Reliable prediction and rational design of highly efficient adsorbents for cesium removal from wastewater via a generalizable stacking-based machine learning framework
- DOI
- 10.1016/j.ceja.2026.101373
- Published
- 2026-08
- Container
- Chemical Engineering Journal Advances
- Publisher
- Elsevier BV
- Open access
- unknown
Credibility signals
uncertain Score 64/100 under policy 1.0.0. This is a metadata assessment, not a judgment of the paper's conclusions.
Show all credibility signals
- supportingDOI registered: A matching record was returned by Crossref.
- supportingDOI resolves: A matching record was returned by Crossref.
- not scoredDirectory of Open Access Journals: No matching DOAJ record was present in this response. No allow-list match; this is not evidence of low credibility.
- not scoredMEDLINE indexed: Not checked or no result supplied; no credibility inference made.
- not scoredOpenAlex core source: Not checked or no result supplied; no credibility inference made.
- not scoredKnown publisher allow-list: Not checked or no result supplied; no credibility inference made.
- not scoredROR affiliation: Not checked or no result supplied; no credibility inference made.
- not scoredRetraction Watch retraction: No retraction notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredRetraction Watch expression of concern: No expression of concern notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredRetraction Watch correction: No correction notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredRetraction Watch reinstatement: No reinstatement notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredOpen access status: Not checked or no result supplied; no credibility inference made.
- not scoredPublication license: Not checked or no result supplied; no credibility inference made.
- not scoredPublication version: A publication version was supplied but is not scored.
- supportingMetadata completeness: All 6 scored descriptive metadata groups are present.
Cite this work
BibTeX
@article{allodium:10.1016/j.ceja.2026.101373,
title = {Reliable prediction and rational design of highly efficient adsorbents for cesium removal from wastewater via a generalizable stacking-based machine learning framework},
author = {Jingran Yang and Jingshen Zhang and Ying Zhou and Diyun Chen and Hui Sun and Lingjun Kong and Minhua Su},
year = {2026},
journal = {Chemical Engineering Journal Advances},
doi = {10.1016/j.ceja.2026.101373},
url = {https://doi.org/10.1016/j.ceja.2026.101373}
}RIS
TY - JOUR TI - Reliable prediction and rational design of highly efficient adsorbents for cesium removal from wastewater via a generalizable stacking-based machine learning framework AU - Jingran Yang AU - Jingshen Zhang AU - Ying Zhou AU - Diyun Chen AU - Hui Sun AU - Lingjun Kong AU - Minhua Su PY - 2026 JO - Chemical Engineering Journal Advances DO - 10.1016/j.ceja.2026.101373 UR - https://doi.org/10.1016/j.ceja.2026.101373 ER -
APA
Yang, J., Zhang, J., Zhou, Y., Chen, D., Sun, H., Kong, L., & Su, M. (2026). Reliable prediction and rational design of highly efficient adsorbents for cesium removal from wastewater via a generalizable stacking-based machine learning framework. Chemical Engineering Journal Advances. https://doi.org/10.1016/j.ceja.2026.101373
Source records
- crossref · retrieved 2026-11-10T19:11:45.402Z