Influence of multivalent background ions competition adsorption on the adsorption behavior of azo dye molecules and removal mechanism: Based on machine learning, DFT and experiments.

[Display omitted] • Adsorption behavior and mechanism of azo dye molecules. • The influence of background ion competition on adsorption. • Prediction of reaction driving forces. • Efficient identification of environmental factors. This study reveals the influence of multivalent background ions, including Ca2+, K+, Na+ and Mg2+ on the adsorption capacity of natural mineral materials for azo dye molecules through experiments and machine learning. At the molecular and electronic scales, DFT calculations elucidate the electronic transfer pathways and adsorption mechanisms between them, providing computational support for the machine learning results. The experimental results demonstrate that the adsorbent has good removal efficiency for azo dye molecules. The pseudo-first-order kinetic model and Langmuir model fit the kinetic and isotherm data well, indicating uniform and predominantly physical adsorption. The machine learning results show that the gradient boosting decision tree model has better fit to the experimental data, and the correlation impact of mixed background ions on the adsorbent is more significant than that of individual ions. The inhibitory strength of ions on the adsorption capacity of the adsorbent follows the order of Mg2+ > Ca2+ > K+ > Na+. DFT results indicate that the adsorption process is mainly facilitated by the interaction of hydrogen bonding (C–H...O) and van der Waals forces (Si–H...C), ensuring high adsorption efficiency. Additionally, it is found that although background ions alter the electron transfer pathways during the adsorption process and reduce the stability of complexes, azo dye molecules can compensate for the blocked adsorption sites by adhering to background ions already adsorbed on the surface of the adsorbent through cation-π interactions, reducing their inhibitory effect on adsorption. This study provides some evidence for the impact of background ions as environmental factors on adsorption processes through a combination of machine learning, experiments, and theoretical calculations. [ABSTRACT FROM AUTHOR]