Work

Supervised Machine Learning and Graph Neural Networks to Predict Collision Cross-Section Values of Aquatic Dissolved Organic Compounds

Year: 2026

Type: article

Source: Journal of the American Society for Mass Spectrometry

DOI: 10.1021/jasms.5c00276

Authors: Sadollah Ebrahimi, Louis Criqui, Armand Soldera, Céline Guéguen

Institutions: Université de Sherbrooke, Institut quantique

Language: English

Topic: Mass Spectrometry Techniques and Applications

Subfield: Spectroscopy

Field: Chemistry

Domain: Physical Sciences

Open Access status: closed

Funders: Canada Foundation for Innovation, Canada First Research Excellence Fund, Natural Sciences and Engineering Research Council of Canada, Fisheries and Oceans Canada, Division of Arctic Sciences

Award: G7666807661

OpenAlex ID: W7119516089

FWCI: 3.117

Cites: 44

Cited by: 2

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