In-depth interpretations of the interactions of aromatic molecules to crosslinked surfaces using the grand canonical approach and quantum mechanics calculations

• Adsorption of phenol and cyclohexane carboxylic acid (CCA) on HABC. • Combination of quantum mechanics calculations and statistical physics models. • Phenol molecules are positioned non-parallel to the adsorbent surface. • Δ E for phenol suggests a predominant reversible interaction mechanism. • CCA adsorption shows a stronger site-specific interaction. This study investigated the adsorption mechanisms of phenol and cyclohexane carboxylic acid (CCA) on a crosslinked adsorbent surface using combined quantum mechanics and statistical physics models. The calculations were performed using Density Functional Theory (DFT) to determine the energy profiles and electronic interactions, while statistical physics models of the monolayer, double layer with one and two adsorption energies, and the multilayer model were employed to describe the adsorption behavior on the adsorbent surface. The results indicate that the phenol molecules are oriented non-parallel to the surface, with the number of molecules adsorbed per site ( d ) greater than 1 at all temperatures studied. With increasing temperature, the number of d decreases, reflecting limited accommodation due to site occupation. The analysis of the adsorption energy ( Δ E ) for phenol ranged from 32 to 27 kJ mol −1 phenol, suggesting a predominant physical interaction mechanism ( Δ E < 40 kJ mol −1 ). For CCA, the adsorption process followed a similar trend, with Δ E around 18 kJ mol −1 , indicating that the removal process also occurs predominantly via physical interactions. The comparison between the compounds reveals that phenol has higher adsorption energy and a more disordered arrangement on the surface. At the same time, CCA exhibits more localized and selective interactions, possibly due to differences in their electronic structures and functional groups, according to the results obtained by DFT. These findings provide fundamental insights for the design and optimization of adsorbents, contributing to the development of more efficient strategies for the removal of organic pollutants.