Activity Trends of Methane Oxidation Catalysts under Emission Conditions,ACS Catalysis

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Activity Trends of Methane Oxidation Catalysts under Emission Conditions
ACS Catalysis ( IF 11.3 ) Pub Date : 2022-08-05 , DOI: 10.1021/acscatal.2c00842
Gi Joo Bang ₁ , Geun Ho Gu _{1,

2} , Juhwan Noh ₁ , Yousung Jung ₁

Affiliation

The emission of unburned exhaust methane from natural-gas-based combustion engines is an important source of greenhouse gas to control. Rutile IrO₂ has shown great potential as a methane oxidation catalyst, but further developments for practical use have been slow as the kinetic mechanism and design principles under exhaust conditions are poorly understood. Here, we demonstrate the experiment-validated first-principles-based microkinetic model (MKM) for IrO₂ to elucidate the mechanistic insights and develop the descriptor-based MKM screening pipeline to discover feasible catalysts for methane complete oxidation. The framework uses a minimal number of ab initio descriptors suggested by sensitivity analysis and scaling relations, equipped further with a machine learning model to extend the search space to a larger scale. We search through hundreds of doped rutile oxides by constructing the MKM-based activity map and suggest promising Pareto-optimum candidates. The proposed workflow can be extended to explore other industrial catalysts under experimental conditions.

中文翻译：

排放条件下甲烷氧化催化剂的活性趋势

以天然气为基础的内燃机排放的未燃烧废气甲烷是需要控制的重要温室气体来源。金红石 IrO ₂已显示出作为甲烷氧化催化剂的巨大潜力，但由于对排气条件下的动力学机制和设计原理知之甚少，实际应用的进一步发展一直很缓慢。在这里，我们展示了经过实验验证的基于第一原理的 IrO _{2微动力学模型 (MKM)}阐明机理见解并开发基于描述符的MKM筛选管道，以发现甲烷完全氧化的可行催化剂。该框架使用由敏感性分析和缩放关系建议的最少数量的从头算描述符，进一步配备了机器学习模型以将搜索空间扩展到更大的范围。我们通过构建基于 MKM 的活动图来搜索数百种掺杂的金红石氧化物，并提出有希望的帕累托最优候选者。所提出的工作流程可以扩展到在实验条件下探索其他工业催化剂。

更新日期：2022-08-05

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