Using machine learning and quantum chemistry descriptors to predict the toxicity of ionic liquids-Reference-Cited by-同舟云学术

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Using machine learning and quantum chemistry descriptors to predict the toxicity of ionic liquids

Published:2018-06 Issue: Volume:352 Page:17-26
ISSN:0304-3894
Container-title:Journal of Hazardous Materials
language:en
Short-container-title:Journal of Hazardous Materials

Author:

Cao Lingdi,Zhu Peng,Zhao Yongsheng,Zhao Jihong

Funder

China Postdoctoral Science Foundation

Intergovernmental International Scientific and Technological Cooperation of Shanghai

Publisher

Elsevier BV

Subject

Health, Toxicology and Mutagenesis,Pollution,Waste Management and Disposal,Environmental Chemistry,Environmental Engineering

Reference56 articles.

1. Peer Reviewed: Design Through the 12 Principles of Green Engineering;Anastas,2003

2. Carbon capture with ionic liquids: overview and progress;Zhang;Energy Environ. Sci.,2012

3. Equimolar CO2 absorption by anion-functionalized ionic liquids;Gurkan;J. Am. Chem. Soc.,2010

4. Imidazole tailored deep eutectic solvents for CO2 capture enhanced by hydrogen bond;Cao;Phys. Chem. Chem. Phys.,2015

5. Feasible ionic liquid-amine hybrid solvents for carbon dioxide capture;Cao;Int. J. Greenh. Gas Control,2017

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