The CatMath: an online predictive platform for thermal + electrocatalysis
Frontiers of Chemical Science and Engineering, ISSN: 2095-0187, Vol: 17, Issue: 12, Page: 2156-2160
2023
- 9Citations
- 3Captures
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Article Description
The catalytic volcano activity models are the quantified and visualized tools of the Sabatier principle for heterogeneous catalysis, which can depict the intrinsic activity optima and trends of a catalytic reaction as a function of the reaction descriptors, i.e., the bonding strengths of key reaction species. These models can be derived by microkinetic modeling and/or free energy changes in combination with the scaling relations among the reaction intermediates. Herein, we introduce the CatMath—an online platform for generating a variety of common and industrially important thermal + electrocatalysis. With the CatMath, users can request the volcano models for available reactions and analyze their materials of interests as potential catalysts. Besides, the CatMath provides the function of the online generation of Surface Pourbaix Diagram for surface state analysis under electrocatalytic conditions, which is an essential step before analyzing the activity of an electrocatalytic surface. All the model generation and analysis processes are realized by cloud computing via a user-friendly interface.[Figure not available: see fulltext.].
Bibliographic Details
http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85177224061&origin=inward; http://dx.doi.org/10.1007/s11705-023-2371-3; https://link.springer.com/10.1007/s11705-023-2371-3; https://dx.doi.org/10.1007/s11705-023-2371-3; https://link.springer.com/article/10.1007/s11705-023-2371-3; http://sciencechina.cn/gw.jsp?action=cited_outline.jsp&type=1&id=7627732&internal_id=7627732&from=elsevier
Springer Science and Business Media LLC
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