Interpretable machine learning for elucidating component synergy in a solid waste-derived multicomponent adsorbent for heavy metal removal.
- DOI
- 10.1016/j.envres.2026.125517
- Published
- 2026 Oct 1
- Container
- Environmental research
- Publisher
- Not recorded
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1016/j.envres.2026.125517,
title = {Interpretable machine learning for elucidating component synergy in a solid waste-derived multicomponent adsorbent for heavy metal removal.},
author = {Zhao M and Zuo D and Tong Y and Tian S and Zhao X and Zhang Q and Shao H and Zhu Z and Meng Z},
year = {2026},
journal = {Environmental research},
doi = {10.1016/j.envres.2026.125517},
url = {https://doi.org/10.1016/j.envres.2026.125517}
}RIS
TY - JOUR TI - Interpretable machine learning for elucidating component synergy in a solid waste-derived multicomponent adsorbent for heavy metal removal. AU - Zhao M AU - Zuo D AU - Tong Y AU - Tian S AU - Zhao X AU - Zhang Q AU - Shao H AU - Zhu Z AU - Meng Z PY - 2026 JO - Environmental research DO - 10.1016/j.envres.2026.125517 UR - https://doi.org/10.1016/j.envres.2026.125517 ER -
APA
M, Z., D, Z., Y, T., S, T., X, Z., Q, Z., H, S., Z, Z., & Z, M. (2026). Interpretable machine learning for elucidating component synergy in a solid waste-derived multicomponent adsorbent for heavy metal removal.. Environmental research. https://doi.org/10.1016/j.envres.2026.125517
Source records
- pubmed · retrieved 2026-09-25T07:48:24.088Z