The urgency for sustainable and efficient hydrogen production has increased interest in heterostructured nanomaterials, known for their excellent photocatalytic properties. Traditional synthesis methods often rely on trial-and-error, resulting in inefficiencies in material discovery and optimization. This work presents a new AI-driven framework that overcomes these challenges by integrating advanced machine-learning techniques specific to heterostructured nanomaterials. Graph Neural Networks (GNNs) enable accurate representations of atomic structures, predicting material properties like bandgap energy and photocatalytic efficiency within ± 0.05 eV. Reinforcement Learning optimises synthesis parameters, reducing experimental iterations by 40% and boosting hydrogen yield by 15–20%. Physics-Informed Neural Networks (PINNs) successfully predict reaction pathways and intermediate states, minimizing synthesis errors by 25%. Variational Autoencoders (VAEs) generate novel material configurations, improving photocatalytic efficiency by up to 15%. Additionally, Bayesian Optimisation enhances predictive accuracy by 30% through efficient hyperparameter tuning. This holistic framework integrates material design, synthesis optimization, and experimental validation, fostering a synergistic data flow. Ultimately, it accelerates the discovery of novel heterostructured nanomaterials, enhancing efficiency, scalability, and yield, thus moving closer to sustainable hydrogen production with improvements in photolytic efficiency, setting a benchmark for AI-assisted research.

Machine Learning approaches from the present state either use unimodal data, unable to model elegant long spatial-temporal dependencies in warning systems or create early warning response datasets with limited quantitative interpretability sets. To address these shortcomings, this work introduces T-RUNSAFE, a multi-pronged, machine learning-based predictive prototype for thermal runaway assessment. The framework integrates five specialized modules: (1) ST-Former, a spatiotemporal transformer that encodes thermal gradients from thermal images and sensor logs using temporal self-attention over LSTMs, thus is superior to traditional LSTMs for capturing evolving thermal patterns; (2) FUSE-GEN, adversarial trained dual-encoder variational autoencoder, fusing acoustic emission (AE) signals and thermal embeddings into a shared latent space for early-stage internal degradation detection; (3) DEGRA-GNN, a graph attention network that capitalizes on battery electrode topology to model the spatial propagation of thermal faults; (4) CAUS-RUN, a counterfactual simulation engine employing structural causal models to attribute risk to specific spatial zones for interpretability; and (5) SENSOR-RL, a reinforcement learning module optimizing sensor sampling policies on real-time risk levels that cuts down on sensor power while still holding to detection accuracy. The experimental results show great early prediction accuracy (AUC-ROC > 0.96), high spatial degradation localization accuracy (93.5%), and a 37% decrease in power consumption of sensing. T-RUNSAFE predicts, interprets, and optimizes resource utilization for thermal runaway risk assessment. By integrating deep learning, physics-informed modeling, and causal reasoning, it enables real-time battery safety monitoring. Although challenges remain regarding sensor cost, computational overhead, and chemistry generalization, the study demonstrates the feasibility of advanced onboard battery management systems tailored for next-generation energy applications.

Nanofluid preparation and its deposition on metallic surfaces are in wide practice due to their enhancement capability in the pool boiling heat transfer. The effect of zirconia nanoparticles coated layer on pool boiling heat transfer has been investigated with de-ionized water. The turbidity, viscosity and stability test are conducted, and confirmed a better dispersion of nanoparticles. The synthesized zirconia nanofluid is deposited by Nanofluid boiling nanoparticle technique at different concentrations on three copper samples. The conventional characterization techniques confirms the better structural behavior of the coating. The obtained results of non-coated copper sample fits well with Rohsenow correlation, which signifies the reliability and stability of the obtained results. Experimental results shows that sample with 200nm coating thickness and 227nm surface roughness improved the heat transfer coefficient by 31.52% compared to non-coated copper sample. The bubble behavior of the same has the highest velocity and lower bubble diameter compared to other at 785kW/m² of heat flux. Hence by this experimentation, it is found that the effect of surface characteristics of the zirconia coated layer on the copper surface enhances the pool boiling heat transfer coefficient at the maximum coating thickness and surface roughness by the considered coating technique.

Due to microscale effects, the segmented microchannels or micro pin fin heat sinks emerged as a high thermal management solution. In this context, the present work analyzes the influence of different heights of square micro pin fins with an aligned array and investigates their influence on pressure drop and heat transfer behavior. The HFE-7100 is used as the working fluid, and the pressure drop and surface temperature behavior are analyzed for different mass fluxes and inlet subcooling. The single-phase flow was analyzed numerically using the computational fluid dynamics (CFD) software ANSYS FLUENT^® for comparing the simulation results with the experimental data, showing that the highest micro pin fins configuration provides a more uniform and lowest wall temperature distribution compared to the lowest configuration. There is a good agreement between the experimental results and the numerical analysis, with a mean absolute error of 6% for all the considered parameters. For the two-phase flow condition, experimental tests were performed, and for the highest subcooling, an increase in mass flux causes an enhancement in the heat transfer for low heat flux; by increasing heat flux, there is a gradual predominance of boiling heat transfer over convection as the heat transfer mechanism. The pressure drop drastically increases with the vapor amount flowing into the system, regardless of the pin fin height; the boiling curves for the higher fin height show a much smaller slope and a smaller wall superheat than the fin with the smallest height, and consequently, a high heat transfer performance. A larger region of the heat sink is filled with vapor for lower inlet subcooling temperatures, degrading the heat transfer performance compared to higher inlet subcooling temperatures.