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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. 

In the title compound, C16H16N2O3, the phenyl groups are twisted away from coplanarity with the ether linkage, forming a dihedral angle of 59.49 (4) with each other. The ether oxygen atom lies somewhat out of both phenyl planes, by 0.066 (2) and 0.097 (2) A ˚ . The acetamide substituents have quite different conformations with respect to the phenyl groups on either side of the molecule. On one side, the C—C—N—C torsion angle is 21.0 (2), while on the other side it is 76.4 (2). In the crystal, the acetamide N—H groups form intermolecular N—H O hydrogen bonds to acetamide O atom, with both NH groups donating to the same molecule. Thus, ladder-like chains exist in the [101] direction. One of the methyl groups has its H atoms disordered into two orientations, and the crystal chosen for data collection was found to be twinned. 

This review sets out to investigate the detrimental impacts of hydrogen gas (H2) on critical boiler components and provide appropriate state-of-the-art mitigation measures and future research directions to advance its use in industrial boiler operations. Specifically, the study focused on hydrogen embrittlement (HE) and high-temperature hydrogen attack (HTHA) and their effects on boiler components. The study provided a fundamental understanding of the evolution of these damage mechanisms in materials and their potential impact on critical boiler components in different operational contexts. Subsequently, the review highlighted general and specific mitigation measures, hydrogen-compatible materials (such as single-crystal PWA 1480E, Inconel 625, and Hastelloy X), and hydrogen barrier coatings (such as TiAlN) for mitigating potential hydrogen-induced damages in critical boiler components. This study also identified strategic material selection approaches and advanced approaches based on computational modeling (such as phase-field modeling) and data-driven machine learning models that could be leveraged to mitigate potential equipment failures due to HE and HTHA under elevated H2 conditions. Finally, future research directions were outlined to facilitate future implementation of mitigation measures, material selection studies, and advanced approaches to promote the extensive and sustainable use of H2 in industrial boiler operations. 

This study investigates a neoteric approach in manufacturing lunar regolith-filled shape memory vitrimer (SMV) composites for extraterrestrial applications. A SMV with robust mechanical properties was combined with locally available lunar regolith to form a composite material. Fourier Transfer Infrared Spectroscopy (FTIR), Scanning Electron Microscope (SEM), Thermogravimetric Analysis (TGA), and X-ray fluorescence (XRF) were used to characterize the resin, the regolith simulant, and the prepared SMV-regolith composites. We explored conventional synthesis as well as 3D printing methods for manufacturing the composite. Glass fabric-reinforced laminated composites were also prepared to evaluate the impact tolerance and damage healing efficiency. Compressive strength, flexural strength, and impact resistance of the composite were tested at both room and elevated temperatures. A compressive strength of 96.0 MPa and 5.4 MPa were recorded for composite with 40 wt% regolith ratio at room and elevated temperatures, respectively. The glass fabric reinforced SMV-regolith laminate exhibited a bending strength of 232.7 MPa, good impact tolerance under low-velocity impact test, and good healing efficiency up to two damage healing cycles. The 3D printed SMV-regolith composite using a liquid crystal display (LCD)-based printer exhibited a good thermomechanical property with a compressive and tensile strength of 139.16 MPa and 13.99 MPa, respectively, and a good shape memory effect. However, the LCD-based printing using vat-photopolymerization limits the size of the printed samples. Nonetheless, this study shows that utilization of regolith to form advanced composite is possible. SMV regolith composite is a promising material for lunar base applications due to its simple manufacturing process, excellent mechanical properties, and low energy consumption. 

This study investigated the impact of low-temperature heat treatments on the mechanical and thermophysical properties of Cu-10Sn alloys fabricated by a laser powder bed fusion (LPBF) additive manufacturing (AM) process. The microstructure, phase structure, and mechanical and thermal properties of the LPBF Cu-10Sn samples were comparatively investigated under both the as-fabricated (AF) condition and after low-temperature heat treatments at 140, 180, 220, 260, and 300 °C. The results showed that the low-temperature heat treatments did not significantly affect the phase and grain structures of the Cu-10Sn alloys. Both pre- and post-treatment samples displayed consistent grain sizes, with no obvious X-ray diffraction angle shift for the α phase, indicating that atom diffusion of the Sn element is beyond the detection resolution of X-ray diffractometers (XRD). However, the 180 °C heat-treated sample exhibited the highest hardness, while the AF samples had the lowest hardness, which was most likely due to the generation of precipitates according to thermodynamics modeling. Heat-treated samples also displayed higher thermal diffusivity values than their AF counterpart. The AF sample had the longest lifetime of ~0.19 nanoseconds (ns) in the positron annihilation lifetime spectroscopy (PALS) test, indicating the presence of the most atomic-level defects. 

ABSTRACT This study presents a novel, bio‐based polymer composite derived from tapioca starch and reinforced with jute fibers, designed for non‐load bearing structural applications. The developed composite demonstrated significant thermal stability, with a single decomposition reaction observed above 300°C via TGA, surpassing many synthetic polymers. DSC analysis revealed a glass transition temperature (Tg) of 69.55°C and notable thermal energy storage capability. Mechanical characterization, including three‐point bending, tensile, and compressive tests, confirmed effective fiber wetting and a tensile strength of 9 MPa for the composite. Furthermore, the composite exhibited mild electrical conductivity of 3.62 × 10⁻⁷ S/m. Structural characterizations (SEM, XRD, FTIR) revealed the presence of an N‐H bond, a functional group common in conductive polymers, suggesting its potential as a mild conductor. Density functional theory simulations provided further insights into the biopolymer's molecular structure. This research highlights the promising potential of tapioca starch composites for various engineering applications, particularly as sustainable packaging materials. 

A sophisticated machine learning framework was developed to design thermally robust shape memory vitrimers (TRSMVs) with superior recycling efficiency, an elevatedT_g, and outstanding shape memory properties, surpassing traditional limitations.