Memory Properties Research Articles

Interface engineering in ZnO/CdO hybrid nanocomposites to enhanced resistive switching memory for neuromorphic computing

Resistive random-access memory (RRAMs) has attracted significant interest for their potential applications in embedded storage and neuromorphic computing. Materials based on metal chalcogenides have emerged as promising candidates for the fulfilment of these requirements. Due to its ability to manipulate electronic states and control trap states through controlled compositional dynamics, metal chalcogenide RRAM has excellent non-volatile resistive memory properties. In the present we have synthesized ZnO-CdO hybrid nanocomposite by using hydrothermal method as an active layer. The Ag/C15ZO/Pt hybrid nanocomposite structure memristors showed electrical properties similar to biological synapses. The device exhibited remarkably stable resistive switching properties that have a low SET/RESET (0.41/−0.2) voltage, a high RON/OFF ratio of approximately 105, a high retention stability, excellent endurance reliability up to 104 cycles and multilevel device storage performance by controlling the compliance current. Furthermore, they exhibited an impressive performance in terms of emulating biological synaptic functions, which include long-term potentiation (LTP), long-term depression (LTD), and paired-pulse facilitation (PPF), via the continuous modulation of conductance. The hybrid nanocomposite memristors notably achieved an impressive recognition accuracy of up to 92.6 % for handwritten digit recognition under artificial neural network (ANN). This study shows that hybrid-nanocomposite memristor performance could lead to efficient future neuromorphic architectures.

Dual-vat photopolymerization 3D printing of vitrimers

Herein, the processing of multi-material vitrimers with heterogenous properties along their x-,y- and z-axis is demonstrated using dual-vat digital light processing (DLP) 3D printing. The printer is based on a conventional vat exchange set-up containing two vats and a cleaning station, which are positioned on a linearly moving platform, and are exchanged automatically during the layer-by-layer build-up of the object. Two thiol-acrylate resins are selected bearing ample -OH and ester moieties, which undergo thermo-activated transesterification in the presence of an organic phosphate as catalyst. Based on the functionality of the acrylate monomers, printing of multi-material structures with flexible (σ = 1.1 MPa, ε = 14.0%) and rigid (σ = 33.3 MPa, ε = 4.4%) domains is feasible. A good interlayer adhesion between the soft and hard domains is evidenced by uniaxial tensile tests, whilst optical microscopy is used to study the interface. Dynamic mechanical analysis further shows that the stiffness of the photopolymers varies over several orders of magnitude (E’23 °C = 209 kPa - 507 MPa). Jacobs working curves of the two different resins reveal comparable printing parameters at higher exposure doses, which do not only facilitate the printing of multiple materials between individual layers (z-heterogeneity) but also within the same layer (x,y-heterogeneity). This is confirmed by the printing of a multi-material gripper as “proof of concept” demonstrator, which contains soft inner teeth and a stiff outer core. Gripping and releasing of objects is shown by exploiting the glass transition-based shape memory properties of the stiff domains. Moreover, due to the dynamic nature of the covalent bonds, damages inserted into the gripper are intrinsically repairable due to a thermo-activated macroscopic reflow. Shape memory experiments further confirmed that the multi-material gripper fully regains its function after the thermal mending process. Thus, the results clearly show that objects with a high degree of functionality can be realized by combining multi-material 3D printing with the chemistry of vitrimers. This is of particular interest for future soft robotic applications and for mimicking biological composite structures.

Ассоциативные представления о памяти в русской и китайской лингвокультурах

The article examines the features of conceptualization of the phenomenon of memory in the Russian and Chinese languages. By comparing the linguistic representations of memory in two cultures, similar and nationally specific images and connotations are revealed. The authors come to the conclusion that the ideas of Russians and Chinese about the connection between memory and thinking, past and future, about the dialectical nature of the phenomenon of memory are universal. However, associative interpretations of the properties of memory are realized in Russian and Chinese linguistic cultures in different images

Shape recovery behavior of polylactic acid/thermoplastic polyurethane polymer blends

Polylactic acid (PLA) and thermoplastic polyurethane (TPU)-based blends were prepared by twin-screw extruder and conventional injection molding (CIM) machine at various weight ratios of PLA/TPU, that is, 90/10, 80/20, and 70/30. The prepared blends were investigated for their shape memory properties by performing shape deformation and recovery experiments at various temperatures and bending angles. During the deformation process, TPU acted as a toughening agent that prevented the PLA/TPU blends from distortion. Hence, the temporary shape can be kept as the internal stress was stored in the blends. On heating above the glass transition (Tg) temperature of PLA, the deformed parts recovered their original shapes quickly along with the release of the stress. The prepared blends were found to exhibit significant shape memory properties at the TPU content of 10 wt%. The recovery rate was found to reach up to approximately 95%. Such shape memory polymers found fascinating applications in aerospace engineering, engineering plastics, biomedical implant materials, and textiles.

Functionalization of Violet Phosphorus Quantum Dots with Azo-Containing Star-Shape Polymer for Optically Controllable Memory

Quantum dots (QDs) are emerging as promising candidates for innovative memristive materials, owing to their distinct surface, quantum size, and edge effects. Recent research has focused on tailoring QDs with specific organic molecules to fine-tune charge transfer states between the host and grafted species, as well as enhancing their dispersibility and processability. Violet phosphorus (VP), a newly discovered two-dimensional phosphorus allotrope, offers excellent carrier dynamics, predictable modifiability, and superior oxidation resistance, making it a promising contender in this domain. In this study, we synthesized a rich azobenzene-containing star-shaped polymer diazonium salt (AzoSPD) to functionalize violet phosphorus quantum dots (VPQDs), with the dual objectives of enhancing organic dispersibility and introducing photo-switching capabilities. The synthesized AzoSPD–VPQDs exhibit intramolecular charge transfer characteristics under electrical stimuli of ambient conditions, displaying significant non-volatile rewriteable memory properties and a substantial switching ratio exceeding 2 × 103. Furthermore, the high resistance state (HRS) current can be enhanced by nearly 40 times under 465 nm illumination, enabling optoelectronic information sensing and storage within a single device. This work not only provides insights into enhancing the optoelectronic properties of QDs through functional organic molecular modification but also represents a pioneering exploration of the potential applications of VPQDs in novel memristors.

High Performance Shape Memory Epoxy Syntactic Foam Composites as Lost Circulation Material in Deep Drilling

AbstractLost circulation is a critical issue in drilling. This work reports the investigations on shape memory epoxy polymers (SMEPs) with high switching temperatures, and good thermal, mechanical and shape memory properties for plugging in deep drilling. The SMEPs are produced by curing diglycidyl ether of bisphenol A (DGEBA) and castor oil glycidyl ether (COGE) with a flexible poly(oxypropylene) diamine (D230) and a rigid 4, 4′ ‐diaminodiphenyl methane (DDM). Increasing D230 contents or using COGE, glass transition temperature (Tg, 93–163 °C, DMA analyses) and crosslink density of SMEPs are reduced, shape recovery rate is increased, while tensile strength, elongation at break and impact strength are improved. Further, shape memory epoxy syntactic foams (SMEFs) with high‐temperature adhesion are developed by introducing polymer microspheres into SMEPs. The SMEFs have compression ratio of 52–58%, and expansion ratio of 100% by adding 2% wt. microspheres. They retain the Tg and thermal stability of SMEPs, and show a delayed, slower recovery than SMEPs. Long fracture plugging experiments exhibit that a pressure‐bearing capability of 8.99–9.81 MPa is achieved with SMEFs in sealing fracture slots at a high temperature.

Organic-inorganic polyureas with benzyl hindered urea bonds and POSS cages in the main chains: Synthesis, shape memory and reprocessing properties

The organic–inorganic polyureas with benzyl hindered urea bonds (HUBs) and POSS cages as the main-chain structural units were synthesized via the polyaddition of 4,4′-diphenylmethane diisocyanate with α,ω-dibenzyl diamines including α,ω-dibenzylamino-terminated poly(ethylene oxide), N,N’-dibenzylhexamethylene-1,6-diamine and 3,13-dibenzylaminoanilino double decker silsesquioxane. The organic–inorganic PUreas were microphase-separated, in which the POSS cages were self-assembled into the microdomains via POSS-POSS interactions. The POSS microdomains acted as the physical crosslinking points and thus the organic–inorganic PUreas behaved as the crosslinked elastomers; the physical crosslinking remained thermally stable even at elevated temperature as high as 200 °C. In spite of the high thermal stability, the networks can be reprocessed with the plasticity in solids via the dynamic exchange of benzyl HUBs. Due also to the physical crosslinking, the organic–inorganic PUreas possessed the excellent shape memory properties; the original shapes were re-configurable by taking advantage of the benzyl HUBs in the main chains.

Self-Defensive Antimicrobial Shape Memory Polyurethanes with Honey-Based Compounds.

Infection treatment plays a crucial role in aiding the body in wound healing. To that end, we developed a library of antimicrobial polymers based on segmented shape memory polyurethanes with nondrug-based antimicrobials (i.e., honey-based phenolic acids (PAs)) using both chemical and physical incorporation approaches. The antimicrobial shape memory polymers (SMPs) have high transition temperatures (>55 °C) to enable maintenance of temporary, programmed shapes in physiological conditions unless a specific external stimulus is present. Polymers showed tunable mechanical and shape memory properties by changing the ratio, chemistry, and incorporation method of PAs. Cytocompatible (∼100% cell viability) synthesized polymers inhibited growth rates of Staphylococcus aureus (∼100% with physically incorporated PAs and >80% with chemically incorporated PAs) and Escherichia coli (∼100% for samples with cinnamic acid (physical and chemical)). Crystal violet assays showed that all formulations inhibit biofilm formation in surrounding solutions, and chemically incorporated samples showed surface antibiofilm properties with S. aureus. Molecular dynamics simulations confirm that PAs have higher levels of interactions with S. aureus cell membranes than E. coli. Long-term antimicrobial properties were measured after storage of the sample in aqueous conditions; the polymers retained their antimicrobial properties against E. coli after up to 20 days. As a proof of concept, magnetic particles were incorporated into the polymer to trigger user-defined shape recovery by applying an external magnetic field. Shape recovery disrupted preformed S. aureus biofilms on polymer surfaces. This antimicrobial biomaterial platform could enable user- or environmentally controlled shape change and/or antimicrobial release to enhance infection treatment efforts.

3D printed epoxy/acrylate hybrid polymers with excellent mechanical and shape memory properties via UV and thermal cationic dual-curing mechanism

Fabricating shape memory polymers with complex configurations and high mechanical performance for application as structural materials remains a significant challenge. Here, an epoxy/acrylate hybrid material with remarkable mechanical properties and excellent shape memory performance is fabricated using vat photopolymerization printing technology through photo-thermal dual curing mechanism. The UV-curing component comprises of a polyurethane acrylate and a rigid active diluent to ensure robustness and toughness. Meanwhile, the thermal curing epoxy constituent utilizes a benzylanilinium salt as the cationic initiator for its storage and handling stability throughout the UV curing stage. The interpenetrating polymer network and the unique two-phase molecular microstructure impart the hybrid material with good shape memory and high mechanical properties. Many complex objects are fabricated using vat photopolymerization printing technology and employed in self-deploying antennas, intelligent mechanical grippers, and switches. Combining with excellent printability, mechanical and shape memory properties, the hybrid materials have great potential applications in various fields.

The Framework and Memristive Circuit Design for Multisensory Mutual Associative Memory Networks.

In this work, we propose a multisensory mutual associative memory networks framework and memristive circuit to mimic the ability of the biological brain to make associations of information received simultaneously. The circuit inspired by neural mechanisms of associative memory cells mainly consists of three modules: 1) the storage neurons module, which encodes external multimodal information into the firing rate of spikes; 2) the synapse module, which uses the nonvolatility memristor to achieve weight adjustment and associative learning; and 3) the retrieval neuron module, which feeds the retrieval signal output from each sensory pathway to other sensory pathways, so that achieve mutual association and retrieval between multiple modalities. Different from other one-to-one or many-to-one unidirectional associative memory work, this circuit achieves bidirectional association from multiple modalities to multiple modalities. In addition, we simulate the acquisition, extinction, recovery, transmission, and consolidation properties of associative memory. The circuit is applied to cross-modal association of image and audio recognition results, and episodic memory is simulated, where multiple images in a scene are intramodal associated. With power and area analysis, the circuit is validated as hardware-friendly. Further research to extend this work into large-scale associative memory networks, combined with visual-auditory-tactile-gustatory sensory sensors, is promising for application in intelligent robotic platforms to facilitate the development of neuromorphic systems and brain-like intelligence.

Shape memory alloys effects on the multi-story structures under extreme loading conditions

The use of intelligent materials with particular behavioral traits has grown significantly in recent years. The most common materials that may revert to their original shape after deformation due to stress or heat change are shape memory alloys (SMAs). Shape memory alloy (SMA) differs from conventional materials due to its shape memory property and hyperplastic nature. This research aims to explore the use of SMAs as a reinforcement to improve the performance of multi-story steel plate shear walls (SPSW) under explosive loading. The finite element (FE) software "ABAQUS" is utilized to simulate the models, and the results of the numerical modeling are validated against experimental research from the literature. The study investigates the impact of different parameters related to the explosion on the behavior of the reinforced multi story SPSW, including the amount of explosive material and the location of the explosion. The findings reveal that the minimum amount of explosive material required to cause rupture in a four-story SPSW reinforced by SMA, positioned at a distance of 1 m from the structure, is equivalent to 40 kg of TNT. Additionally, the minimum distance required for element rupture in the four-story SPSW, reinforced by SMA strips, is found to be 0.5 m. As the explosion height increases, the amount of out-of-plane base shear decreases. Furthermore, an interesting observation is that as the explosion height increases, the displacement of the infill plate also increases, leading to beam failure and, ultimately, infill plate failure. Niti SMA exhibited superior blast resistance compared to Iron-based SMA, showcasing its potential for enhancing structural resilience.

Reconfigurable Physical Reservoir Enabled by Polarization of Ferroelectric Polymer P(VDF–TrFE) and Interface Charge‐Trapping/Detrapping in Dual‐Gate IGZO Transistor

AbstractNeuromorphic computers promise to enhance computing efficiency by eliminating conventional von Neumann architecture bottlenecks. Bio‐inspired artificial neural networks, such as feedforward neural networks and reservoir computing (RC), face challenges due to the unique memristor requirements. In this study, a dual‐gate ferroelectric polymer P(VDF–TrFE)‐coupled thin film transistor (DG–TFT) with an IGZO channel is presented. It yields complementary short‐ and long‐term memory functionalities are derived from the charge‐trapping/detrapping process at the IGZO‐SiO2 dielectric interface and ferroelectric polarization. These memory functionalities can be switched using different gated modes to meet the requirements of the reservoir and readout layers in RC. The bottom‐gated mode (BG‐mode) exhibits short‐term memory effects and nonlinear dynamics, whereas the top‐gated mode (TG‐mode) displays improved long‐term memory characteristics. To evaluate the long‐term memory properties, Python is used for pattern recognition. For the nonlinear dynamics and short‐term memory response of the BG‐mode, the DG–TFT is employed as a reservoir layer to handle various temporal tasks. Notably, the polarization level of the ferroelectric layer is coupled to improve the richness of the reservoir states, providing a reconfigurable RC system with an expanded capacity to effectively process and accommodate diverse signals. This holds potential for next‐generation hybrid intelligent applications.

Investigation into white matter microstructure differences in visual training by using an automated fiber tract subclassification segmentation quantification method

Visual training has emerged as a useful framework for investigating training-related brain plasticity, a highly complex task involving the interaction of visual orientation, attention, reasoning, and cognitive functions. However, the effects of long-term visual training on microstructural changes within white matter (WM) is poorly understood. Therefore, a set of visual training programs was designed, and automated fiber tract subclassification segmentation quantification based on diffusion magnetic resonance imaging was performed to obtain the anatomical changes in the brains of visual trainees. First, 40 healthy matched participants were randomly assigned to the training group or the control group. The training group underwent 10 consecutive weeks of visual training. Then, the fiber tracts of the subjects were automatically identified and further classified into fiber clusters to determine the differences between the two groups on a detailed scale. Next, each fiber cluster was divided into segments that can analyze specific areas of a fiber cluster. Lastly, the diffusion metrics of the two groups were comparatively analyzed to delineate the effects of visual training on WM microstructure. Our results showed that there were significant differences in the fiber clusters of the cingulate bundle, thalamus frontal, uncinate fasciculus, and corpus callosum between the training group compared and the control group. In addition, the training group exhibited lower mean fractional anisotropy, higher mean diffusivity and radial diffusivity than the control group. Therefore, the long-term cognitive activities, such as visual training, may systematically influence the WM properties of cognition, attention, memory, and processing speed.

Integrated In-Memory Sensor and Computing of Artificial Vision Based on Full-vdW Optoelectronic Ferroelectric Field-Effect Transistor.

The development and application of artificial intelligence have led to the exploitation of low-power and compact intelligent information-processing systems integrated with sensing, memory, and neuromorphic computing functions. The 2D van der Waals (vdW) materials with abundant reservoirs for arbitrary stacking based on functions and enabling continued device downscaling offer an attractive alternative for continuously promoting artificial intelligence. In this study, full 2D SnS2 /h-BN/CuInP2 S6 (CIPS)-based ferroelectric field-effect transistors (Fe-FETs) and utilized light-induced ferroelectric polarization reversal to achieve excellent memory properties and multi-functional sensing-memory-computing vision simulations are designed. The device exhibits a high on/off current ratio of over 105 , long retention time (>104 s), stable cyclic endurance (>350 cycles), and 128 multilevel current states (7-bit). In addition, fundamental synaptic plasticity characteristics are emulated including paired-pulse facilitation (PPF), short-term plasticity (STP), long-term plasticity (LTP), long-term potentiation, and long-term depression. A ferroelectric optoelectronic reservoir computing system for the Modified National Institute of Standards and Technology (MNIST) handwritten digital recognition achieved a high accuracy of 93.62%. Furthermore, retina-like light adaptation and Pavlovian conditioning are successfully mimicked. These results provide a strategy for developing a multilevel memory and novel neuromorphic vision systems with integrated sensing-memory-processing.

Laser additive manufacturing of shape memory biopolymer bone scaffold: 3D conductive network construction and electrically driven mechanism

IntroductionThe electro-actuated shape memory polymer scaffold has gained increasing attentions on the utilization of minimally invasive surgery for bone defect repair, which requires to construct an efficient conductive network to accomplish electrical-to-thermal conversion from conductive fillers to the entire matrix evenly. ObjectivesIn this study, multiwall carbon nanotube (MWCNT) was convective self-assembled on the ZnO tetrapod (t-ZnO) template, where MWCNT was controlled to disperse uniformly and regulated to contact with each other effectively due to the immersion capillary force during the evaporation loss of the convective self-assembly process, leading to an interwoven layer on the t-ZnO surface. MethodsThe prepared t-ZnO@MWCNT assembly was embedded in the poly(L-lactic acid)/thermoplastic polyurethane (PLLA/TPU) scaffold fabricated via selective laser sintering to construct a 3D conductive MWCNT network for improving the electro-actuated shape memory properties. ResultsIt was observed that the interconnected MWCNT formed a 3D conductive network in the matrix without significant aggregation, which boosted the electrical-to-thermal properties of the scaffold, and the scaffold containing t-ZnO@MWCNT assembly possessed better electro-actuated shape memory properties with shape fixity of 98.0% and shape recovery of 98.8%. ConclusionThe scaffold exhibited improved electro-actuated shape memory properties and mechanical properties and the osteogenic inductivity was promoted with the combined effect of t-ZnO and electrical stimulation.

Fabrication of well-defined lignin-derived elastomers by atom transfer radical polymerization and Diels-Alder reaction towards sustainable, super-tough and high temperature-resistant hot-melt adhesives

Hot-melt adhesives synthesized from sustainable resource have attracted increasing attention in fields of home decoration, handicraft production, automotive industries, etc., due to their free of volatile organic compounds (VOCs) and environmentally friendly advantages. However, it is still challenging to fabricate super-tough and heat-resistant hot-melt adhesives with utilization of the inherited properties of sustainable resource as feedstock. Herein, a combination of photoinduced metal-free atom transfer radical polymerization (ATRP) and Diels-Alder (DA) reaction was applied to prepare lignin-derived elastomers for super-tough and high temperature-resistant hot-melt adhesives. Firstly, photoinduced metal-free ATRP of lauryl methacrylate (LMA) derived from fatty acids and furfuryl methacrylate (FMA) derived from furfural, were employed to fabricate sustainable well-defined multi-arm lignin-graft copolymers bearing furfuryl groups with the aid of lignin based macro-initiator (L-Br). The photoinduced metal-free ATRP in this work could be activated and deactivated by an “ON–OFF” ultraviolet (UV) light switch, and successfully achieved the precise control over the molecular weight and composition of lignin-graft copolymers. Then, Diels-Alder (DA) reaction was applied to functionalize the lignin-graft copolymers with dynamic crosslinked network and obtain the lignin-derived elastomers. Benefiting from the multi-arm structure and the dynamic crosslinked network, these lignin-derived elastomers exhibited favorable mechanical property, thermal stability, shape memory, self-healing and recycled properties. In addition, the lignin-derived elastomers could be used as hot-melt adhesives and their lap shear strengths could reach 5.95 MPa, while being bonded with aluminum plates. Moreover, the steel plate bonded with lignin-derived elastomers could remain stable even at 100 °C. We believe that this work will offer a new approach for designing lignin-derived elastomers towards sustainable, strong, super-tough and high-temperature-resistant hot-melt adhesives.

Nanocomposites of polyethylene with Fe3O4 nanoparticles via surface-initiated ROMP: Thermomechanical, shape memory and photothermal properties

The nanocomposites of polyethylene with Fe3O4 nanoparticles (NPs) were synthesized via surface-initiated ring-opening metathesis polymerization (SI-ROMP). First, the surfaces of pristine Fe3O4 NPs were functionalized with (5-nicyclo[2.2.1]hept-2-enyl)ethyl]triethoxylsilane. The as-obtained Fe3O4 NPs functionalized with norbornene were used to mediate the ROMP of cyclooctene (COE). By controlling the mass ratios of COE to the surface-functionalized Fe3O4 NPs, the polycyclooctene-grafted Fe3O4 NPs (denoted PCOE-g-Fe3O4 NPs) were obtained with variable contents of Fe3O4 NPs. Finally, these PCOE-g-Fe3O4 NPs were hydrogenated into the corresponding polyethylene-grafted Fe3O4 NPs (PE-g-Fe3O4 NPs). The nanocomposites of PE with Fe3O4 NPs were successfully synthesized as evidenced by the morphologies that the Fe3O4 NPs were well dispersed in PE matrix at the scale of nanometer. The nanocomposites displayed the mechanical properties superior to plain PE. More importantly, the nanocomposites were capable of displaying superparamagnetic, thermally-induced shape memory and photothermal properties. Furthermore, the photothermal behavior can be utilized to trigger the shape memory properties in a non-contact fashion.

A comprehensive characterization of 3D printable poly ether ketone ketone

The current work focuses on the comprehensive characterization of a 3D printable biomaterial, polyether ketone ketone (PEKK). The PEKK granules are first characterized and then utilized for extrusion of the PEKK filaments. The extruded PEKK filaments are characterized for crystallinity, quality, and printability, wherein they exhibit amorphous nature, good quality, and appropriate printability. Utilizing the filaments, the samples are printed with the appropriate printing parameters, which are further characterized for layer adhesion, voids, and crystallinity, wherein they showed seamless layer adhesion, improper beads consolidation, and the amorphous nature. The as printed samples are further annealed at different temperatures (200 and 250 °C). The scanning electron microscopy (SEM) of the annealed samples (A-200 and A-250) revealed better void consolidation, while the X-ray diffraction (XRD) revealed better crystallinity compared to the un-annealed sample. The printed samples are also investigated for dynamic mechanical analysis (DMA), shape memory, and tensile properties. The storage moduli of the annealed samples are observed to be better than the un-annealed sample. The annealed samples exhibited better shape memory properties: shape fixity and shape recovery ratio of A-200 and A-250 samples, 90.28 and 90.75%, and 99.16 and 94.73%, respectively, compared to the un-annealed samples. The highest shape fixity ratio and the shape recovery ratio are noted for A-250 (90.75%) and A-200 (∼ 100%). The A-200 and A-250 samples showed enhanced tensile modulus and strength, 4.16 and 49.67%, and 36.61 and 35.06%, respectively compared to the un-annealed sample. The highest modulus is noted for A-250, while the strength is comparable (36.61 and 35.06%) for A-200 and A-250.

Effect of curing temperature on shape memory properties of graphene oxide‐carbon fiber hybrid‐reinforced shape memory polymer composites

AbstractIn order to study the effect of curing temperature on the shape memory properties (SMPs) of GO‐CF hybrid‐reinforced SMPCs, eight sets of composites were prepared at different curing temperatures (60, 70, 80, 90, 100, 110, 120, and 130°C). The rheological and curing properties of the matrix, and the apparent density, porosity, microstructure, and SMPs of the composite were measured and analyzed, respectively. The results showed that at 100°C, the composite has the highest apparent density (1.52 g/cm3), the lowest porosity (0.47%), the best SMPs, the shape fixed rate of 99.13%, the shape recovery rate of 96.54%, and the maximum shape recovery force (5.15 N). The shape recovery speed and rate of shape recovery are also the fastest. This is because when the curing temperature is 100°C, the viscosity of the matrix can be kept at a low level, and the curing reaction rate is appropriate. This not only ensures that the matrix can be fully infiltrated into the fiber space but also obtains the matrix with appropriate crosslinking degree. Therefore, the microstructure of the prepared composite is ideal, which makes the matrix has a strong ability to restrain the expansion force of the fiber and a high efficiency of the transfer force and heat. Finally, in the test of SMPs, the composite showed higher SMPs. It was concluded that the SMPs of the composites were mainly affected by the infiltration effect of the matrix in the fiber.Highlights The microstructure of the composite was more ideal at 100 °C. The composite with the lowest porosity has the best shape memory properties. The high porosity weakens the heat and force transfer ability of the matrix. The shape memory properties of composites are the best prepared at 100 °C.

Preparation and Performance Study of Polyurethane/Epoxy Resin Interpenetrating Networks with Self‐Healing, Shape Memory, and High Damping Properties

AbstractInterpenetrating polymer networks (IPNs) possessing a wide damping temperature range are commonly employed as damping materials that are applied in numerous fields. But IPNs will gradually appear to fatigue microcracks and fracture when long‐term in the energy absorption and dissipation. So, the ability for IPNs to recover from damage is necessary that can considerably prolong their service life. Based on polyurethane elastomer and cross‐linked epoxy, several innovative self‐healing IPNs are designed and prepared in this research. The IPNs containing dynamic disulfide bond exhibit favorable damping and self‐healing properties. In detail, the loss factor of IPN‐80/20 is beyond 0.3 at temperatures between −0.8 and 65.5 °C that covers the operating temperature range at room temperature. What is more, The PU/EP IPNs also have excellent shape memory properties, self‐healing ability, and thermal stability.