Application of Life Cycle Assessment and Machine Learning for High-Throughput Screening of Green Chemical Substitutes-Reference-Cited by-同舟云学术

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Application of Life Cycle Assessment and Machine Learning for High-Throughput Screening of Green Chemical Substitutes

Published:2020-07-23 Issue:30 Volume:8 Page:11141-11151
ISSN:2168-0485
Container-title:ACS Sustainable Chemistry & Engineering
language:en
Short-container-title:ACS Sustainable Chem. Eng.

Author:

Zhu Xinzhe¹,Ho Chi-Hung¹,Wang Xiaonan¹^ORCID

Affiliation:

1. Department of Chemical and Biomolecular Engineering, National University of Singapore, 4 Engineering Drive 4, Singapore 117585

Funder

Ministry of Education - Singapore

Publisher

American Chemical Society (ACS)

Subject

Renewable Energy, Sustainability and the Environment,General Chemical Engineering,Environmental Chemistry,General Chemistry

Link

https://pubs.acs.org/doi/pdf/10.1021/acssuschemeng.0c02211

Reference49 articles.

1. Green chemistry

2. Green Chemistry: Science and Politics of Change

3. High-throughput screening of chemicals as functional substitutes using structure-based classification models

4. Green chemistry tools to influence a medicinal chemistry and research chemistry based organisation

5. Life cycle assessment of fine chemical production: a case study of pharmaceutical synthesis

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