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Direct Estimation of Aromatization Energy from 1 H NMR and UV-Vis Absorption Data of Homodesmotic Molecules.

Puthiyavalappil K ArathiCherumuttathu H Suresh
Published in: The Journal of organic chemistry (2024)
This study delves into the ring-opening reaction of two distinct diaryl-ring-pyran systems, referred to as dr n p 1 and dr n p 2 , where the term 'ring' encompasses aromatic, nonaromatic, or antiaromatic motifs. These systems transform into the corresponding cis-ortho quinonoid systems, denoted as c - dr n q 1 and c - dr n q 2 . Homodesmotic pairs ( dr n p 1 , dr n p 2 ) and ( c - dr n q 1 , c - dr n q 2 ) are categorized as (aromatic, nonaromatic), (aromatic, partially aromatic), (antiaromatic, nonaromatic), and (nonaromatic, nonaromatic), with their energy difference representing aromatization energy ( E aroma ). Using reliable density functional theory, E aroma is assessed for various aromatic and antiaromatic ring motifs, including borderline cases and nonaromatic structures. For example, benzene exhibits an E aroma of 23.4 kcal/mol, indicating 3.9 kcal/mol aromatic stabilization per CC bond, while cyclobutadiene shows -29.9 kcal/mol, indicating a 7.5 kcal/mol destabilization of the CC bond. This approach extends to evaluating global and local aromatic stabilization effects in polycyclic hydrocarbons, nonbenzenoid systems, and heterocyclic compounds. Additionally, variation in 1 H NMR chemical shift (δ avg ) correlates with E aroma , suggesting that a -1.0 ppm shift corresponds to 24.2 kcal/mol aromatization energy. UV-vis absorption maxima difference (Δλ avg ) correlates linearly with E aroma , enabling direct assessment of aromatization energy from UV-vis spectra using suitable homodesmotic pairs. This comprehensive approach enhances our understanding of structural, energetic, and spectroscopic aspects of aromatic and antiaromatic systems.
Keyphrases
  • editorial comment
  • amino acid
  • density functional theory
  • magnetic resonance
  • high resolution
  • molecular dynamics
  • mass spectrometry
  • artificial intelligence
  • deep learning