Recent advances in robust ammonia synthesis from nitrate reduction through MXene‐based electrocatalysts: from fundamental and development to machine-learning

Ammonia (NH₃) is a key chemical feedstock, a carbon-free fuel, and a hydrogen carrier, yet its production still relies on the energy- and emission-intensive Haber-Bosch process. Electrocatalytic nitrate reduction (eNO₃^-RR) offers a sustainable alternative, converting NO₃^- and water to NH₃ under ambient conditions, but it demands catalysts that combine high activity, selectivity, and stability. Two-dimensional transition-metal carbides, nitrides, and carbonitrides (MXenes) are promising candidates because of their high electrical conductivity, large surface area, and tunable surface chemistry, although their use in eNO₃^-RR remains underexplored. This review covers the mechanisms of nitrate-to-ammonia conversion and the synthesis of MXenes, then analyzes how noble-metal decoration, alloying, hybridization with metal compounds, and single-atom engineering shape catalytic performance. It also examines how machine-learning-assisted computational modeling can predict and optimize MXene-based catalysts. Finally, key challenges and future directions are outlined to guide the rational design of MXene electrocatalysts for green ammonia synthesis.