Prediction of the Non-Reducing Biomineralization of Nuclide–Microbial Interactions by Machine Learning: The Case of Uranium and <i>Bacillus subtilis</i>

Prediction of the Non-Reducing Biomineralization of Nuclide–Microbial Interactions by Machine Learning: The Case of Uranium and <i>Bacillus subtilis</i>

<i>Bacillus subtilis</i> exhibits a great affinity to soluble U(VI) through non-reducing biomineralization. The pH value, temperature, initial uranium concentration, bacterial concentration, and adsorption time are recognized as the five environmental sensitive factors that can regulate...

Full description

Saved in:
Bibliographic Details
Main Authors: Shirong Qiang, Leijin Liu, Siqi Li, Shuang Wang, Xinyang Huang, Jiaxin Yang, Jiayu Song, Yue Zhang, Yongxiang Huang, Qiaohui Fan
Format: Article
Language:English
Published: MDPI AG 2025-04-01
Series:Toxics
Subjects:
uranium
<i>Bacillus subtilis</i>
machine learning
non-reducing biomineralization
Online Access:https://www.mdpi.com/2305-6304/13/4/305
Tags: Add Tag
No Tags, Be the first to tag this record!

Internet

https://www.mdpi.com/2305-6304/13/4/305

Similar Items