Developing highly active electrocatalysts for the oxygen evolution reaction is a pivotal challenge in sustainable water electrolysis. Herein, we report a novel in situ oxidative phase-restructuring strategy to fabricate oxygen vacancy-rich NiFe2O4 (NFO) directly on nickel foam. Distinct from conventional hydrothermal methods that typically yield thermodynamically stable crystals with limited intrinsic defects, our unique one-pot process involves the formation of a reduced metallic intermediate. The subsequent drastic phase transformation from this metallic state to a spinel oxide thermodynamically enforces the generation of abundant oxygen vacancies to relieve lattice stress, resulting in unique polycrystalline nanocrystal assemblies (NFO-1). Electrochemical evaluations reveal that NFO-1 significantly outperforms its thermodynamically equilibrated counterpart (NFO-2), exhibiting a low overpotential of 330 mV at 20 mA cm−2 and a remarkable mass activity of 6.78 A g−1. This superior performance is primarily attributed to intrinsic oxygen vacancies generated during the oxidative phase evolution, which optimize the active-site electronic structure and enhance charge–transfer kinetics. Furthermore, the catalyst demonstrates excellent durability over 1200 cycles. This work highlights oxidative phase restructuring as a powerful pathway to engineer intrinsic defects for high-efficiency energy-conversion applications.

Amid escalating climate challenges and the inefficiencies of linear economic models, this review examines how artificial intelligence (AI) can enable circular economy (CE) strategies to advance environmental sustainability in the energy sector. Drawing on 40 peer-reviewed studies from 2020 to 2025, the research highlights AI applications like machine learning, predictive analytics, and digital twins which optimize resource use, reduce waste, and enhance energy efficiency across industrial and energy systems. AI demonstrates significant potential in integrating renewables, extending battery life, optimizing smart grids, and transforming market operations through automated trading and blockchain-enabled transparency. However, barriers like high costs, data accessibility issues, regulatory gaps, and skills shortages hinder widespread adoption, particularly among small and medium enterprises. Emerging solutions, including federated learning for decentralized energy management and simulationbased design for material efficiency, offer promising pathways forward. The study emphasizes the need for targeted policies, open data frameworks, and interdisciplinary collaboration to address systemic challenges. By providing a comprehensive review that aims to accelerate the integration of CE principles into the energy sector through AI, while calling for further empirical research to evaluate long-term sustainability outcomes and ethical implications.

Three-dimensional (3D) printing technology has emerged as a critical tool in healthcare emergency responses, addressing challenges such as supply chain disruptions and resource constraints. This paper explores its role in on-demand production of medical equipment, including personal protective equipment (PPE) and patient-tailored devices for disaster response. The study also examines 3D printing applications in creating temporary medical facilities and mobile healthcare units, emphasizing its speed, adaptability, and potential for enhancing care in disaster-stricken and remote areas. Ethical and regulatory considerations are discussed, alongside the need for robust frameworks to ensure safety and equity. While 3D printing offers transformative possibilities for building resilient healthcare systems, challenges related to cost, regulation, and scalability must be addressed. Future research and collaboration are vital to fully realize its potential in global healthcare delivery.

The integration of artificial intelligence (AI) into computational materials science (CMS) has introduced powerful approaches for accelerating the discovery and optimization of advanced energy materials. As energy demands shift toward renewable systems, the development of efficient materials for batteries, fuel cells, and electrocatalysts becomes increasingly critical. This paper systematically reviews recent AI methodologies applied within CMS, particularly those leveraging density functional theory (DFT), molecular dynamics (MD), and kinetic Monte Carlo (KMC) simulations. Emphasis is placed on the use of machine learning (ML) models, including supervised learning, deep learning, and hybrid strategies for property prediction, structure optimization, and inverse design. The review categorizes current applications across key energy technologies and discusses how AI is reshaping material screening and development pipelines. It concludes with an outlook on future directions, highlighting the need for standardized datasets, interpretable models, and physics-informed frameworks to improve predictive accuracy and facilitate AI adoption in practical materials research.

Artificial Intelligence (AI) is transforming the prediction and optimization of advanced energy materials by enabling accurate, scalable modeling beyond traditional methods. This review evaluates recent AI applications—including Graph Neural Networks (GNNs), Convolutional and Recurrent Neural Networks (CNNs, RNNs), tree-based ensembles, and Gaussian Process Regression (GPR)—for forecasting performance metrics such as overpotential, conductivity, capacity, and degradation. GNNs achieved R2 > 0.90 in structure-sensitive tasks; LSTM models predicted battery degradation with <10% error; and tree-based models balanced accuracy (MAE < 0.15 V) with interpretability. GPR excelled in low-data regimes via uncertainty quantification. Hybrid and physics-informed models improved generalizability and data efficiency. While challenges remain in data quality and integration with experiments, emerging strategies like autonomous labs and generative design offer promising advances. This review provides comparative benchmarks and highlights pathways for robust AI-driven materials discovery.