A data-driven high-throughput workflow applied to promoted In-oxide catalysts for CO2 hydrogenation to methanol
| العنوان: | A data-driven high-throughput workflow applied to promoted In-oxide catalysts for CO2 hydrogenation to methanol |
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| المؤلفون: | Mohammad Khatamirad, Edvin Fako, Chiara Boscagli, Matthias Müller, Fabian Ebert, Raoul Naumann d'Alnoncourt, Ansgar Schaefer, Stephan Andreas Schunk, Ivana Jevtovikj, Frank Rosowski, Sandip De |
| المصدر: | Catalysis Science & Technology. 13:2656-2661 |
| بيانات النشر: | Royal Society of Chemistry (RSC), 2023. |
| سنة النشر: | 2023 |
| مصطلحات موضوعية: | Catalysis |
| الوصف: | To facilitate accelerated catalyst design, a combined computation and experimental workflow based on machine learning algorithms is proposed, which detects key performance-related descriptors in a CO2 to methanol reaction, for In2O3-based catalysts. |
| تدمد: | 2044-4761 2044-4753 |
| URL الوصول: | https://explore.openaire.eu/search/publication?articleId=doi_________::29f419d7d4dc5239da1e81f7355fa5ac https://doi.org/10.1039/d3cy00148b |
| حقوق: | OPEN |
| رقم الأكسشن: | edsair.doi...........29f419d7d4dc5239da1e81f7355fa5ac |
| قاعدة البيانات: | OpenAIRE |
| تدمد: | 20444761 20444753 |
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