Deep Learning in Environmental Toxicology: Current Progress and Open Challenges-Reference-Cited by-同舟云学术

Deep Learning in Environmental Toxicology: Current Progress and Open Challenges

Published:2023-06-20 Issue: Volume: Page:
ISSN:2690-0637
Container-title:ACS ES&T Water
language:en
Short-container-title:ACS EST Water

Author:

Tan Haoyue¹²^ORCID,Jin Jinsha¹,Fang Chao³^ORCID,Zhang Ying¹,Chang Baodi¹,Zhang Xiaowei¹²^ORCID,Yu Hongxia¹²,Shi Wei¹²^ORCID

Affiliation:

1. State Key Laboratory of Pollution Control and Resources Reuse, School of Environment, Nanjing University, Nanjing 210023, Jiangsu, China

2. Jiangsu Province Ecology and Environment Protection Key Laboratory of Chemical Safety and Health Risk, Nanjing 210023, Jiangsu, China

3. National Engineering Research Centre of Energy-Efficient Semi-conductor Devices and Materials, School of Electronic Science and Engineering, Nanjing University, Nanjing 210023, Jiangsu, China

Funder

Jiangsu Provincial Key Research and Development Program

National Key Research and Development Program of China

Natural Science Foundation of Jiangsu Province for Distinguished Young Scholars

Fundamental Research Funds for the Central Universities

National Natural Science Foundation of China

Environmental Protection Research Project of Jiangsu Province

Publisher

American Chemical Society (ACS)

Subject

Water Science and Technology,Environmental Chemistry,Chemistry (miscellaneous),Chemical Engineering (miscellaneous)

Link

https://pubs.acs.org/doi/pdf/10.1021/acsestwater.3c00152

Reference156 articles.

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