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In the title compound, C₈H₈ClNO₂, the acetamide substituent is twisted out of the phenyl plane, forming a dihedral angle of 58.61 (7)°. In the extended structure, each molecule donates two hydrogen bonds [N—H...O(carbonyl) and O—H...O(carbonyl)] and thus also accepts two such hydrogen bonds. The chlorine atom is not involved in the hydrogen bonding. 

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. 

In the title compound, C10H12N2O4, the four substituents lie out of the phenyl plane by varying degrees. The methyl C atom lies 0.019 (3) A ˚ out of plane, while the methoxy O and C atoms lie 0.067 (2) and 0.042 (3) A ˚ out of plane, respectively, with the C—C—O—C torsion angle being 3.3 (2). The plane of the nitro group is twisted out of the phenyl plane, forming a dihedral angle of 12.03 (9) with it. The acetamide substituent is twisted considerably more out of the phenyl plane, forming a dihedral angle of 47.24 (6) with it. In the extended structure, the acetamide NH group donates a hydrogen bond to an acetamide carbonyl O atom, thereby forming chains propagating in the [010] direction. 

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. 

ABSTRACT The discovery of high‐performance shape memory polymers (SMPs) with enhanced glass transition temperatures (Tg) is of paramount importance in fields such as geothermal energy, oil and gas, aerospace, and other high‐temperature applications, where materials are required to exhibit shape memory effect at extremely high‐temperature conditions. Here, we employ a novel machine learning framework that integrates transfer learning with variational autoencoders (VAE) to efficiently explore the chemical design space of SMPs and identify new candidates with high Tg values. We systematically investigate the effect of different latent space dimensions on the VAE model performance. Several machine learning models are then trained to predict Tg. We find that the SVM model demonstrates the highest predictive accuracy, withR²values exceeding 0.87 and a mean absolute percentage error as low as 6.43% on the test set. Through systematic molar ratio adjustments and VAE‐based fingerprinting, we discover novel SMP candidates with Tg values between 190°C and 200°C, suitable for high‐temperature applications. These findings underscore the effectiveness of combining VAEs and SVM for SMP discovery, offering a scalable and efficient method for identifying polymers with tailored thermal properties. 

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.