Bbumba Simon, Ssekatawa John, Karume Ibrahim, Tebandeke Emmanuel, Kigozi Moses, Yiga Solomon, Setekera Robert, Ssebuliba Joseph, Sekitto Steven, Mbabazi Ruth, Kiganda Ivan, Kato Maximillian, Taremwa Patrick, Murungi Moses, Arum Chinaecherem Tochukwu, Letibo Collins Yiiki, Kaddu Geofrey, Namugwanya Margret, Kusasira John, Mwesigwa Peace, Ntale Muhammad
Department of Chemistry, College of Natural Sciences, Makerere University, P.O. Box 7062, Kampala, Uganda.
Department of Science, Faculty of Science and Computing, Ndejje University, P.O. Box 7088, Kampala, Uganda.
BMC Chem. 2025 Jul 22;19(1):218. doi: 10.1186/s13065-025-01590-3.
This study involved the chemical synthesis of Metal-organic Frameworks (MOFs). The synthesized MOFs were characterized using Scanning Electron Microscopy (SEM), Fourier Transform Infrared (FTIR), and Powder X-ray diffraction (PXRD). Artificial intelligence models such as Response Surface Methodology (RSM) and Artificial Neural Networks (ANN) were used to predict and optimize the adsorptive removal of Rhodamine B (RhB) from water. The adsorption process was optimized using RSM with a Central Composite Design (CCD), which predicted a maximum removal efficiency of 95.91% under the following conditions: initial dye concentration (10 mg/L), adsorbent dosage (15 mg), pH (6), and temperature (25 °C). ANN was also optimized using similar conditions and the resulting predictive removal efficiency of 97.18% was obtained. Non-linear isotherm studies strongly correlated with the Freundlich (R² = 0.9987) and Sips (R² = 0.9928) models, indicating multilayer and monolayer adsorption. Non-linear Pseudo-first-order, Pseudo-second-order, and Elovich model correlation coefficients of 0.9644, 0.9998, and 0.952 suggested that the mechanisms were by chemisorption and physisorption on energetically stable heterogeneous surfaces. The findings of this study show a dual approach based on metal-organic framework and machine learning models as efficient alternatives to understanding the removal of RhB from water.
本研究涉及金属有机框架材料（MOFs）的化学合成。使用扫描电子显微镜（SEM）、傅里叶变换红外光谱（FTIR）和粉末X射线衍射（PXRD）对合成的MOFs进行了表征。使用响应面法（RSM）和人工神经网络（ANN）等人工智能模型来预测和优化从水中吸附去除罗丹明B（RhB）的过程。使用带有中心复合设计（CCD）的RSM对吸附过程进行了优化，预测在以下条件下最大去除效率为95.91%：初始染料浓度（10 mg/L）、吸附剂用量（15 mg）、pH值（6）和温度（25°C）。ANN也在类似条件下进行了优化，得到的预测去除效率为97.18%。非线性等温线研究与Freundlich（R² = 0.9987）和Sips（R² = 0.9928）模型高度相关，表明存在多层和单层吸附。非线性拟一级、拟二级和Elovich模型的相关系数分别为0.9644、0.9998和0.952，表明其机制是在能量稳定的异质表面上通过化学吸附和物理吸附进行的。本研究结果表明，基于金属有机框架和机器学习模型的双重方法是理解从水中去除RhB的有效替代方法。
