School of Advanced Interdisciplinary Studies, Hunan University of Technology and Business, Changsha, 410205, China; Changsha Social Laboratory of Artificial Intelligence, Changsha, 410205, China.
School of Advanced Interdisciplinary Studies, Hunan University of Technology and Business, Changsha, 410205, China.
J Environ Manage. 2023 Dec 1;347:119065. doi: 10.1016/j.jenvman.2023.119065. Epub 2023 Oct 4.
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 R = 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 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.
金属有机骨架（MOFs）是一种很有前途的吸附剂，可以用来去除废水中的砷（As）。砷的去除效率受到多种因素的影响，例如 MOFs 的结构特性、吸附条件和砷的形态。通过传统实验来研究所有相关因素具有挑战性。为了预测 MOFs 对有机砷、无机砷和总砷的吸附能力，并揭示吸附机制，我们开发了四个基于机器学习的模型，以吸附条件、MOF 特性和不同砷形态的特征作为输入。结果表明，极端梯度提升（XGBoost）模型表现出最好的预测性能（测试 R=0.93-0.96）。验证实验证明了基于无机砷的 XGBoost 模型具有很高的准确性。特征重要性分析表明，砷的浓度、MOFs 的比表面积和溶液的 pH 值是影响无机砷吸附的三个关键因素，而影响有机砷吸附的因素则是砷的浓度、MOFs 的 pH 值和金属簇的氧化态。砷与 MOFs 之间形成配位络合物可能是无机砷和有机砷吸附的主要吸附机制。然而，静电相互作用可能对有机砷吸附的影响比对无机砷吸附的影响更大。总的来说，本研究为评估 MOFs 对砷的吸附提供了一种新策略，并发现了潜在的决定性因素和吸附机制，从而促进了对含砷废水处理的研究。
