserted image of b, the thickness of the hard TPU is 200μm and the thickness of the soft TPU is 300μm. TPU / DMF with the highest Young's modulus is used as the hard TPU, and the Young's modulus is 0.31MPa. In order to simulate the modulus gradient of human skin, TPU with a Shore hardness (SH) of 30 or 50 is used as an optional material for soft TPU.

[0094] To test the relationship between the relative change in capacitance and pressure of the pressure sensor, the pressure sensor was fixed on the sample stage, and different pressures were applied by a linear motor. The loading pressure was measured and recorded using a digital force gauge, and the capacitance signal was recorded using an LCR meter. The capacitance test frequency was 1kHz and the voltage was 1V. Figure 5 c shows the relationship between the capacitance response and pressure of three pressure sensors whose soft TPU in the electrode layer is TPU (SH-30), TPU (SH-50) and TPU / DMF without gradient modulus. 1 is the sensitivity value of the pressure sensor response in the first linear region, from Figure 5 c It can be seen that the ionization pressure sensor based on TPU (SH-30) exhibits extremely high sensitivity (S 1 =37,347.98kPa- 1 ), the sensing range is 151.6 kPa. As the modulus gradient decreases, the sensitivity of the sensor based on TPU (SH-50) decreases (S 1=5100.41kPa-1), the sensing range is expanded to 239.2kPa. In addition, the gradient-free modulus sensor based on TPU / DMF has low sensitivity, but a wide sensing range, up to 1048kPa, which can meet a variety of application requirements.

[0095] On the basis of the above gradient modulus, microstructures are introduced into the planar structure based on TPU (SH-50) to further regulate the pressure sensing performance, such as Figure 6 As shown in a, the hybrid cylinder (HC) consists of a hemisphere with a radius of 200 μm and a cylinder with heights of 0, 200 μm, and 400 μm, respectively, which are called HC 0 HC 200 HC 400 The center-to-center spacing between adjacent microstructures in the microstructure array is 600 μm, and the total area is 1 cm 2 When no pressure is applied, the air gap caused by the spacer layer still exists. When pressure is applied, the microstructure electrode contacts both sides of the ion layer, resulting in a change in contact area ( Figure 6 b). At the same time, the top microstructure electrode and the ion layer microstructure contact each other to form an interlocking microstructure, further increasing the contact area variation. In this uniform microstructure, based on HC 0 The microstructured pressure sensor has the highest sensitivity (S 1 =3,392.21kPa -1 ), but the sensing range is the narrowest (338.9kPa). Based on HC 400 The microstructured sensor showed the widest sensing range (338.9 kPa), and S 1 =2,394.18kPa -1 Based on HC 200 The microstructured sensor can achieve a sensitivity of S 1 =2,641.73kPa -1 Therefore, compared with the above-mentioned planar structure pressure sensor, the hybrid cylinder can effectively expand the sensing range while maintaining high sensitivity.

[0096] On the basis of the above, HC 0 HC 200 and HC 400 The gradient microstructure is introduced to provide area changes under different pressures. Among them, the density of the mixed cylinder, especially HC 400 Density is crucial to the detection range of pressure sensors. The present invention designs a pressure sensor based on three types of HC: low, medium and high. 400 Microstructure density pressure sensor ( Figure 7 a) HC between electrode and ion layer 400 HC200 HC 0 Under pressure, the HC 400 and HC 200 When longitudinally compressed, HC 0 The contact interface is formed between Figure 7 b). Figure 7 c shows the performance of pressure sensors based on low, medium and high density microstructures, S 1 1,127.7 kPa respectively -1 , 1,400.49kPa -1 and 1,445.11 kPa -1 The pressure sensor based on medium-density microstructure has the widest detection range, up to 956.7 kPa. By further adjusting the gradient modulus difference and microstructure density, the sensing performance can be precisely controlled and the pressure sensor device structure for specific applications can be reversely designed.

[0097] The repeatability of the medium-density microstructure pressure sensing system was tested by fixing the pressure sensor on a sample stage and controlling the linear motor to load a pressure of 900 kPa for 1500 cycles. Figure 8 As shown, the response signal of the pressure sensor is stable and has high repeatability under constant pressure load.

[0098] The pressure resolution of the medium-density microstructure pressure sensing system was tested as follows: the pressure sensor was fixed, and a static pressure of 2.5 kPa was first applied to the device using a 3D printed model, and maintained for 4 seconds. Then, weights of different masses were added in sequence and maintained for 4 seconds. Fig. 9 As shown, the response signal of the pressure sensor is stable and can distinguish pressure changes of 500Pa.

[0099] Handwriting is widely used for identity verification as a unique behavioral biometric feature of an individual. Identifying an individual through handwriting (especially signature) is essential for maintaining security and privacy, but handwriting can be reproduced by highly imitating or using tools such as stencil paper. In order to solve this problem, a handwriting recognition and encryption system was developed based on the highly sensitive pressure sensor prepared in the present invention, which combined with a machine learning algorithm to capture individual handwriting characteristics such as grip strength, writing speed and stroke order to verify identity information.

[0100] like Fig.10As shown in the figure, in order to realize handwriting recognition, the sensor is integrated into the gel pen. When writing, the index finger presses the sensor to capture the handwriting features, and the real-time capacitance signal is recorded using an LCR meter. The number of capacitance peaks is related to the number of strokes, and continuous writing will reduce the number of peaks. Grip strength affects the median of capacitance peaks, while writing speed affects the total time and average half-peak width. Kurtosis and skewness provide information about the length of adjacent strokes and the point of pen placement. In addition, the symmetry index, average rising slope, and average falling slope are also considered. Five volunteers repeatedly wrote the word "Beihang" and the English letters "A", "B", and "C", and recorded the capacitance curves. By extracting the above features, a data set of 2000 samples was established for training and testing machine learning models, with a ratio of 8:2 between the training set and the test set. The support vector machine (SVM) model in machine learning was used for handwriting recognition. The training set was input into the SVM classifier to classify the data of five volunteers writing the same content or one volunteer writing different content. The trained SVM classifier was tested using 5-fold cross validation to quantify the recognition accuracy to evaluate the model performance. The trained models can classify unknown data, recognize handwritten information and authenticate identities.

[0101] Fig.11 The continuous capacitance signals of five volunteers writing "Beihang" five times each are displayed, and the repeatability of the handwriting and the consistency of the signal are good.

[0102] like Fig.12 As shown in a, for the classification model of the content written by the same volunteer, the classification accuracy of its test results is 97.5%. Fig.12 b provided five volunteers to write "Beihang", "A", "B", and "C" respectively, and the accuracy of classifying the identity of the writer was 99%, 100%, 100%, and 96%, achieving effective and universal recognition. In addition, handwriting recognition can also be extended to Chinese characters. Therefore, the smart pen equipped with a highly sensitive ion-type pressure sensor can effectively analyze handwriting features and provide identity authentication with the assistance of machine learning algorithms, which is suitable for scenarios such as finance, law enforcement, and security.

[0103] Based on this smart pen, a handwriting recognition and encryption system was developed. Fig.13The workflow of system decoding is demonstrated. The system records raw capacitance data in real time during the writing process, such as "Beihang", and saves it to the computer. After opening the user interface built by Matlab software, the user can select his or her identity and signature content, and read the raw data by selecting the file path. By clicking the "Feature Extraction" option, the raw data can be quickly processed. Finally, click the "Recognition" option to start access control. Access rights are only granted when the signature content and identity match. For example, when Volunteer 3 rewrote "Beihang" and selected "Sign 3", access was allowed. However, when Volunteer 6 copied Volunteer 3's handwriting with sulfuric acid paper, it could not be unlocked and was denied entry. The highly sensitive pressure sensor provides rich handwriting information, allowing the system to effectively distinguish between real and forged signatures. The handwriting recognition and encryption system based on ionized pressure sensors has application prospects in enhancing personal security and privacy.

[0104] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. A flexible pressure sensor with programmable performance, characterized in that: The structure includes a flexible substrate, an electrode layer, a flexible ion layer, and a spacer layer. Two materials with gradient moduli are provided in the planar structure of the flexible substrate, and a gradient microstructure is provided on the surface of the flexible substrate; the electrode layer is provided on the surface of the flexible substrate, and the surface of the flexible ion layer is provided with a gradient microstructure, and the flexible substrate and the flexible ion layer are surrounded by a frame, and the frame is at the same height as the surface microstructure; the spacer layer is located between the flexible substrate and the flexible ion layer, and the spacer layer is in contact with the frame of the flexible substrate and the flexible ion layer, and is assembled into a "sandwich" structure with interlocking microstructures through lamination.

2. The programmable performance flexible pressure sensor according to claim 1, characterized in that: The two materials with gradient modulus in the flexible substrate are hard TPU and soft TPU respectively; The difference in Young's modulus between the hard TPU and the soft TPU is 0.1 MPa-0.3 MPa, and the preferred modulus difference is 0.222 MPa; the thickness ratio of the hard TPU to the soft TPU is 1:1, 1:1.5, 1:2, and the preferred thickness ratio is 1:1.

5.

3. The programmable performance flexible pressure sensor according to claim 1, characterized in that: The gradient microstructure on the surface of the flexible ion layer is a mixed cylinder array composed of hemispheres and cylinders of different heights, and the material of the gradient microstructure is hard TPU; In the gradient microstructure, the radius of the hemisphere is 200 μm, and the height of the cylinder is 0, 200 μm, and 400 μm, respectively, and they are named HC 0 HC 200 and HC 400 .

4. The programmable performance flexible pressure sensor according to claim 1, characterized in that: In the gradient microstructure, when the HC 400 The density is 1-10 cm -2 When the density is low, when the HC 400 The density is 11-20 cm -2 , is medium density; when the HC 400 The density is 21-30 cm -2 , which is high density.

5. The method for preparing a programmable performance flexible pressure sensor according to any one of claims 1 to 4, characterized in that: The steps include: (1) Design 3D printing mold Draw a 3D model in a 3D design software, slice the 3D model using slicing software, and print using photo-curing technology to obtain a 3D printed mold; (2) Preparation of PDMS soft template A PDMS solution is dripped onto the 3D printing mold, and after heating and curing, the PDMS layer is peeled off to obtain a PDMS soft template for preparing an electrode layer or an ion layer; (3) Preparation of flexible substrate Dispersing TPU in an organic solvent and mixing it thoroughly to obtain a TPU solution; dripping the TPU solution on a PDMS soft template for preparing an electrode layer, heating it for a first curing, and forming a hard TPU layer; based on the coffee ring effect produced by the evaporation of the solvent in the solution, dripping a soft TPU solution on the concave part of the back side of the hard TPU layer, heating it for a second curing to form a soft TPU layer, and peeling it off from the PDMS template to obtain a flexible substrate; (4) Depositing a conductive film on the surface of the electrode layer substrate First, the frame portion of the electrode layer substrate is covered with a tape, and then a conductive film is deposited on the electrode layer substrate. After completion, the tape is removed to obtain the electrode layer; (5) Preparation of ion layer Adding ionic liquid to the hard TPU solution, heating and fully mixing to obtain an ionic solution; drop-coating the ionic solution on the PDMS mold of the ionic layer, heating and curing, and peeling off after cooling to room temperature to obtain an ionic layer; (6) Assembling and preparing pressure sensor The tape is cut into a frame shape to serve as a spacer layer between the electrode layer and the ion layer, and then two electrode layers are bonded and assembled on both sides of the ion layer to obtain the programmable performance pressure sensor.

6. The method for preparing a programmable performance pressure sensor according to claim 5, characterized in that: In step (3), the TPU solution used in the first thermal curing is prepared by mixing TPU particles with an organic solvent, so that the cavity filled in the second thermal curing is formed by evaporation of the solvent, and the organic solvent is N,N-dimethylformamide or tetrahydrofuran; The mass ratio of the TPU to the organic solvent is in the range of 1:3-1:10, with a preferred ratio of 1:

5.

7. The method for preparing a programmable performance pressure sensor according to claim 5, characterized in that: In step (5), different weights of ionic liquids are added to the TPU solution, with a weight ratio ranging from 30-60 wt%, preferably 55 wt%; the ionic liquid is an aprotic ionic liquid, preferably [EMIM] + [TFSI] - .

8. The method for preparing a programmable performance pressure sensor according to claim 5, characterized in that: In step (6), the spacer layer prevents the electrode layer microstructure and the ion layer microstructure from contacting each other, and the thickness of the spacer layer is in the range of 20-200 μm, preferably 50 μm.

9. Application of the programmable performance pressure sensor according to any one of claims 1 to 4 or the programmable performance pressure sensor prepared by the method according to any one of claims 5 to 8 in intelligent robots and flexible wearable devices.

10. The use according to claim 9, characterized in that: The application is in the field of handwriting recognition, and the sensitivity of the programmable pressure sensor is greater than 100 kPa -1 , can withstand more than 1000 dynamic cycle pressures, and the static pressure resolution is less than 1kPa.

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