School of Engineering and Built Environment, Griffith University, Nathan, QLD, 4111, Australia.
School of Environment and Science, Griffith University, Nathan, QLD, 4111, Australia.
Chemosphere. 2023 Oct;339:139674. doi: 10.1016/j.chemosphere.2023.139674. Epub 2023 Jul 28.
This comprehensive study analysed 55 articles published between 2011 and 2022 on the use of metal organic frameworks (MOFs) for phosphate adsorption. The study found that the performance of MOFs in phosphate adsorption is influenced by various factors such as the type of MOF, synthesis method, modification/alteration, and operational conditions (initial concentration, adsorbent dose, pH, contact time, and temperature). Most of the MOFs have a wide range of theoretical maximum adsorption capacity for phosphate, but their long-term use in phosphorus recovery may be limited due to the adsorption mechanisms being dominated by inner sphere complexation. The study employed machine learning to construct artificial neural network (ANN) models for predicting phosphate adsorption capacity based on input features from operation and synthesis procedures. The initial phosphate concentration was the most important input from the operational features, while the modulator agent was consistently relevant during MOF synthesis. The models showed strong fitting for most MOF types recorded for the study, such as UIO-66, MIL-100, ZIF-8, Al-MOFs, La-MOFs, and Ce-MOFs. Overall, this study provides valuable insights for the design of MOF adsorbents for phosphate adsorption and offers guidance for future research in this area.
这项全面研究分析了 2011 年至 2022 年间发表的 55 篇关于金属有机骨架（MOFs）用于磷酸盐吸附的文章。研究发现，MOFs 在磷酸盐吸附中的性能受到多种因素的影响，例如 MOF 的类型、合成方法、修饰/改变以及操作条件（初始浓度、吸附剂剂量、pH 值、接触时间和温度）。大多数 MOFs 对磷酸盐的理论最大吸附容量范围很广，但由于吸附机制主要受内球络合的影响，它们在磷回收中的长期使用可能受到限制。该研究采用机器学习构建人工神经网络（ANN）模型，根据操作和合成过程的输入特征来预测磷酸盐的吸附能力。初始磷酸盐浓度是来自操作特性的最重要输入，而在 MOF 合成过程中，调节剂始终是相关的。该模型对研究中记录的大多数 MOF 类型（如 UIO-66、MIL-100、ZIF-8、Al-MOFs、La-MOFs 和 Ce-MOFs）都表现出很强的拟合性。总的来说，这项研究为设计用于磷酸盐吸附的 MOF 吸附剂提供了有价值的见解，并为该领域的未来研究提供了指导。
