Abstract

Fentanyl is a potent synthetic opioid pain reliever with a high bioavailability that can be used as prescription anesthetic. Rapid identification via non-contact methods of both known and emerging opioid substances in the fentanyl family help identify the substances and enable rapid medical attention. We apply PBEh-3c method to identify vibrational normal modes from 0.01 to 3 THz in solid fentanyl and its selected analogs. The molecular structure of each fentanyl analog and unique arrangement of H-bonds and dispersion interactions significantly change crystal packing and is subsequently reflected in the THz spectrum. Further, the study of THz spectra of a series of stereoisomers shows that small changes in molecular structure results in distinct crystal packing and significantly alters THz spectra as well. We discuss spectral features of synthetic opioids with higher potency than conventional fentanyl such as ohmefentanyl and sufentanil and discover the pattern of THz spectra of fentanyl analogs.

Details

Title
Accurate prediction of terahertz spectra of molecular crystals of fentanyl and its analogs
Author
Chun-Hung, Wang 1 ; Terracciano, Anthony C 2 ; Masunov, Artёm E 3 ; Xu Mengyu 4 ; Vasu, Subith S 2 

 University of Central Florida, NanoScience Technology Center, Orlando, USA (GRID:grid.170430.1) (ISNI:0000 0001 2159 2859) 
 University of Central Florida, Department of Mechanical and Aerospace Engineering, Orlando, USA (GRID:grid.170430.1) (ISNI:0000 0001 2159 2859); University of Central Florida, Center for Advanced Turbomachinery and Energy Research, Orlando, USA (GRID:grid.170430.1) (ISNI:0000 0001 2159 2859) 
 University of Central Florida, NanoScience Technology Center, Orlando, USA (GRID:grid.170430.1) (ISNI:0000 0001 2159 2859); South Ural State University, Chelyabinsk, Russia (GRID:grid.440724.1) (ISNI:0000 0000 9958 5862); National Research Nuclear University MEPhI, Moscow, Russia (GRID:grid.183446.c) (ISNI:0000 0000 8868 5198) 
 University of Central Florida, Center for Advanced Turbomachinery and Energy Research, Orlando, USA (GRID:grid.170430.1) (ISNI:0000 0001 2159 2859); University of Central Florida, Department of Statistics and Data Science, Orlando, USA (GRID:grid.170430.1) (ISNI:0000 0001 2159 2859) 
Publication year
2021
Publication date
2021
Publisher
Nature Publishing Group
e-ISSN
20452322
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2490849736
Copyright
© The Author(s) 2021. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.