Prediction and molecular mechanism of phosphate adsorption by metal oxides

Metal oxide adsorbents have emerged as promising phosphate removal materials due to their excellent adsorption performance, low price, and stability. However, the relationship between the structure of metal oxides and the performance of phosphate adsorption is still unclear, and the molecular mechanism of phosphate adsorption by metal oxides remains to be elucidated. Understanding the major influencing factors and mechanisms of phosphate adsorption by metal oxides is critical for the design of metal-based materials for phosphate adsorption. In this study, the kinetics and thermodynamics of phosphate adsorption by Gd2O3 and CeO2 were analyzed, and the phosphate adsorption capacity of 32 metal oxides was determined. ReliefF algorithm was used to identify the key descriptors of metal oxides affecting phosphate adsorption. Based on the descriptors, prediction models for phosphate adsorption were developed based on six machine learning algorithms, and the applicability domain of the models was characterized. Phosphate adsorption on the surface of six typical metal oxides was investigated by using molecular simulations based on density functional theory (DFT). The results demonstrated that the maximum adsorption capacity of Gd2O3 for phosphate was 4.14 times higher than that of CeO2. The crystal structure of Gd2O3 was changed after adsorbing phosphate and resulted in Gd2O3 transformation into urchin shaped structures, but the crystal structure of CeO2 remained basically unchanged before and after adsorption. The results of phosphate adsorption of 32 metal oxides showed that rare earth metal oxides (except CeO2) and ZnO had higher adsorption capacity than other metal oxides. The electronegativity and cationic charge of metal oxides were identified as two important factors for phosphate adsorption using ReliefF algorithm. Six machine learning algorithms were utilized to build the prediction models for screening metal oxides with high adsorption capacity, and the predictive accuracy of the decision tree model exceeded 90% for both training and test sets. Classification rules of the decision tree model indicated that metal oxides with electronegativity <= 1.23 had high adsorption potential for phosphate. Characterization of the model's applicability domain showed that all metal oxides were within the applicability domain. DFT analysis showed that adsorption energies of Gd2O3 and Y2O3 for phosphate were -3.97 and -4.56 eV, which were higher than those of CeO2, NiO, ZnO and Cr2O3(-1.61- -0.26 eV). The adsorption configuration of Gd2O3 and Y2O3 with higher adsorption energy for phosphate was bidentate binuclear, while that of CeO2, NiO, ZnO and Cr2O3 with lower adsorption energy was monodentate mononuclear. Combined with molecular orbital theory and density of states analysis, it was found that the d orbitals from Gd and Y atoms higher than the Fermi level interact with the p orbitals of the oxygen atom from the phosphate, splitting into antibonding orbitals higher than the Fermi level. Theoretical calculations revealed a new mechanism that the d orbitals energy level of metal atoms determines the phosphate adsorption energy. The prediction model established in this study can provide a basis for screening metal-based materials that can effectively adsorb phosphate, and the molecular mechanism deciphered by DFT calculations can provide a theoretical basis for the design of adsorbents.