Machine‐Learning‐Powered Screening of CuNi@FeO Electrocatalyst for Nitrate‐to‐Ammonia Conversion in Industrial Wastewater Concentrate

ABSTRACT

High‐pressure reverse osmosis concentrates (HPROC) generated from coal chemical zero‐liquid‐discharge processes accumulate nitrate in high‐salinity matrices that deteriorate downstream salt recovery and nitrogen management. This necessitates selective nitrate conversion before evaporation‐crystallization. Electrocatalytic reduction offers a promising route to convert nitrate into ammonia, yet electrocatalyst design remains largely empirical, complicating the development of manufacturable electrodes for practical applications. Herein we developed a literature‐informed interpretable machine learning (ML) framework to screen the CuNi@FeO electrocatalyst toward highly selective nitrate‐to‐ammonia conversion under high‐salinity conditions. The self‐supporting CuNi@FeO was fabricated by spontaneous deposition on sacrificial iron foam and it could realize spatially and energetically nitrate activation, hydrogen supply, and hydrogen evolution reaction (HER) suppression, accounting for a Faradaic efficiency (FE) of 98.74% and an ammonia yield of 19.56 mg h ⁻¹ cm ⁻² at −1.0 V vs. reversible hydrogen electrode (RHE). Operando spectroscopy and theoretical calculations revealed a synergistic multisite pathway involving the sequential *NO ₃ →*N→*NH _x conversion. The membrane‐electrode assembly reactor could sustain 200 mA cm ⁻² for over 100 h and ammonia FE of 91.46% at cell voltage of 2.5 V during treatment of real industrial wastewater concentrate. This work establishes a paradigm shift to ML‐powered screening of electrocatalyst toward scalable nitrate valorization from industrial concentrates.