From MAB precursors to dynamic MBene interfaces: A mechanistic framework for working-state evolution and functional performance.

Authors

Jakkapan Kumsi, Nikom Klomkliang, Frank Güell, Bartosz Dziejarski, Mashallah Rezakazemi, Jarosław Serafin

Published in

Advances in colloid and interface science. Volume 360. Pages 104093. Nov 13, 2026. Epub Nov 13, 2026.

Abstract

Two-dimensional (2D) transition-metal borides (MBenes) have emerged as a chemically distinct family of layered materials whose behavior differs fundamentally from that of carbide- and nitride-based 2D systems. Unlike MXenes, experimentally realized MBenes are governed by precursor-encoded complexity, whereby MAB precursor chemistry, synthesis history, surface terminations, defects, reconstruction, and oxidation collectively determine their electronic structure and functional response. Although recent studies have rapidly expanded the range of synthesized MBenes and demonstrated their potential across diverse applications, the mechanistic links connecting precursor chemistry, synthesis pathways, dynamic surface evolution, and operational performance remain insufficiently resolved. This Review develops a unified mechanistic framework that connects these factors to the working states of MBenes in electrocatalysis, photocatalysis, sensing, and electrochemical energy storage. Rather than treating MBenes as static two-dimensional boride lattices, we show that their experimentally relevant behavior is more consistently understood in terms of chemically heterogeneous and dynamically evolving interfaces whose active states depend on both synthesis history and operating conditions. We critically distinguish experimentally supported observations from computational predictions and mechanistic hypotheses, with particular emphasis on operando characterization, realistic computational models, and integrated experiment theory simulation approaches. By framing MBenes as synthesis-encoded, chemically adaptive materials, this Review establishes design principles for linking precursor selection and surface evolution to functional performance and provides a foundation for predictive, mechanism-driven MBene engineering.

PMID:
43033791
Bibliographic data and abstract were imported from PubMed on 15 Nov 2026.

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