In Silico Study of the π-π Stacking Interactions in Borazine-Borazine, Boroxine-Boroxine, Borazine-Boroxine Complexes and Their Substituted Derivatives
Abstract
The study investigates the stacking interactions between borazine-borazine, boroxine-boroxine, and borazine-boroxine complexes and their substituted derivatives, obtained by replacing all the hydrogen atoms with the electron-withdrawing -F and the electron-donating -CH3 groups. The strength of these interactions is rationalized using a composite density functional theory method, r2SCAN-3c. It is found that the staggered conformation is more stable than the eclipsed conformation in all of the cases studied. Noncovalent interaction (NCI) plots and quantum theory of atoms in molecules analysis were carried out to confirm the presence of NCIs in the complexes. Analysis through the symmetry-adapted perturbation theory approach indicates that dispersion is the most significant contributor to the stabilization of the stacking interactions. However, the observation of small, negative charge transfer (CT) energy values demonstrates stabilization due to CT upon complexation; the magnitude of stabilization increases with both -F and -CH3 substitutions, and the staggered configuration in each case exhibits higher CT than the eclipsed one.
How strong is the evidence?
Evidence Hub reads this as a simulation / modelling, done in computer models (in silico), not in people. Its evidence level is 5 of 5 (Very low · case / narrative / animal / in vitro / in silico). Case reports, narrative reviews, and animal, cell or computer-model studies give the weakest evidence for human health.
Read the paper
Related studies
- Chaos and quantum thermalization
- The Dalton quantum chemistry program system
- Cord Blood Leptin and Adiponectin as Predictors of Adiposity in Children at 3 Years of Age: A Prospective Cohort Study
- Quantitative metabolomics by H-NMR and LC-MS/MS confirms altered metabolic pathways in diabetes
- Stable adiabatic quantum batteries
- A novel self-lipid antigen targets human T cells against CD1c(+) leukemias
Go deeper in Evidence Hub
Evidence Hub adds a study data sheet (design, sample size, evidence level, key numbers), an AI summary and a chat grounded in the paper's text.