Dataset for Parametric Uncertainty in Microkinetic Predictions of Dynamic Rate Enhancement
收藏DataCite Commons2025-06-23 更新2026-04-25 收录
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https://hdl.handle.net/11299/272862
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资源简介：
The study incorporates linear scaling relations and Brønsted-Evans-Polanyi relations to model the behavior of programmable catalysts. Two case studies (CaseStudy1 and CaseStudy2) use Monte Carlo simulations and global sensitivity analysis to quantify model uncertainty and identify key parameters driving performance variations. The first case study involve a generic prototype reaction and the second case study focuses on the oxygen evolution reaction (OER). This dataset contains the raw data used in the study of Parametric Uncertainty in Microkinetic Predictions of Dynamic Rate Enhancement.
提供机构：
Data Repository for the University of Minnesota (DRUM)
创建时间：
2025-06-23
