Designing less noble yet high-performing multimetallic catalysts for catalytic converters using a high-throughput approach

Catalysis Science & Technology 2026

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Authors: Son Dinh Le, Nhan Nu Thanh Ton, Panitha Phulkerd, Poulami Mukherjee, Naoto Nagata, Toshiaki Taniike
DOI: 10.1039/d5cy01298h
Cited by: 0
References: 77

Abstract

High-throughput screening of 140 multimetallic catalysts identifies cost-effective formulations with 40–60% PGM achieving superior NO reduction ( T 50 = 271–296 °C). Machine learning and statistics provide insights into metal synergies and promoters.

Fields of Study & Subjects

Catalytic Processes in Materials Science Machine Learning in Materials Science Electrocatalysts for Energy Conversion

Publication details

Journal / source
Catalysis Science & Technology
Volume
16
Issue
4
Pages
1348–1358
Publication date
1 January 2026
Language
EN