High-Throughput Computational Screening and Machine Learning Model for Accelerated Metal–Organic Frameworks Discovery in Toluene Vapor Adsorption

甲苯 吸附 金属有机骨架 重量分析 化学工程 材料科学 金属 化学 有机化学 工程类
作者
Xiaohua Liu,Ruihan Wang,Xin Wang,Dingguo Xu
出处
期刊:Journal of Physical Chemistry C [American Chemical Society]
卷期号:127 (23): 11268-11282 被引量:6
标识
DOI:10.1021/acs.jpcc.3c01749
摘要

Some hazardous gases, like toluene vapor, have caused serious environmental pollution. The adsorption of toluene using metal–organic frameworks (MOFs) has been considered a useful mechanism to reduce environmental pollution. High-throughput computation using the grand canonical Monte Carlo (GCMC) approach was used to screen high-performance MOFs from the CoRE MOF database. A total of 802 MOFs are selected with a toluene uptake larger than HKUST-1 (6.34 mmol/g at 1900 Pa, 298 K) and CMOF-3b ([(L3b)Cu2]n, L3b = 4,4′,4″,4‴-(2,2′-dihydroxy-1,1′-binaphthalene-4,4′,6,6′-tetrayl)), showing the highest toluene vapor adsorption capacity (25.57 mmol/g). Approximately 80% of high-performance MOFs contain open metal sites. Further analyses of the quantitative structure–property relationships reveal that the MOF adsorption capacity for toluene could be primarily correlated with gravimetric surface area and void fraction. Moreover, using the HKUST-1 as a template, center-metal element replacement is suggested to be effective in improving toluene vapor adsorption. Finally, based on our previously proposed MOF-CGCNN algorithm, a regression model is developed to predict toluene adsorption capacity. Combined with high-throughput GCMC calculation, the machine learning model is applied to screen a larger MOF database (containing 137,953 hypothetical MOFs), which accelerates the virtual discovery of new high-performance candidate MOFs for toluene adsorption. The proposed strategy will be useful in material design or discovery for reducing toluene, thereby benefitting the environment.
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
lababala完成签到 ,获得积分10
刚刚
星辰大海应助科研通管家采纳,获得10
1秒前
1秒前
大个应助笑点低诗桃采纳,获得10
1秒前
平常安完成签到,获得积分10
1秒前
2秒前
小二郎应助科研通管家采纳,获得10
2秒前
烟花应助科研通管家采纳,获得10
2秒前
2秒前
Hello应助科研通管家采纳,获得10
3秒前
Owen应助科研通管家采纳,获得10
3秒前
3秒前
学渣一枚发布了新的文献求助10
3秒前
3秒前
烟花应助科研通管家采纳,获得10
3秒前
共享精神应助科研通管家采纳,获得10
4秒前
sasa完成签到 ,获得积分10
4秒前
小二郎应助科研通管家采纳,获得10
4秒前
4秒前
4秒前
4秒前
4秒前
5秒前
morena发布了新的文献求助10
6秒前
深情安青应助量子星尘采纳,获得30
7秒前
优秀的服饰完成签到 ,获得积分10
7秒前
mm完成签到,获得积分10
7秒前
SciGPT应助量子星尘采纳,获得30
8秒前
zjs完成签到,获得积分10
8秒前
万能图书馆应助量子星尘采纳,获得10
8秒前
空曲完成签到 ,获得积分10
9秒前
Mr.Cletus发布了新的文献求助10
9秒前
LANzzy完成签到,获得积分10
10秒前
牛文文发布了新的文献求助10
12秒前
凹凸曼发布了新的文献求助30
13秒前
13秒前
Alma完成签到 ,获得积分10
15秒前
DD应助小西贝采纳,获得10
16秒前
852应助量子星尘采纳,获得10
17秒前
汉堡包应助展希希采纳,获得10
17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 3989469
求助须知:如何正确求助?哪些是违规求助? 3531722
关于积分的说明 11254409
捐赠科研通 3270221
什么是DOI,文献DOI怎么找? 1804946
邀请新用户注册赠送积分活动 882113
科研通“疑难数据库(出版商)”最低求助积分说明 809176