Controlling Chirogenic Effects in Porphyrin Based Supramolecular Systems: Theoretical Analysis Versus Experimental Observations.
Irina OsadchukHanna-Eliisa LutsAleksandra ZahharovaToomas TammVictor BorovkovPublished in: Chemphyschem : a European journal of chemical physics and physical chemistry (2024)
Electronic circular dichroism (ECD) spectroscopy is a widely employed method for studying chiral analysis, requiring the presence of a chromophore close to a chiral centre. Porphyrinoids are found to be one of the best chromophoric systems serving for this purpose and enabling the application of ECD spectroscopy for chirality determination across diverse classes of organic compounds. Consequently, it is crucial to understand the induction mechanisms of ECD in the porphyrin-based complexes. The present study explores systematically the influence of secondary chromophores, bonded to an achiral zinc porphyrin or to chiral guest molecules, on the B-region of ECD spectra using the time-dependent density functional theory (TD-DFT) calculations. The study analyses the impact of change in both the conformation of achiral porphyrin (host) and change in position and conformation of chiral organic molecule (guest) on the B-band of ECD spectra (energy, intensity, sign of Cotton effect). Finally, conclusions made on model complexes are applied to published experimental data, contributing to a deeper understanding of various factors influencing ECD spectra in chiral systems. In addition, a computer program aimed to help rationalise ECD spectra by visualizing corresponding orbital energies, rotatory strengths, electric and magnetic transition moments, and angles between them, is presented.
Keyphrases
- density functional theory
- molecular dynamics
- photodynamic therapy
- capillary electrophoresis
- ionic liquid
- energy transfer
- high resolution
- metal organic framework
- single molecule
- molecular dynamics simulations
- randomized controlled trial
- mass spectrometry
- deep learning
- systematic review
- molecularly imprinted
- machine learning
- solid state
- liquid chromatography
- quantum dots
- meta analyses