DFT study about capturing of toxic sulfur gases over cyclic tetrapyrrole

The development of sensors that can detect hazardous analytes selectively and accurately, particularly sulfur based irritants, is quite essential. The infinite-conjugation in cyclic conducting polymers make them highly sensitive to toxic analytes. We implemented B3LYP-D3/6-311 + G (d, p) level to explore the sensing mechanism of cyclic tetrapyrrole (CTPy) for reliable detection of carbonyl sulfide, carbon disulfide, hydrogen sulfide sulfur monoxide, sulfur dioxide and sulfur trioxide using the DFT practice. The interaction energies, Eint ranges from -25.6 to -89.2 kJ mol- 1 for sulfur gases over CTPy. Charge transfer interactions in complexes are predicted using natural bond orbital (NBO) and charge decomposition (CDA) analysis. The reduced density gradient (RDG) method supports hydrogen bonding and dispersion interactions in the complexes. The decrease in HOMO-LUMO energy gaps, as well as the red shifting of lambda max in UV-Visible spectra, demonstrate sensitivity of CTPy towards sulfur gases. The improved conductivity of complexes is owing to production of numerous energy levels in oc-cupied and virtual orbitals closer to the Fermi level in DOS spectra. Furthermore, PDOS spectra reveal that CTPy is chiefly contribute to energy of HOMO. The recent findings show CTPy has a significant sensitivity to sul-fur irritants. We hope that the above findings and their implications will give valuable suggestions for an experimentalist in designing extremely sensitive hazardous analyte sensors employing CTPy.

Automated summary

The development of sensors that can selectively and accurately detect hazardous analytes, especially sulfur-based irritants, is crucial. In this study, the sensing mechanism of cyclic tetrapyrrole (CTPy) for reliable detection of various sulfur gases was explored using DFT calculations. The results showed that CTPy exhibited significant sensitivity to sulfur gases, as demonstrated by the interaction energies, charge transfer interactions, and changes in energy gaps and UV-Visible spectra. These findings provide valuable insights for designing highly sensitive hazardous analyte sensors employing CTPy.