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Published on: October 10, 2014
Photophysics and complexation of indolic compounds: the effect of intra-annular and extra-annular substitution
Soumyadip Ray1, Atheena Krishna D1, Padmabati Mondal1
1Department of Chemistry, Indian Institute of Science Education and Research (IISER), Tirupati, Andhra Pradesh, India. padmabati.mondal@iisertirupati.ac.in.
Abstract:
In this work, substitution-dependent modulation of ground- and excited-state properties is examined for intra-annular aza-substituted indoles (indazole, benzimidazole, and benzotriazole) and extra-annular C5-substituted derivatives (CN, COOH, CHO, F, Cl, OH, OCH, and NH2). A dataset encompassing 12 monomers and 46 dimers is utilized. Intra-annular aza-substitutions are isoelectronic to indole and do not change the aromaticity, while vertical excitation energies (4.9-5.2 eV) are governed primarily by the substitution site. Extra-annular substituents exhibit systematic electronic trends: HOMO-LUMO gaps and excitation energies decrease from CN to NH2 with a nominal exception. The excited state energy profiles of all the compounds are calculated using the TD-DFT method and compared thoroughly. The complexation energies of the dimers of all possible combinations were then obtained for intra- and extra-annular congeners separately using the DFT method. For intra-annular aza-substitutions, the dimer complexation strengths span 8.5-11.4 kcal mol-1, with the benzimidazole homo-dimer showing the most favourable π-π interaction. Dimer binding energies range from 10.0 to 16.3 kcal mol-1 for extra-annular substituted systems, maximized for the CN-COOH heterodimer. DLPNO-CCSD(T) analysis indicates strong dispersion contributions from both electron-withdrawing and electron-donating groups; however, electrostatic interaction provides the major contribution to the interaction energy. The resulting structure-property relationships provide design principles for tuning the photophysics and supramolecular interactions in indolic systems, and establish a preliminary dataset for future machine-learning models linking substitution patterns to complexation and excitation behaviour.
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