@article{Ghoreishian2026, 
author = {Seyed Majid Ghoreishian and Masoomeh Ghasemi and Nasrollah Hamidi and Jianhua Tong and H. Bryan Riley},
title = {Recent advances in robust ammonia synthesis from nitrate reduction through MXene‐based electrocatalysts: from fundamental and development to machine-learning},
year = {2026},
journal = {Composite Functional Materials},
volume = {2},
number = {3},
keywords = {Ammonia, Electrocatalytic, Renewable energy, Nitrogen reduction, Hydrogen carrier, Machine Learning},
url = {https://www.sciopen.com/article/10.63823/20260306},
doi = {10.63823/20260306},
abstract = {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.}
}