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 |
---|---|
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 |
---|