Design of bifunctional resin-microbe complex guided by density functional theory and machine learning for enhanced phenol degradation and Cr (VI) reduction

Abstract

Hexavalent chromium and phenol coexist in wastewater and exhibit synergistic toxicity, enhancing biological harmful effects. The integration of Density Functional Theory (DFT) and Machine Learning (ML) offers a promising approach to addressing complex environmental challenges, through bridging macroscopic and microscopic regular analysis. DFT simulations revealed that the tertiary amine (-N(CH₃)₂) and quaternary ammonium (-N⁺(CH₃)₃) groups on D301, carrying positive charges, can assemble phenol-degrading microorganisms through electrostatic interactions. HOMO/LUMO energy and the Fukui function revealed a low energy gap of 0.128 Ha between D301 and Cr(VI), suggesting the potential for spontaneous Cr(VI) reduction by D301. experiments demonstrated that the resin–microorganism composite material could degrade 1500 mg/L of phenol and reduce 20 mg/L of Cr(VI), which is higher than most of the currently reported co-removal levels. Using Bayesian regression, a synergistic metabolic model is established to predict the removal performance. The resin-microbe system can remove 34–36 % of 1800 mg/L phenol under 20 mg/L Cr(VI), with a prediction error of less than 5 %. This study, through DFT and integrated ML, revealed the active sites of the resin and constructed a co-metabolism model of phenol and Cr(VI), providing a new strategy for material design and microbial assembly in the removal of co-contaminants.