ABSTRACT
A series of cocrystals of halogen bond donors 1,4-diiodotetrafluorobenzene (p-F4DIB) and tetraiodoethylene (TIE) with five aromatic heterocyclic diazine mono-N-oxides based on pyrazine, tetramethylpyrazine, quinoxaline, phenazine, and pyrimidine as halogen bonding acceptors were studied. Structural analysis of the mono-N-oxides allows comparison of the competitive occurrence of N···I vs O···I interactions and the relative strength and directionality of these two types of interactions. Of the aromatic heterocyclic diazine mono-N-oxide organoiodine cocrystals examined, six exhibited 1:1 stoichiometry, forming chains that utilized both N···I and O···I interactions. Two cocrystals presented 1:1 stoichiometry with exclusive O···I interactions. Two cocrystals displayed a 2:1 stoichiometry-one characterized solely by O···I interactions and the other solely by N···I interactions. We have also compared these interactions to those present in the corresponding diazines, some of which we report here and some which have been previously reported. In addition, a computational analysis using density functional theory (M062X/def2-SVPD) was performed on these two systems and has been compared to the experimental results. The calculated complex formation energies were, on average, 4.7 kJ/mol lower for the I···O halogen bonding interaction as compared to the corresponding N···I interaction. The average I···O interaction distances were calculated to be 0.15 Å shorter than the corresponding I···N interactions.
ABSTRACT
Following the emergence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in late 2019, the use of wastewater-based surveillance (WBS) has increased dramatically along with associated infrastructure globally. However, due to the global nature of its application, and various workflow adaptations (e.g., sample collection, water concentration, RNA extraction kits), numerous methods for back-calculation of gene copies per volume (gc/L) of sewage have also emerged. Many studies have considered the comparability of processing methods (e.g., water concentration, RNA extraction); however, for equations used to calculate gene copies in a wastewater sample and subsequent influences on monitoring viral trends in a community and its association with epidemiological data, less is known. Due to limited information on how many formulas exist for the calculation of SARS-CoV-2 gene copies in wastewater, we initially attempted to quantify how many equations existed in the referred literature. We identified 23 unique equations, which were subsequently applied to an existing wastewater dataset. We observed a range of gene copies based on use of different equations, along with variability of AUC curve values, and results from correlation and regression analyses. Though a number of individual laboratories appear to have independently converged on a similar formula for back-calculation of viral load in wastewater, and share similar relationships with epidemiological data, differential influences of various equations were observed for variation in PCR volumes, RNA extraction volumes, or PCR assay parameters. Such observations highlight challenges when performing comparisons among WBS studies when numerous methodologies and back-calculation methods exist. To facilitate reproducibility among studies, the different gc/L equations were packaged as an R Shiny app, which provides end users the ability to investigate variability within their datasets and support comparisons among studies.
