Prediction of arsenic adsorption onto metal organic frameworks and adsorption mechanisms interpretation by machine learning

Ting Xiong; Jiawen Cui; Zemin Hou; Xingzhong Yuan; Hou Wang; Jie Chen; Yi Yang; Yishi Huang; Xintao Xu; Changqing Su; Lijian Leng

doi:10.1016/j.jenvman.2023.119065

Prediction of arsenic adsorption onto metal organic frameworks and adsorption mechanisms interpretation by machine learning

J Environ Manage. 2023 Dec 1:347:119065. doi: 10.1016/j.jenvman.2023.119065. Epub 2023 Oct 4.

Authors

Ting Xiong¹, Jiawen Cui², Zemin Hou², Xingzhong Yuan³, Hou Wang³, Jie Chen², Yi Yang², Yishi Huang², Xintao Xu², Changqing Su⁴, Lijian Leng⁵

Affiliations

¹ School of Advanced Interdisciplinary Studies, Hunan University of Technology and Business, Changsha, 410205, China; Changsha Social Laboratory of Artificial Intelligence, Changsha, 410205, China.
² School of Advanced Interdisciplinary Studies, Hunan University of Technology and Business, Changsha, 410205, China.
³ College of Environmental Science and Engineering, Hunan University, Changsha, 410082, China.
⁴ Changsha Social Laboratory of Artificial Intelligence, Changsha, 410205, China; School of Resources and Environment, Hunan University of Technology and Business, Changsha, 410205, China.
⁵ School of Energy Science and Engineering, Central South University, Changsha, 410083, China. Electronic address: lljchs@126.com.

PMID: 37801942
DOI: 10.1016/j.jenvman.2023.119065

Abstract

Metal-organic frameworks (MOFs) are promising adsorbents for the removal of arsenic (As) from wastewater. The As removal efficiency is influenced by several factors, such as the textural properties of MOFs, adsorption conditions, and As species. Examining all of the relevant factors through traditional experiments is challenging. To predict the As adsorption capacities of MOFs toward organic, inorganic, and total As and reveal the adsorption mechanisms, four machine learning-based models were developed, with the adsorption conditions, MOF properties, and characteristics of different As species as inputs. The results demonstrated that the extreme gradient boosting (XGBoost) model exhibited the best predictive performance (test R² = 0.93-0.96). The validation experiments demonstrated the high accuracy of the inorganic As-based XGBoost model. The feature importance analysis showed that the concentration of As, the surface area of MOFs, and the pH of the solution were the three key factors governing inorganic-As adsorption, while those governing organic-As adsorption were the concentration of As, the pH_pzc value of MOFs, and the oxidation state of the metal clusters. The formation of coordination complexes between As and MOFs is possibly the major adsorption mechanism for both inorganic and organic As. However, electrostatic interaction may have a greater effect on organic-As adsorption than on inorganic-As adsorption. Overall, this study provides a new strategy for evaluating As adsorption on MOFs and discovering the underlying decisive factors and adsorption mechanisms, thereby facilitating the investigation of As wastewater treatment.

Keywords: Adsorption; Arsenic; Extreme gradient boosting (XGBoost); Machine learning; Metal organic frameworks.

MeSH terms

Adsorption
Arsenic*
Machine Learning
Metal-Organic Frameworks* / chemistry
Metals

Substances

Metal-Organic Frameworks
Arsenic
Metals