Prediction of the interaction strength of an urea‐based probe toward ions in water by means of Density Functional Theory/Polarizable Continuum Model calculations

Abstract We study numerically, by means of density functional theory (DFT) calculations complemented with an implicit solvation model, a novel chemical probe bearing urea and aromatic phenyl groups. We probe the interaction in water of the latter with a wide variety of ions relevant to water quality. We perform geometry minimizations using PBE0 functional and aug‐cc‐pVDZ basis set, and a polarizable continuum model (PCM) to take into account the aqueous solvent. We underline for the first time several methodological details concerning the definition of the binding or interaction energy, and the basis set superposition error definition in the context of implicit solvation models. We observe two competing interaction modes for this probe: a urea‐enhanced, cation‐ π interaction (with cations only), and hydrogen bonding occurring between the urea group and anions, the former being more favorable than the latter. A Generalized Kohn–Sham Energy Decomposition Analysis (GKS‐EDA) in implicit solvent is performed to analyze the nature of the ions–probe interactions. Magnesium and sodium ions, and respectively glyphosate and hypochlorite ions, are found as the cations (resp. anions) having the largest binding free energies with the probe. This is the first time such an exhaustive study, investigating the selectivity of an organic probe toward a wide variety of ions in water, is carried out in the context of DFT/PCM models. Computer‐aided sensor design needs reliable and efficient methods. Our methodology can be used as a general way to gain a valuable insight into the sensitivity of organic ligands toward a variety of ions or pesticides in water, without the need of an explicit solvent description, but still going beyond the state‐of‐the‐art DFT in vacuo approach. By predicting possible competitive interactions, and understanding their nature, this methodology can thus help to better design functional groups selective to specific targets.