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Updated: Sep 4, 2026

Magnetometric Characterization of Intermediates in the Solid-State Electrochemistry of Redox-Active Metal-Organic Frameworks
Published on: June 9, 2023
Magnetoelectrocatalysis at the frontier: advanced quantum materials, mechanistic insights, and application landscapes
Koushik Mitra1, Saheli Samanta2, Uttam Kumar Ghorai1
1Department of Industrial Chemistry and Applied Chemistry, Swami Vivekananda Research Centre, Ramakrishna Mission Vidyamandira, Belur Math, Howrah-711202, India. uttam.indchem@vidyamandira.ac.in.
Abstract:
Amid escalating environmental pollution and the urgent surge in sustainable energy demand, electrocatalysis has emerged as a key technology for sustainable energy conversion and chemical transformation. However, despite decades of progress, the rational design and development of electrocatalysts that simultaneously overcome kinetic, stability, and selectivity limitations remain central challenges. In this review, we summarize the foundations and boundaries of the magneto-electrocatalytic domain, an emerging transformative breakthrough that integrates magnetisation and advanced quantum materials into electrochemical transformations. We bridge material innovations spanning from three-dimensional to two-dimensional limits and topological quantum systems, with mechanistic insights into spin-dependent reaction pathways, highlighting how magnetic interactions can modulate adsorption energetics, interfacial charge transfer and reaction intermediates. Particular emphasis is placed on experimentally validated catalysts and advanced quantum materials exhibiting phenomena such as spin-momentum locking, magnetochiral anomaly, and chirality-induced spin selectivity (CISS). We discuss how either low external magnetic fields or fictitious magnetic fields arising from Berry curvature engineering can orchestrate complex catalytic frameworks and alter rate-determining steps. By unifying magnetohydrodynamic effects with spin polarization mechanisms, we provide a comprehensive framework for a better understanding of coupled electric-magnetic interactions under operando conditions. Finally, we outline practical guidelines and emerging opportunities for data-driven discovery, emphasizing how artificial intelligence (AI) and machine learning (ML) can decode multivariate spin-field-structure correlations and accelerate catalyst selection and optimization. Together, these perspectives define design principles, mechanistic benchmarks and technological opportunities that position magnetoelectrocatalysis as a transformative platform for next-generation spin-engineered electrochemical systems.
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