AI-Assisted Operando Surface Chemistry Design of Water Oxidation Catalysts: Bridging Advanced Energy Conversion with Human Exposure Risk Assessment of Traffic-Related Volatile Organic Compounds
Keywords:
operando spectroscopy, water oxidation, artificial intelligence, machine learning, volatile organic compounds, exposure assessment, sustainable energy, air quality, socio-technical systemsAbstract
The acceleration of sustainable hydrogen production through water oxidation catalysis stands at the frontier of energy conversion technologies, yet the societal dimensions of associated environmental exposures remain largely disconnected. This paper develops an integrative framework that couples artificial intelligence (AI)-assisted operando surface chemistry design of oxygen evolution catalysts with the system-level assessment of human exposure to traffic-related volatile organic compounds (VOCs). Operando spectroscopy combined with machine learning now enables high-throughput interrogation of catalyst surface states under reaction conditions, revealing electronic structure descriptors that govern performance. Concurrently, urban air quality research leverages AI-driven spatiotemporal models and dense sensor networks to capture the dynamic variability of VOC concentrations in transportation microenvironments, generating fine-grained exposure and health risk profiles. We argue that the data architectures, model interpretability requirements, and adaptive feedback strategies developed for catalyst discovery offer transferable design principles for predictive environmental health systems. Conversely, exposure assessment methodologies, particularly those involving low-cost sensing and population mobility patterns, can inform the life-cycle risk evaluation of catalyst materials and deployment infrastructure. The paper examines structural trade-offs in merging these domains through shared digital twin platforms, highlights governance challenges including data sovereignty, algorithmic fairness, and regulatory harmonization, and advocates for a convergent socio-technical infrastructure that simultaneously advances clean energy goals and public health protection. Robustness, sustainability, and policy implications are systematically discussed, culminating in a research agenda that places AI at the center of an integrated energy-environment-health continuum.
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