已入深夜,您辛苦了!由于当前在线用户较少,发布求助请尽量完整地填写文献信息,科研通机器人24小时在线,伴您度过漫漫科研夜!祝你早点完成任务,早点休息,好梦!

Data-Driven Inference of Synthesis Guidelines for High-Performance Zeolite-Based Selective Catalytic Reduction Catalysts at Low Temperatures

催化作用 沸石 还原(数学) 推论 选择性催化还原 材料科学 化学工程 化学 计算机科学 有机化学 工程类 数学 人工智能 几何学
作者
Shinyoung Bae,Hwangho Lee,Jun‐Seop Shin,Hyun Sub Kim,Yeonsoo Kim,Do Heui Kim,Jong Min Lee
出处
期刊:Chemistry of Materials [American Chemical Society]
卷期号:34 (17): 7761-7773 被引量:6
标识
DOI:10.1021/acs.chemmater.2c01092
摘要

Numerous zeolite-based selective catalytic reduction (SCR) catalysts have been investigated to improve nitrogen oxide (NOx) removal efficiency at low temperatures of 25–200 °C in diesel vehicles. However, the majority of these studies examined only one of each feature's effects. The catalysis mechanism consists of complex reactions, and the various features interact, making it difficult to predict their combinatorial effects on the catalytic activity. Recently, machine learning-based models have been widely employed in catalysis science to infer hidden information about catalysts without knowledge of the underlying physical principles. Interpretable machine learning models are particularly useful for catalyst research because they can explain the causal relationship between characteristics and catalytic performance. In this study, we construct a machine learning model utilizing a decision tree, one of the representative interpretable machine learning models. Using this model, we evaluate the causal relationship between features and the NOx removal efficiency of zeolite-based SCR catalysts at low temperatures, which is difficult to deduce due to the high number of features. Additionally, we extract several synthesis guidelines for catalysts that show superior NOx removal performance at low temperatures. New catalysts were synthesized using the proposed rules, and their performance was validated experimentally.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
更新
PDF的下载单位、IP信息已删除 (2025-6-4)

科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
Beyond完成签到,获得积分10
1秒前
脑洞疼应助开放芷天采纳,获得10
1秒前
大个应助漂亮白枫采纳,获得10
5秒前
zjgjnu完成签到,获得积分10
5秒前
6秒前
9秒前
9秒前
Karol发布了新的文献求助10
9秒前
桐桐应助zzzkyt采纳,获得10
11秒前
陶然共忘机完成签到 ,获得积分10
11秒前
ohooo完成签到,获得积分10
12秒前
12秒前
开放芷天发布了新的文献求助10
14秒前
15秒前
15秒前
彭于晏应助大面包采纳,获得10
17秒前
18秒前
ohooo发布了新的文献求助10
19秒前
sean发布了新的文献求助20
19秒前
20秒前
子阅完成签到 ,获得积分10
20秒前
雷家发布了新的文献求助10
20秒前
zzzkyt发布了新的文献求助10
22秒前
小蘑菇应助dapis采纳,获得10
23秒前
SciGPT应助Steven采纳,获得10
23秒前
研友_LNM558发布了新的文献求助50
26秒前
26秒前
26秒前
桐桐应助orange9采纳,获得10
26秒前
缥缈的松鼠完成签到 ,获得积分10
27秒前
27秒前
27秒前
30秒前
大面包发布了新的文献求助10
31秒前
恋雅颖月应助方睿智采纳,获得10
37秒前
Sophia发布了新的文献求助10
37秒前
38秒前
zgl完成签到,获得积分10
39秒前
易寒完成签到,获得积分10
40秒前
41秒前
高分求助中
A new approach to the extrapolation of accelerated life test data 1000
ACSM’s Guidelines for Exercise Testing and Prescription, 12th edition 500
‘Unruly’ Children: Historical Fieldnotes and Learning Morality in a Taiwan Village (New Departures in Anthropology) 400
Indomethacinのヒトにおける経皮吸収 400
Phylogenetic study of the order Polydesmida (Myriapoda: Diplopoda) 370
基于可调谐半导体激光吸收光谱技术泄漏气体检测系统的研究 350
Robot-supported joining of reinforcement textiles with one-sided sewing heads 320
热门求助领域 (近24小时)
化学 材料科学 医学 生物 工程类 有机化学 生物化学 物理 内科学 纳米技术 计算机科学 化学工程 复合材料 遗传学 基因 物理化学 催化作用 冶金 细胞生物学 免疫学
热门帖子
关注 科研通微信公众号,转发送积分 3989857
求助须知:如何正确求助?哪些是违规求助? 3531994
关于积分的说明 11255679
捐赠科研通 3270758
什么是DOI,文献DOI怎么找? 1805053
邀请新用户注册赠送积分活动 882195
科研通“疑难数据库(出版商)”最低求助积分说明 809208