Combination of Chromatographic and Machine Learning-Driven Virtual Fractionation Identifies Aryl Hydrocarbon Receptor Agonists in Sediments

Journal:

Environmental Science and Technology

Published:

2026-05-11

DOI:

10.1021/acs.est.6c02059

Affiliations:

5

Authors:

6

Go to article

Institutions	Authors	Share
Helmholtz Centre for Environmental Research (UFZ), Germany	4.000000	0.67
State Key Laboratory of Environmental Chemistry and Ecotoxicology (SKLECE), RCEES CAS, China	1.500000	0.25
University of Tübingen (Uni Tübingen), Germany	0.500000	0.08

Interested in Pollution and Contamination?