Life cycle assessment of nanowires for sustainable material production: A systematic review

• Integration of LCA with AI for predictive sustainability assessment. • Development of online dashboard for real-time LCA data visualization. • Application of ex-ante and process-based LCA for emerging nanomaterial systems. • Coupling SSbD principles with LCA to guide safe, eco-efficient material design. • Implementation of ML algorithms for sustainability clustering and optimization. Nanowires (NWs) are groundbreaking materials in the domains of energy storage, photovoltaics, electronics, and biomedicine due to their exceptional electrical, optical, and structural properties. However, because their synthesis often involves energy-intensive processes and hazardous chemicals, there are serious sustainability issues. This analysis integrates bibliometric and content-based evaluations carried out over the past decade to evaluate the environmental performance of silicon, alumina, gallium-based, and silicon-carbide NWs using Life Cycle Assessment (LCA). Comparative results from multiple applications, such as CO₂ photoreduction, lithium-ion batteries, tandem solar cells, and polymer reinforcement, demonstrate that silicon NWs are among the most environmentally hazardous, primarily contributing to human toxicity, mineral resource depletion, and the potential for global warming. Alumina and SiC NWs, on the other hand, have better environmental profiles, underscoring the significance of choosing sustainable materials. Important methodological flaws are noted, such as insufficient end-of-life modelling, the omission of Life Cycle Cost (LCC) and Social LCA (S-LCA), and the failure to adequately account for regional energy mixes. The paper suggests using artificial intelligence and machine learning to improve data-driven modelling, scenario prediction, and sustainability forecasting in order to address these problems. It also suggests combining Safe-and-Sustainable-by-Design (SSbD) principles with LCA. The novelty of this study lies in integrating SSbD and AI/ML into the LCA framework through MATLAB and online dashboards for predictive, data-driven NW sustainability design. It provides a policy-oriented roadmap to steer nanowire technologies toward socially equitable, economically feasible, and environmentally sustainable innovation, consistent with circular economy and SSbD principles.