金属氧化物对磷酸盐吸附的预测及分子机制

Prediction and molecular mechanism of phosphate adsorption by metal oxides

金属氧化物吸附剂由于其优异的性能和稳定性而成为有前景的磷酸盐去除材料。揭示纳米金属氧化物吸附磷酸盐的关键影响因素和吸附机制,对吸附磷酸盐金属基材料的设计至关重要.因此,本研究测定了32种金属氧化物的磷酸盐吸附量,通过ReliefF算法识别影响金属氧化物吸附磷酸盐的关键描述符,基于此建立了对磷酸盐吸附的预测模型,并采用密度泛函理论(density functional theory,DFT)模拟了磷酸盐在6种典型金属氧化物表面的吸附。结果显示,金属氧化物的电负性和阳离子电荷是决定磷酸盐吸附的关键因素采用6种机器学习算法构建了可筛选高吸附量金属氧化物的预测模型,决策树模型的训练集和验证集的预测准确度超过90%.DFT计算结果表明,金属原子d轨道与磷酸基团氧原子p轨道的相互作用是决定金属原子和氧原子成键的关键,阐明了金属原子d轨道能级决定磷酸基团吸附能的新机制.本研究所建立的预测模型可为筛选吸附磷酸盐的金属基材料提供依据,DFT计算揭示的分子机制可为吸附剂的设计提供理论基础.

Metal oxide adsorbents have emerged as promising phosphate removal materials due to their excellent adsorptionperformance,low price,and stability.However,the relations hip between the structure of metal oxides and the performanceof phosphate adsorption is still unclear,and the molecular mechanism of phosphate adsorption by metal oxides remains tobe elucidated.Understanding the major influencing factors and mechanisms of phosphate adsorption by metal oxides iscritical for the design of metal-based materials for phosphate adsorption.In this study,the kinetics and thermodynamics ofphosphate adsorption by Gd_(2)O_(3)and CeO_(2)were analyzed,and the phosphate adsorption capacity of 32 metal oxides wasdetermined.Relief Falgorithm was used to identify the key descriptors of metal oxides afecting phosphate adsorption.Based on the descriptors,prediction models for phosphate adsorption were developed based on six machine learningalgorithms,and the applicability domain of the models was characterized.Phosphate adsorption on the surface of sixtypical metal oxides was investigated by using molecular simulations based on density functional theory(DFT).The resultsdemonstrated that the maximum adsorption capacity of Gd_(2)O_(3)for phosphate was 4.14 times higher than that of CeO_(2).Thecrystal structure of Gd_(2)O_(3)was changed after adsorbing phosphate and resulted in Gd_(2)O_(3)transformation into urchin shapedstructures,but the crystal structure of CeO_(2)remained basiclly unchanged before and after adsorption.The results ofphosphate adsorption of 32 metal oxides showed that rare earth metal oxides(except CeO_(2))and ZnO had higher adsorptioncapacity than other metal oxides.The electronegativity and cationic charge of metal oxides were identified as twoimportant factors for phosphate adsorption using ReliefF algorithm.Six machine learning algorithms were utilized to buildthe prediction models for screening metal oxides with high adsorption capacity,and the predictive accuracy of the decisiontree model exceeded 90%for both training and test sets.Classification rules of the decision tree model indicated that metaloxides with electronegativity≤1.23 had high adsorption potential for phosphate.Characterization of the model'sapplicability domain showed that all metal oxides were within the applicability domain.DFT analysis showed thatadsorption energies of Gd_(2)O_(3)and Y_(2)O;for phosphate were-3.97 and-4.56 eV,which were higher than those of CeO_(2),NiO,ZnO and Cr_(2)O_(3)(-1.61-0.26 eV).The adsorption configuration of Gd_(2)O_(3)and Y_(2)O_(3)with higher adsorption energyfor phosphate was bidentate binuclear,while that of CeO_(2),NiO,ZnO and Cr_(2)O_(3)with lower adsorption energy wasmonodentate mononuclear.Combined with molecular orbital theory and density of states analysis,it was found that the dorbitals from Gd and Y atoms higher than the Fermi level interact with the p orbitals of the oxygen atom from thephosphate,splitting into antibonding orbitals higher than the Fermi level.Theoretical calculations revealed a newmechanism that the d orbitals energy level of metal atoms determines the phosphate adsorption energy.The predictionmodel established in this study can provide a basis for screening metal-based materials that can ffectively adsorbphosphate,and the molecular mechanism deciphered by DFT calculations can provide a theoretical basis for the design ofadsorbents.