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Toward Practical Design of High-Entropy Catalysts for Chlorine Evolution Reaction via Pareto-Guided Multi-Objective
Ruyu Yang1, Donglai Zhou1, Zijin Jia1
1State Key Laboratory of Precision and Intelligent Chemistry, Hefei National Research Center for Physical Sciences at the Microscale, University of Science and Technology of China, Hefei, China.
Abstract:
The electrocatalytic chlorine evolution reaction (CER) is essential to modern chlor-alkali industry, yet conventional RuO2 catalysts suffer from parasitic oxygen evolution. High-entropy ruthenium oxides (Ru-HEO) are promising alternatives, but their practical design is hindered by complex composition-structure-performance relationship. Herein, we construct a Pareto-guided multi-objective Bayesian optimization framework to enable autonomous high-throughput exploration of quinary Ru-HEO system. Through this trade-off strategy, we identify compositions that efficiently balance mass activity, Cl2 selectivity and material cost. The leading Ru-HEO catalyst with only 8.4 at% Ru achieves a remarkable activity of 5083 A g-1 Ru at 1.50 V versus RHE and maintains excellent 100-h stability, outperforming commercial RuO2 and the state-of-the-art catalysts reported. Integrated into a photovoltaic-electrochemical (PV-EC) prototype device and tested under simulated diurnal illumination, it sustains >95% selectivity, a maximum solar-to-chemical (STC) efficiency of 14.6% and projected Cl2 production costs as low as $0.177 per kg. Our work establishes a closed-loop, AI-accelerated research paradigm that integrates multi-objective optimization with robotic experimentation, offering a generalizable and expedited pathway toward high-performance electrocatalysts for sustainable chemicals manufacturing.
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