Search for: All records

Polymer matrix composites have been used extensively in the aerospace and automotive industries. Nevertheless, the growing demand for composites raises concerns about the thermal stability, cost, and environmental impacts of synthetic fillers like graphene and carbon nanotubes. Hence, this study investigates the possibility of enhancing the thermomechanical properties of polymer composites through the incorporation of agricultural waste as fillers. Particles from walnut, coffee, and coconut shells were used as fillers to create particulate composites. Bio-based composites with 10 to 30 wt.% filler were created by sifting these particles into various mesh sizes and dispersing them in an epoxy matrix. In comparison to the pure polymer, DSC results indicated that the inclusion of 50 mesh 30 wt.% agricultural waste fillers increased the glass transition temperature by 8.5%, from 55.6 °C to 60.33 °C. Also, the TGA data showed improved thermal stability. Subsequently, the agricultural wastes were employed as reinforcement for laminated composites containing woven glass fiber with a 50% fiber volume fraction, eight plies, and varying particle filler weight percentages from 0% to 6% with respect to the laminated composite. The hybrid laminated composite demonstrated improved impact resistance of 142% in low-velocity impact testing. These results demonstrate that fillers made of agricultural wastes can enhance the thermomechanical properties of sustainable composites, creating new environmentally friendly prospects for the automotive and aerospace industries. 

Due to the complex behaviour of amorphous shape memory polymers (SMPs), traditional constitutive models often struggle with material-specific limitations, challenging curve-fitting, history-dependent stress calculations and error accumulation from stepwise calculation for governing equations. In this study, we propose a physics-informed artificial neural network (PIANN) that integrates a conventional neural network with a strain-based phase transition framework to predict the constitutive behaviour of amorphous SMPs. The model is validated using five temperature–stress datasets and four temperature–strain datasets, including experimental data from four types of SMPs and simulation results from a widely accepted model. PIANN predicts four key shape memory behaviours: stress evolution during hot programming, stress recovery following both cold and hot programming and free strain recovery during heating branch. Notably, it predicts recovery strain during heating without using any heating data for training. Comparisons with experimental data show excellent agreement in both programming (cooling) and recovery (heating) branches. Remarkably, the model achieves this performance with as few as two temperature–stress curves in the training set. Overall, PIANN addresses common challenges in SMP modelling by eliminating history dependence, improving curve-fitting accuracy and significantly enhancing computational efficiency. This work represents a substantial step forward in developing generalizable models for SMPs. 

ABSTRACT The discovery of novel thermoset shape memory polymers (TSMPs) for additive manufacturing can be accelerated through the use of a deep‐generative algorithm, minimizing the need for laborious traditional laboratory experiments. This study is the first to introduce an innovative approach that uses a deep generative learning model, namely the conditional variational autoencoder (CVAE), to discover novel TSMPs with lower glass transition temperature () and high recovery stress values (). In this study, specific chemical groups, such as epoxy, amine, thiol, and vinyl, are integrated as constraints to generate novel TSMPs while preserving the essential reaction properties. To address the challenges posed by a small dataset, the CVAE model is used with graph‐extracted features. Unlike previous studies focused on single‐polymer systems, this research extends to two‐monomer samples, discovering 22 novel TSMPs. This approach has practical implications in additive manufacturing, biomedical devices, aerospace, and robotics for the discovery of novel samples from limited data. 

Abstract Skin‐like robust materials with prominent sensing performance have potential applications in flexible bioelectronics. However, it remains challenging to achieve mutually exclusive properties simultaneously including low interfacial impedance, high stretchability, sensitivity, and electrical resilience. Herein, a material and structure design concept of mixed ion‐electron conduction and mechanical interlocking structure is adopted to fabricate high‐performance mechanical‐bioelectrical dual‐modal composites with large stretchability, excellent mechanoelectrical stability, low interfacial impedance, and good biocompatibility. Flower‐like conductive metal‐organic frameworks (cMOFs) with enhanced conductivity through the overlapped level of metal‐ligand orbital are assembled, which bridge carbon nanotubes (denoted as cMOFs‐b‐CNTs). Then, precursor of poly(styrene‐block‐butadiene‐block‐styrene)/ionic liquid penetrates the pores and cavities in cMOFs‐b‐CNTs‐based network fabricated via filtration process, creating a semi‐embedded structure via mechanical interlocking. Thus, the mixed ion‐electron conduction and semi‐embedded structure endow the as‐prepared composites with a low interfacial impedance (51.60/28.90 kΩ at 10/100 Hz), wide sensing range (473%), high sensitivity (2195.29), rapid response/recovery time (60/85 ms), low limit of detection (0.05%), and excellent durability (>5000 cycles to 50% strain). Demonstrations of multifunctional mechanical‐bioelectrical dual‐modal sensors for in vivo/vitro monitoring physiological motions, electrophysiological activities, and urinary bladder activities validate the possibility for practical uses in biomedical research areas. This concept creates opportunities for the construction of durable skin‐like sensing materials.