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We have predicted acid dissociation constants (pKa), octanol-water partition coefficients (KOW), and DPMC lipid membrane-water partition coefficients (Klipid-w) of 150 different 8-carbon containing poly-/per-fluoroalkyl carboxylic acids (C8-PFCAs) utilizing COMSO-RS theory. Different trends associated with functionalization, degree of fluorination, degree of saturation, degree of chlorination, and branching are discussed based upon the predicted values for the partition coefficients. In general, functionalization closest to the carboxylic head group had the greatest impact on the value of the predicted physicochemical properties. 

Per-and polyfluoroalkyl substances (PFAS) are a class of contaminants of emerging concern frequently used in products like aqueous firefighting foams and non-stick coatings due to their stability and surfactant-like qualities. The lack of analytical standards for many emerging PFAS have severely limited our ability to comprehensively identify unknown PFAS contaminants in the environment, especially those that occur as isomers. Annotation of small molecules and identification of unknowns based only on elemental composition and mass fragmentation patterns remain major challenges in nontarget analysis employing liquid chromatography with high-resolution mass spectrometry (LC-HRMS). In this study, chromatographic retention factors (k) and mass spectral fragmentation patterns of 32 known PFAS were determined using our optimized parameters in LC-HRMS. The same method was then used to analyze previously unidentified PFAS in actual environmental samples. Using characteristic ions observed in the MS fragmentation of PFAS, the most probable isomeric structures of the detected PFAS were predicted. To increase confidence in the predicted molecular structure, Density Functional Theory and Conductor-like Screening Model for Realistic Solvents (COSMO-RS) calculations were used to predict physicochemical properties of different constitutional isomers. The DFT calculations facilitated geometric optimization, determination of polarizability, and calculation of the chemical potential the isomers. COSMO-RS uses the chemical potential to predict thermodynamic properties of molecules such as pKa, solubility, and Kow. These properties were then used to make a multi-variable linear regression to predict k values. The model was trained using 32 known PFAS. The properties used were log Kow of the neutral and anion species of the PFAS, and their polarizability. The model was specific enough to predict significantly different k values of unknown compounds with similar structures, which facilitated assignment of isomeric structures of PFAS. 

Nontarget analysis using liquid chromatography–high resolution mass spectrometry (LC–HRMS) is a valuable approach in characterizing for contaminants of emerging concern (CECs) in the environment. However, identification of these analytes can be quite costly or taxing without proper analytical standards. To circumvent this problem we utilize Quantitative structure-retention relationships (QSRR) models to predict elution order and retention times. Properties calculated from density functional theory (DFT) and the conductor-like screening model for real solvents (COSMO-RS) theory are used to produce our QSRR models, which can be calculated for virtually any analyte. We show that this methodology has been successful in identification of per- /poly-fluoroalkyl substances (PFAS) and other contaminants. Nontarget analysis using liquid chromatography– high resolution mass spectrometry (LC–HRMS) is a valuable approach in characterizing for contaminants of emerging concern (CECs) in the environment. However, identification of these analytes can be quite costly or taxing without proper analytical standards. To circumvent this problem we utilize Quantitative structureretention relationships (QSRR) models to predict elution order and retention times. Properties calculated from density functional theory (DFT) and the conductor-like screening model for real solvents (COSMO-RS) theory are used to produce our QSRR models, which can be calculated for virtually any analyte. We show that this methodology has been 

Engineered nanomaterials have unique properties compared to their bulk counterparts. Copper oxide nanoparticles (CuO NPs) are one example of nanomaterials used in a wide range of consumer products due to their conductivity and biocidal properties. While CuO NPs can induce toxicity in various organisms, their interactions with different organisms and how they affect cellular homeostasis is yet to be fully understood. In this work, the toxicity of CuO NPs was evaluated in different human cell lines (colorectal carcinoma, cervical cancer, embryonic kidney, and lung fibroblast), showing a dose-dependent toxicity. An untargeted lipidomics approach using liquid chromatography-quadrupole time of flight mass spectrometry was employed in a human colon carcinoma cell line to investigate the impact of CuO NP exposure at the cellular level. A 24 h CuO NP exposure at 2.5 and 5 μg mL −1 resulted in upregulation of different metabolites: triacylglycerols, phosphatidylcholines, and ceramides accumulated. The most profound increase in a dose-dependent manner was observed in ceramides, specifically in C18:0, C18:1, and C22:0 species, with up to ∼10 fold accumulations. Further experiments suggested that activation of autophagy and oxidative stress could be responsible for the toxicity observed in these cell lines. Increases in the level of glutathione oxide (∼7 fold) also supported the activation of oxidative stress upon CuO NP treatment. Based on the changes in different metabolites induced by CuO NP exposure and previous studies from our laboratory, we propose that autophagy and oxidative stress could play a role in CuO NP-induced toxicity.