Conductive particle reinforced elastomer self-sensing driver and preparation method thereof
By introducing conductive particles into the liquid crystal elastomer to form a conductive network, and using Joule heating to drive liquid crystal deformation combined with neural network control, the problems of uneven heating, slow response, high energy consumption and poor stability of liquid crystal actuators are solved, and efficient and stable driving and self-sensing functions are realized.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-12-08
- Publication Date
- 2026-03-10
AI Technical Summary
Existing liquid crystal actuators suffer from problems such as uneven heating, slow response speed, high energy consumption, interface stress concentration, complex manufacturing process, and poor stability, making it difficult to achieve mass production and long-term stable use.
A self-sensing actuator for liquid crystal elastomer reinforced with conductive particles is used. By forming a continuous or semi-continuous conductive network in the liquid crystal elastomer matrix, the conductive particles generate Joule heat to drive the deformation of the liquid crystal elastomer, and the deformation is self-sensing through the change in resistance. Combined with neural network, intelligent control is achieved.
It achieves uniform heating, rapid response, and low-energy drive of liquid crystal elastomers, avoids interface stress concentration, simplifies system structure, is suitable for applications such as facial expression robots that require skin contact, and supports adaptive control and online learning.
Smart Images
Figure CN121628271A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of soft robot actuation and smart materials technology, specifically to a self-sensing actuator for an elastomer reinforced with conductive particles and its preparation method. Background Technology
[0002] Liquid crystal elastomers, as a type of intelligent polymer material that combines polymer elasticity and liquid crystal anisotropy, can produce large-scale reversible deformation under external stimuli such as heat, light, and electricity. They have been widely studied and used in fields such as soft robots, artificial muscles, and flexible actuators. Their working principle is to trigger the transformation of liquid crystal units from an ordered nematic phase to a disordered isotropic phase through external stimuli, thereby producing macroscopic deformations such as contraction or bending.
[0003] Traditional LCE actuators mainly rely on the following driving methods: (1) External heating: LCE is heated by external heat sources such as hot air and light, but there are problems such as uneven heating, slow response speed and low energy efficiency. Although light can achieve faster heating, the overall power consumption is extremely high and the energy consumption is large. (2) Resistance wire winding: Metal resistance wire is embedded on or inside the LCE for Joule heating. Although this method can achieve electronic control, it has the following defects: The stiffness of the metal wire is much higher than that of the LCE substrate, which leads to stress concentration at the interface. Long-term cyclic use is prone to peeling or breakage. Local overheating is serious. The temperature near the resistance wire is much higher than the expected working temperature, which may lead to LCE degradation. The preparation process is complicated and it is difficult to achieve mass production. (3) Liquid metal microchannel: Liquid metal microchannel is constructed inside the LCE and the high conductivity of liquid metal is used for heating. However, this method has the following problems: The microchannel preparation process is complicated and the yield is low. Long-term deformation may lead to channel rupture or leakage and poor stability. Liquid metal has a high density, which increases the weight of the actuator. Summary of the Invention
[0004] To address the shortcomings of existing technologies, this invention provides a conductive particle-reinforced elastomer self-sensing actuator and its preparation method, thus solving the problems mentioned in the background section.
[0005] To achieve the above objectives, the present invention is implemented through the following technical solution: a self-sensing actuator for an elastomer reinforced with conductive particles, comprising a liquid crystal elastomer matrix and conductive particles, wherein the conductive particles form a continuous or semi-continuous conductive network in the matrix, and when energized, generate Joule heat to drive the liquid crystal elastomer to undergo reversible contraction, while the deformation of the liquid crystal elastomer causes a measurable change in the resistance of the conductive network, thereby realizing the deformation self-sensing function.
[0006] Preferably, the liquid crystal elastomer matrix has thermal deformation capability and a nematic-isotropic transition temperature of 60-100°C. The conductive particles are uniformly dispersed in the liquid crystal elastomer matrix with a mass fraction of 0.5%-5.0%. The conductive particles are selected from at least one of carbon nanotubes and graphene, or a composite of both.
[0007] Preferably, the conductive particles are multi-walled carbon nanotubes with a diameter of 10-50 nm, a length of 1-20 μm, and a mass fraction of 1.0%-3.0%.
[0008] Preferably, the conductive particles are graphene or graphene oxide, with a sheet size of 0.5-50 μm, a thickness of 1-10 nm, and a mass fraction of 1.5%-4.0%.
[0009] Preferably, the liquid crystal elastomer matrix undergoes uniaxial stretching and orientation treatment during the preparation process, so that the liquid crystal units and conductive particles are oriented along the stretching direction.
[0010] Preferably, the method for preparing the conductive particle-reinforced elastomer self-sensing actuator specifically includes the following steps: S1. Disperse the conductive particles in an organic solvent, sonicate for 0.5-2 hours to prepare a uniform dispersion, and perform vacuum degassing if necessary; S2. Add liquid crystal monomer, crosslinking agent, chain extender and initiator, and mix thoroughly; S3. Transfer the mixed solution to the mold and prepolymerize at room temperature or with heat for 12-48 hours; S4. Stretch uniaxially to 150%-250% strain, maintain the stretched state and then perform UV curing or thermosetting. S5. Demolding, cutting, and coating both ends with flexible electrodes to obtain a conductive particle-enhanced LCE actuator.
[0011] Preferably, the deformation control method of the actuator specifically includes the following steps: S1. Apply a PWM drive signal to power on the driver and cause it to heat up and contract. S2. The resistance value R(t) and its rate of change dR / dt at both ends of the real-time sampling driver are preferably measured using a four-wire method to avoid the influence of contact resistance, with a sampling rate of 21kHz. S3. Input the resistance value and historical sequence into the pre-trained neural network model and output the estimated strain Eest(t); S4. Calculate the strain error AE = Etarget - Eest; S5. The PWM duty cycle is adjusted according to the error by the PID controller, and the loop is iterated until the target strain is reached.
[0012] Preferably, the neural network model is an LSTM or GRU network, and the training data covers the "PWM-resistance-strain" time series at different temperatures and loading rates to compensate for the nonlinear and hysteresis characteristics of the material.
[0013] This invention provides a conductive particle-reinforced elastomer self-sensing actuator and its preparation method. Compared with the prior art, it has the following advantages: This conductive particle-reinforced elastomer self-sensing actuator and its preparation method form a continuous or semi-continuous conductive network in a matrix. When energized, Joule heating is generated, driving the liquid crystal elastomer to reversibly contract. Simultaneously, the deformation of the liquid crystal elastomer causes a measurable change in the resistance of the conductive network, thereby achieving a deformation self-sensing function. The liquid crystal elastomer matrix has thermally induced deformation capability, with a nematic-isotropic transition temperature of 60-100℃. The conductive particles are uniformly dispersed in the liquid crystal elastomer matrix, with a mass fraction of 0.5%-5.0%. The conductive particles are selected from at least one of carbon nanotubes and graphene, or a combination of both. The conductive particles simultaneously function as heating and sensing elements, eliminating the need for external temperature or displacement sensors and significantly simplifying the system structure. Resistance feedback directly corresponds to the deformation state, bypassing thermal conduction delay. Combined with intelligent compensation via neural networks, the conductive particles are evenly distributed, avoiding localized hot spots in the resistance wire. The conductive particles are physically / chemically bonded to the LCE matrix without a mechanical interface. Compared to embedded metal wires, the conductive particles have a relatively smaller impact on the stiffness of the LCE, maintaining excellent flexibility. This makes it suitable for applications requiring skin-fitting, such as facial expressions in robots. The resistance-strain relationship provides an ideal interface for digital control, facilitating the integration of advanced algorithms such as neural networks and fuzzy control, and supporting adaptive control and online learning. Attached Figure Description
[0014] Figure 1 This is a schematic diagram of the conductive particle-enhanced LCE driver structure of the present invention; Figure 2 This is a block diagram of the neural network control system of the present invention; Figure 3 Charts showing experimental data for this invention Figure 1 ; Figure 4 Charts showing experimental data for this invention Figure 2 . Detailed Implementation
[0015] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.
[0016] Please see Figure 1-4 The present invention provides three technical solutions: a self-sensing actuator for an elastomer reinforced with conductive particles, comprising a liquid crystal elastomer matrix and conductive particles, wherein the conductive particles form a continuous or semi-continuous conductive network in the matrix, and when energized, generate Joule heat to drive the liquid crystal elastomer to reversibly contract, and at the same time, the deformation of the liquid crystal elastomer causes a measurable change in the resistance of the conductive network, thereby realizing the deformation self-sensing function.
[0017] In this invention, the liquid crystal elastomer matrix has thermal deformation capability and a nematic-isotropic transition temperature of 60-100°C. The conductive particles are uniformly dispersed in the liquid crystal elastomer matrix with a mass fraction of 0.5%-5.0%. The conductive particles are selected from at least one of carbon nanotubes and graphene, or a combination of both.
[0018] In this invention, the conductive particles are multi-walled carbon nanotubes with a diameter of 10-50 nm, a length of 1-20 μm, and a mass fraction of 1.0%-3.0%.
[0019] In this invention, the conductive particles are graphene or graphene oxide, with a sheet size of 0.5-50 μm, a thickness of 1-10 nm, and a mass fraction of 1.5%-4.0%.
[0020] In this invention, the liquid crystal elastomer matrix undergoes uniaxial stretching and orientation treatment during the preparation process, so that the liquid crystal units and conductive particles are oriented along the stretching direction.
[0021] This invention also discloses a method for preparing a self-sensing actuator of an elastomer reinforced with conductive particles, specifically including the following embodiments: Example 1: Carbon nanotube-enhanced LCE actuator (1) Material preparation Multi-walled carbon nanotubes (MWCNTs, 2.0 wt%) were dispersed in tetrahydrofuran (THF) and sonicated for 1 hour (power approximately 300 W) to achieve a dispersion solid content of approximately 15-25 wt%. Vacuum degassing was performed if necessary to improve repeatability. Add liquid crystal monomer RM257 (80 mol%), crosslinking agent PETMP (20 mol%), and photoinitiator rgacure819 (0.5 wt%), and stir magnetically for 2 hours; Pour the mixed solution into a glass mold (0.5 mm thick) and prepolymerize at room temperature for 24 hours under nitrogen protection; Uniaxial stretching to 200% strain, followed by UV curing (365nm, 10mW / cm, 30 minutes) under tension to fix liquid crystal alignment and CNT network orientation; Demolding and cutting into strips (30mm long, 5mm wide, and 0.5mm thick), with silver paste electrodes (5mm long) coated at both ends; (2) Performance testing (Test environment: room temperature 23+2°C, relative humidity 40-60%, sample size n=5) Electrical conductivity: Resistance is approximately 50 Ω at room temperature, and conductivity is approximately 120 S / m; Driving performance: When a 3V voltage (approximately 60mA current) is applied, the material temperature rises to 80°C within 0.3 seconds, shrinks by 30% along its long axis, and recovers its original length within 0.8 seconds at ambient temperature after power is cut off; Self-sensing: During the contraction process, the resistance decreases from 50Ω to 30Ω (a decrease of 40%), and the change in resistance is monotonically related to the strain; a four-wire measurement method is used, with a sampling rate of 1kHz and a measurement delay of <10ms; Cycle life: After 100,000 load-unload cycles (frequency 0.5H), the shrinkage rate decreases by 5%, and the resistance drift is <10%. (3) Application of soft robots By installing a strip-shaped actuator on the soft robot's actuator, rapid contraction is achieved when powered on. Through electro-anode feedback combined with neural network control, the contraction amplitude can be precisely controlled (error <2% strain), and the response time is <0.5 seconds.
[0022] Example 2: Graphene-enhanced LCE driver (1) Material preparation The preparation process was the same as in Example 1, using graphene oxide sheets (2.5 wt%), which were surface functionalized and then mixed with LCE monomers. (2) Performance test (test strips are the same as in Example 1) Electrical conductivity: room temperature resistance is approximately 40 Ω, and conductivity is approximately 150 S / m; Driving performance: When a 2.5V voltage is applied, the temperature rises to 75°C within 0.4 seconds, and the shrinkage rate is 32%; Self-sensing: Resistance change range 45% (40-22), high sensitivity; Mechanical properties: In tensile testing, the breaking strength is approximately 18 MPa (about 3.6 times higher than the approximately 5 MPa of pure LCE), meeting the requirements of soft robot actuators.
[0023] Furthermore, any content not described in detail in this specification is existing technology known to those skilled in the art.
[0024] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.
[0025] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.
Claims
1. An electrically conductive particle-reinforced elastomeric self-sensing actuator comprising a liquid crystal elastomer matrix and electrically conductive particles, characterized in that: The conductive particles form a continuous or semi-continuous conductive network in the matrix, and Joule heat is generated when electricity is applied to drive the reversible shrinkage of the liquid crystal elastomer, and the deformation of the liquid crystal elastomer causes a measurable change in the resistance of the conductive network, thereby realizing the deformation self-sensing function.
2. An electrically conductive particle-reinforced elastomeric self-sensing actuator according to claim 1, characterized in that: The liquid crystal elastomer matrix has a thermally induced deformation capability, and the nematic-isotropic transition temperature is 60-100℃, the conductive particles are uniformly dispersed in the liquid crystal elastomer matrix, and the mass fraction is 0.5%-5.0%, the conductive particles are selected from at least one of carbon nanotubes, graphene, or a composite of the two.
3. An electrically conductive particle reinforced elastomeric self-sensing actuator according to claim 1, characterized in that: The conductive particles are multi-walled carbon nanotubes with a diameter of 10-50nm and a length of 1-20μm, and the mass fraction is 1.0%-3.0%.
4. The conductive particle enhanced elastomer self-sensing actuator of claim 1, wherein: The conductive particles are graphene or graphene oxide with a sheet size of 0.5-50μm and a thickness of 1-10nm, and the mass fraction is 1.5%-4.0%.
5. The conductive particle enhanced elastomer self-sensing actuator of claim 1, wherein: The liquid crystal elastomer matrix is subjected to uniaxial stretching orientation treatment during preparation, so that the liquid crystal units and the conductive particles are oriented and arranged along the stretching direction.
6. The conductive particle reinforced elastomer self-sensing driver according to any one of claims 1-5, the preparation method specifically comprising the following steps: S1, dispersing the conductive particles in an organic solvent, ultrasonic treatment for 0.5-2 hours, preparing a uniform dispersion liquid, and vacuum degassing if necessary; S2, adding liquid crystal monomers, crosslinking agents, chain extenders and initiators, and mixing thoroughly; S3, transferring the mixed solution to a mold, pre-polymerizing at room temperature or heating for 12-48 hours; S4, uniaxial stretching to 150%-250% strain, and maintaining the stretched state for ultraviolet curing or thermal curing; S5, demolding, cutting, and coating flexible electrodes on both ends to obtain a conductive particle reinforced LCE driver.
7. An electrically conductive particle reinforced elastomeric self-sensing actuator according to claim 1, wherein: The deformation control method of the driver specifically comprises the following steps: S1, applying a PWM driving signal to make the driver heat and shrink when electrified; S2, real-time sampling of the resistance value R(t) and its change rate dR / dt at both ends of the driver, preferably using four-wire measurement method to avoid the influence of contact resistance, and the sampling rate is 21kHz; S3, inputting the resistance value and historical sequence into a pre-trained neural network model to output an estimated strain Eest(t); S4, calculating the strain error AE=Etarget-Eest; S5, adjusting the PWM duty cycle according to the error through a PID controller, and iterating in a closed loop until the target strain is reached.
8. An electrically conductive particle-reinforced elastomeric self-sensing actuator according to claim 7, characterized in that: The neural network model is an LSTM or GRU network, and the training data covers different temperatures, different loading rates, and "PWM-resistance-strain" time series, which is used to compensate for the nonlinearity and hysteresis characteristics of the material.