Alankar Alankar

This work investigates the thermo‐oxidative aging of three anonymized industrial natural‐rubber‐based tyre mixes subjected to circulating air at 75°C for up to 21 days. Uniaxial tensile testing, dynamic mechanical analysis (DMA), oxygen elemental analysis using a CHNSO elemental analyzer and through‐thickness nanoindentation were combined within a common aging protocol to compare bulk mechanical, viscoelastic, chemical, and spatially resolved aging signatures. Tensile testing showed that aging reduced rupture strain much more strongly than rupture stress, identifying loss of extensibility as the more sensitive tensile aging signature. DMA revealed formulation‐dependent changes in stiffness, dissipation and frequency sensitivity, while CHNSO analysis showed progressive bulk oxygen uptake. Nanoindentation showed substantial aging‐induced stiffening together with the development of surface‐to‐core mechanical heterogeneity. Importantly, within the present experimental dataset, the different aging descriptors did not provide identical formulation rankings. Mix C showed the greatest increase in average indentation reduced modulus, whereas Mix A developed the largest absolute surface‐to‐core modulus contrast. Bulk oxygen uptake and spatial stiffness heterogeneity therefore provide complementary rather than interchangeable information about thermo‐oxidative aging. Overall, the results show that a single bulk property can obscure important differences in aging response and support a multimodal assessment based on extensibility, viscoelastic drift, oxygen uptake, and spatial stiffness heterogeneity.

Glucose contents of simulated body fluid may both inhibit and accelerate corrosion of magnesium alloys. The nature of the influence may depend on the type and content of glucose and the characteristics of simulated body fluid. Corrosion of a Mg-Zn-Zr alloy (i.e., ZK60) in modified-simulated body fluid (m-SBF) was studied with and without added glucose, employing potentiodynamic polarisation (PDP) and electrochemical impedance spectroscopy (EIS). The morphology and chemical characteristics of the corrosion product layer were investigated by scanning electron microscopy (SEM), grazing-incidence X-ray diffraction (GI-XRD), and X-ray photoelectron spectroscopy (XPS), which enabled a mechanistic understanding of the role of glucose addition in corrosion of the alloy. Principal component analysis (PCA) through a Bayesian inference pipeline was applied to the full experimental dataset to provide a statistically rigorous and quantitative assessment of the electrochemical data beyond conventional mean-comparison. This was meant not to replace the mechanistic interpretation derived from PDP, EIS, and surface characterization, but to quantify pattern structure in the raw electrochemical data and to express uncertainty in the condition-level electrochemical summaries in probabilistic terms.

Electron backscatter diffraction (EBSD) has traditionally relied upon methods such as the Hough transform and dictionary Indexing to interpret diffraction patterns and extract crystallographic orientation. However, these methods encounter significant limitations, particularly when operating at high scanning speeds, where the exposure time per pattern is decreased beyond the operating sensitivity of CCD camera. Hence the signal to noise ratio decreases for the observed pattern which makes the pattern noisy, leading to reduced indexing accuracy. This research work aims to develop generative machine learning (ML) models for the post–processing or on-the-fly processing of Kikuchi patterns which are capable of restoring noisy EBSD patterns obtained at high scan speeds. These restored patterns can be used for the determination of crystal orientations to provide reliable indexing results. We compare the performance of such generative models in enhancing the quality of patterns captured at short exposure times (high scan speeds). An interesting observation is that the methodology is not data-hungry as typical ML methods.

High‐energy materials (HEMs) are critical for propulsion and defense domains, yet their discovery remains constrained by experimental data and restricted access to testing facilities. This work presents a novel approach toward high‐energy molecules by combining long short‐term memory (LSTM) networks for molecular generation and attentive graph neural networks (GNN) for property predictions. We propose a transformative embedding space construction strategy that integrates fixed SHA‐256 embeddings with partially trainable representations. Unlike conventional regularization techniques, this changes the representational basis itself, reshaping the molecular input space before learning begins. Without recourse to pretraining, one of the proposed models for low data regime achieves 74.3% validity and 47.1% novelty. The generated library exhibits a mean Tanimoto coefficient of 0.214 relative to the training set signifying the ability of framework to generate a diverse chemical space. We identified 37 promising candidates for further research, all exhibiting a predicted detonation velocity greater than 9 .

Graphene coatings, derived through chemical vapour deposition (CVD), offer a promising solution for corrosion mitigation due to their unique combination of impermeability, inertness and toughness. While pristine graphene coatings significantly enhance corrosion resistance, variabilities in the properties and the performance of these coatings, when influenced by CVD process parameters, pose a challenge. Variations in defect content, largely due to differing CVD methodologies across research groups, significantly affect corrosion resistance, ranging from notable improvements to potential acceleration of corrosion. To systematically address these variabilities, this review proposes the integration of machine learning (ML) techniques to optimise CVD parameters, thereby achieving more consistent and high‐quality graphene coatings. Moreover, recent advancements in artificial intelligence, specifically large language models (LLMs), have the potential to revolutionise this domain by enabling sophisticated data analysis and hypothesis generation from vast quantities of literature. These capabilities can accelerate the optimisation of graphene production processes. By integrating LLMs, researchers can quickly synthesise and assess vast amounts of published data, refine process parameters and predict outcomes more accurately. This approach not only enhances the understanding of CVD graphene's behaviour under different conditions but also drives the innovation of robust methodologies for the development of corrosion‐resistant coatings.