Machine learning-driven high-throughput prediction of Hg<sup>0</sup> capture in MOFs and decipherment of structure-property relationships.
- DOI
- 10.1016/j.jhazmat.2026.143910
- Published
- 2026-10-13
- Container
- J Hazard Mater
- Publisher
- Not recorded
- Open access
- no
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Cite this work
BibTeX
@article{allodium:10.1016/j.jhazmat.2026.143910,
title = {Machine learning-driven high-throughput prediction of Hg\<sup\>0\</sup\> capture in MOFs and decipherment of structure-property relationships.},
author = {Zhang Z and Jia L and Cheng P and Wu YW and Li ZK and Li ZB and Fan CH and Yu Y.},
year = {2026},
journal = {J Hazard Mater},
doi = {10.1016/j.jhazmat.2026.143910},
url = {https://doi.org/10.1016/j.jhazmat.2026.143910}
}RIS
TY - JOUR TI - Machine learning-driven high-throughput prediction of Hg<sup>0</sup> capture in MOFs and decipherment of structure-property relationships. AU - Zhang Z AU - Jia L AU - Cheng P AU - Wu YW AU - Li ZK AU - Li ZB AU - Fan CH AU - Yu Y. PY - 2026 JO - J Hazard Mater DO - 10.1016/j.jhazmat.2026.143910 UR - https://doi.org/10.1016/j.jhazmat.2026.143910 ER -
APA
Z, Z., L, J., P, C., YW, W., ZK, L., ZB, L., CH, F., & Y., Y. (2026). Machine learning-driven high-throughput prediction of Hg<sup>0</sup> capture in MOFs and decipherment of structure-property relationships.. J Hazard Mater. https://doi.org/10.1016/j.jhazmat.2026.143910
Source records
- europe-pmc · retrieved 2026-11-10T19:02:38.126Z