Machine-learning-assisted screening of pure-silica zeolites for effective removal of linear siloxanes and derivatives. Issue 6 (23rd January 2020)

• Record Type: Journal Article
• Authors: Lin, Shiru; Wang, Yekun; Zhao, Yinghe; Pericchi, Luis R.; Hernández-Maldonado, Arturo J.; Chen, Zhongfang
• Is Part Of: Journal of materials chemistry. Volume 8:Issue 6(2020)
• Page Start: 3228
• Page End: 3237
• Publication Date: 2020-01-23
• Subjects: Materials -- Research -- Periodicals; Chemistry, Analytic -- Periodicals; Environmental sciences -- Research -- Periodicals; 543.0284
• DOI: 10.1039/c9ta11909d
• Languages: English
• ISSNs: 2050-7488

Abstract

Abstract : By a two-step computational process, namely Grand Canonical Monte Carlo (GCMC) simulations and machine learning (ML), we screened 50 959 hypothetical pure-silica zeolites and identified 230 preeminent zeolites with excellent adsorption performances. Abstract : As emerging organic contaminants, siloxanes have severe impacts on the environment and human health. Simple linear siloxanes and derivates, trimethylsilanol (TMS), dimethylsilanediol (DMSD), monomethylsilanetriol (MMST), and dimethylsulfone (DMSO2 ), are four persistent and common problematic compounds (PCs) from the hydroxylation and sulfuration of polydimethylsiloxanes. Herein, through a two-step computational process, namely Grand Canonical Monte Carlo (GCMC) simulations and machine learning (ML), we systematically screened 50 959 hypothetical pure-silica zeolites and identified 230 preeminent zeolites with excellent adsorption performances with all these four linear siloxanes and derivates. This work vividly demonstrates that the collocation of data-driven science and computational chemistry can greatly accelerate materials discovery and help solve the most challenging separation problems in environmental science.