An interpretable machine learning framework for predicting and analyzing arsenic adsorption on metal-modified biochar
Crossref DOI link: https://doi.org/10.1007/s42452-026-08281-1
Published Online: 2026-01-25
Update policy: https://doi.org/10.1007/springer_crossmark_policy
Tang, Ziwei
Liang, Bo
Text and Data Mining valid from 2026-01-25
Accepted Manuscript valid from 2026-01-25
Article History
Received: 24 November 2025
Accepted: 18 January 2026
First Online: 25 January 2026
Declarations
:
: Not applicable.
: The authors declare no competing interests.