This is a critical review of artificial intelligence/machine learning (AI/ML) methods applied to
battery research. It aims at providing a comprehensive, authoritative, and critical, yet easily …

Presently, conventional technologies in water treatment are not efficient enough to
completely mineralize refractory water contaminants. In this context, the implementation of …

During the last decades, X-ray absorption spectroscopy (XAS) has become an
indispensable method for probing the structure and composition of heterogeneous catalysts …

The discovery and development of catalysts and catalytic processes are essential
components to maintaining an ecological balance in the future. Recent revolutions made in …

Transition metal-nitrogen-doped carbons (TMNCs) are a promising class of catalysts for the
CO2 electrochemical reduction reaction. In particular, high CO2-to-CO conversion activities …

This review paper delves into synergistic integration of artificial intelligence (AI) and
machine learning (ML) with high-throughput experimentation (HTE) in the field of …

Metal-organic frameworks (MOFs) are a new class of nanoporous materials that are widely
used in various emerging fields due to their large specific surface area, high porosity and …

Abstract Machine learning (ML) is emerging as a powerful tool for identifying quantitative
structure–activity relationships to accelerate electrocatalyst design by learning from historic …

Electrocatalytic carbon dioxide/carbon monoxide reduction reaction (CO (2) RR) has
emerged as a prospective and appealing strategy to realize carbon neutrality for …

The potential of operando X-ray techniques for following the structure, fate, and active site of
single-atom catalysts (SACs) is highlighted with emphasis on a synergetic approach of both …