充分发挥机器学习在减轻功能材料对环境影响方面的潜力

IF 6.1 3区 环境科学与生态学 Q1 ENVIRONMENTAL SCIENCES Reviews of environmental contamination and toxicology Pub Date : 2022-12-01 DOI:10.1007/s44169-022-00024-8
Ying He, Guohong Liu, Chengjun Li, Xiliang Yan
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Reaching the Full Potential of Machine Learning in Mitigating Environmental Impacts of Functional Materials
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来源期刊
CiteScore
12.80
自引率
0.00%
发文量
11
审稿时长
>24 weeks
期刊介绍: Reviews of Environmental Contamination and Toxicology publishes reviews pertaining to the sources, transport, fate and effects of contaminants in the environment. The journal provides a place for the publication of critical reviews of the current knowledge and understanding of environmental sciences in order to provide insight into contaminant pathways, fate and behavior in environmental compartments and the possible consequences of their presence, with multidisciplinary contributions from the fields of analytical chemistry, biochemistry, biology, ecology, molecular and cellular biology (in an environmental context), and human, wildlife and environmental toxicology. •Standing on a 55+ year history of publishing environmental toxicology reviews •Now publishing in journal format boasting rigorous review and expanded editorial board •Publishing home for extensive environmental reviews dealing with sources, transport, fate and effect of contaminants •Through Springer Compact agreements, authors from participating institutions can publish Open Choice at no cost to the authors
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