Qiang Zhang , Yuanhao Wang , Jia Zhang , Yang Yue , Guangren Qian
{"title":"应用机器学习设计和理解氮氧化物选择性催化还原的有效催化剂","authors":"Qiang Zhang , Yuanhao Wang , Jia Zhang , Yang Yue , Guangren Qian","doi":"10.1016/j.apcata.2024.119825","DOIUrl":null,"url":null,"abstract":"<div><p>Traditional catalyst design includes trial-and-error and orthogonal methods. However, these processes usually require large number of experiments to get an optimized formula. Machine learning was applied in designing effective catalyst for selective catalytic reduction (SCR) of nitrogen oxide (NOx). Catalyst formulas and their activities in previous reports were collected and fitted by extreme gradient boosting algorithm and explanatory algorithm-SHapley Additive exPlanations. Mn-Cr coupling was predicted to be the most effective for SCR among various couplings, which was then proved by experimental results. SCR activity of Mn catalyst was increased from 50.3 % to 85.0 % at 150°C after the catalyst was loaded by Cr. Furthermore, machine learning and experimental characterizations revealed that the big total electronegativity of Cr resulted in bidentate nitrate bonding one cation with two oxygens, which was the most active NOx-derived intermediate during SCR. This work is in favor of catalyst design and catalytic-species recognition at the same time.</p></div>","PeriodicalId":243,"journal":{"name":"Applied Catalysis A: General","volume":null,"pages":null},"PeriodicalIF":4.7000,"publicationDate":"2024-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Application of machine learning in designing and understanding effective catalyst for selective catalytic reduction of nitrogen oxide\",\"authors\":\"Qiang Zhang , Yuanhao Wang , Jia Zhang , Yang Yue , Guangren Qian\",\"doi\":\"10.1016/j.apcata.2024.119825\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>Traditional catalyst design includes trial-and-error and orthogonal methods. However, these processes usually require large number of experiments to get an optimized formula. Machine learning was applied in designing effective catalyst for selective catalytic reduction (SCR) of nitrogen oxide (NOx). Catalyst formulas and their activities in previous reports were collected and fitted by extreme gradient boosting algorithm and explanatory algorithm-SHapley Additive exPlanations. Mn-Cr coupling was predicted to be the most effective for SCR among various couplings, which was then proved by experimental results. SCR activity of Mn catalyst was increased from 50.3 % to 85.0 % at 150°C after the catalyst was loaded by Cr. Furthermore, machine learning and experimental characterizations revealed that the big total electronegativity of Cr resulted in bidentate nitrate bonding one cation with two oxygens, which was the most active NOx-derived intermediate during SCR. This work is in favor of catalyst design and catalytic-species recognition at the same time.</p></div>\",\"PeriodicalId\":243,\"journal\":{\"name\":\"Applied Catalysis A: General\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":4.7000,\"publicationDate\":\"2024-05-31\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Applied Catalysis A: General\",\"FirstCategoryId\":\"1\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0926860X24002709\",\"RegionNum\":2,\"RegionCategory\":\"化学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"CHEMISTRY, PHYSICAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Applied Catalysis A: General","FirstCategoryId":"1","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0926860X24002709","RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"CHEMISTRY, PHYSICAL","Score":null,"Total":0}
Application of machine learning in designing and understanding effective catalyst for selective catalytic reduction of nitrogen oxide
Traditional catalyst design includes trial-and-error and orthogonal methods. However, these processes usually require large number of experiments to get an optimized formula. Machine learning was applied in designing effective catalyst for selective catalytic reduction (SCR) of nitrogen oxide (NOx). Catalyst formulas and their activities in previous reports were collected and fitted by extreme gradient boosting algorithm and explanatory algorithm-SHapley Additive exPlanations. Mn-Cr coupling was predicted to be the most effective for SCR among various couplings, which was then proved by experimental results. SCR activity of Mn catalyst was increased from 50.3 % to 85.0 % at 150°C after the catalyst was loaded by Cr. Furthermore, machine learning and experimental characterizations revealed that the big total electronegativity of Cr resulted in bidentate nitrate bonding one cation with two oxygens, which was the most active NOx-derived intermediate during SCR. This work is in favor of catalyst design and catalytic-species recognition at the same time.
期刊介绍:
Applied Catalysis A: General publishes original papers on all aspects of catalysis of basic and practical interest to chemical scientists in both industrial and academic fields, with an emphasis onnew understanding of catalysts and catalytic reactions, new catalytic materials, new techniques, and new processes, especially those that have potential practical implications.
Papers that report results of a thorough study or optimization of systems or processes that are well understood, widely studied, or minor variations of known ones are discouraged. Authors should include statements in a separate section "Justification for Publication" of how the manuscript fits the scope of the journal in the cover letter to the editors. Submissions without such justification will be rejected without review.