Elucidating Interactions Between Ionic Liquids and Polycyclic Aromatic Hydrocarbons by Quantum Chemical Calculations

2013 ◽  
Vol 117 (9) ◽  
pp. 4521-4532 ◽  
Author(s):  
Durgesh Wagle ◽  
Ganesh Kamath ◽  
Gary A. Baker
Keyword(s):  
Polycyclic Aromatic Hydrocarbons ◽  
Ionic Liquids ◽  
Quantum Chemical Calculations ◽  
Aromatic Hydrocarbons ◽  
Quantum Chemical ◽  
Polycyclic Aromatic

Optical properties of pyridine adsorbed polycyclic aromatic hydrocarbons using quantum chemical calculations

2019 ◽  
Vol 21 (1) ◽  
pp. 448-454 ◽  
Author(s):  
Junais Habeeb Mokkath
Keyword(s):  
Optical Properties ◽  
Polycyclic Aromatic Hydrocarbons ◽  
Quantum Chemical Calculations ◽  
Aromatic Hydrocarbons ◽  
Quantum Chemical ◽  
Hydrogen Atoms ◽  
Polycyclic Aromatic

Polycyclic aromatic hydrocarbons (PAHs), the molecular version of graphene, having edges saturated with hydrogen atoms, have recently emerged as a novel nanoplasmonic material.

Hydrogenated polycyclic aromatic hydrocarbons (HnPAHs) as catalysts for hydrogenation reactions in the interstellar medium: a quantum chemical model

2019 ◽  
Vol 21 (22) ◽  
pp. 12012-12020 ◽  
Author(s):  
Ricardo M. Ferullo ◽  
Carolina E. Zubieta ◽  
Patricia G. Belelli
Keyword(s):  
Polycyclic Aromatic Hydrocarbons ◽  
Aromatic Hydrocarbons ◽  
Quantum Chemical ◽  
Density Functional ◽  
Activation Barrier ◽  
Functional Studies ◽  
Quantum Chemical Model ◽  
Polycyclic Aromatic ◽  
Hydrogenation Reactions ◽  
No Activation

Density functional studies show that neutral HnPAHs are able to catalyze the formation of water with no activation barrier.

scholarly journals Estimation of n-octanol/water partition coefficients of polycyclic aromatic hydrocarbons by quantum chemical descriptors

2008 ◽  
Vol 6 (2) ◽  
pp. 310-318 ◽  
Author(s):  
Gui-Ning Lu ◽  
Xue-Qin Tao ◽  
Zhi Dang ◽  
Xiao-Yun Yi ◽  
Chen Yang
Keyword(s):  
Polycyclic Aromatic Hydrocarbons ◽  
Aromatic Hydrocarbons ◽  
Quantum Chemical ◽  
Partition Coefficients ◽  
Density Functional ◽  
Optimal Model ◽  
Quantum Chemical Descriptors ◽  
Polycyclic Aromatic ◽  
Pls Analysis ◽  
Chemical Descriptors

AbstractQuantitative structure-property relationship (QSPR) modeling is a powerful approach for predicting environmental behavior of organic pollutants with their structure descriptors. This study reports an optimal QSPR model for estimating logarithmic n-octanol/water partition coefficients (log K OW) of polycyclic aromatic hydrocarbons (PAHs). Quantum chemical descriptors computed with density functional theory at B3LYP/6-31G(d) level and partial least squares (PLS) analysis with optimizing procedure were used for generating QSPR models for log K OW of PAHs. The squared correlation coefficient (R 2) of the optimal model was 0.990, and the results of crossvalidation test (Q 2cum=0.976) showed this optimal model had high fitting precision and good predictability. The log K OW values predicted by the optimal model are very close to those observed. The PLS analysis indicated that PAHs with larger electronic spatial extent and lower total energy values tend to be more hydrophobic and lipophilic.

ESTIMATION OF SOIL SORPTION COEFFICIENTS OF POLYCYCLIC AROMATIC HYDROCARBONS BY QUANTUM CHEMICAL DESCRIPTORS

2008 ◽  
Vol 07 (01) ◽  
pp. 67-79 ◽  
Author(s):  
GUI-NING LU ◽  
CHEN YANG ◽  
XUE-QIN TAO ◽  
XIAO-YUN YI ◽  
ZHI DANG
Keyword(s):  
Polycyclic Aromatic Hydrocarbons ◽  
Aromatic Hydrocarbons ◽  
Quantum Chemical ◽  
Soil Sorption ◽  
Optimal Model ◽  
Quantum Chemical Descriptors ◽  
Soils And Sediments ◽  
Polycyclic Aromatic ◽  
Pls Analysis ◽  
Chemical Descriptors

Quantitative structure–property relationship (QSPR) modeling is a powerful approach for predicting environmental behavior of organic pollutants with their structure descriptors. This study reports an optimal QSPR model for estimating logarithmic soil sorption coefficients (log K OC ) of polycyclic aromatic hydrocarbons (PAHs). Quantum chemical descriptors computed using density functional theory at the B3LYP/6-31G(d) level and partial least squares (PLS) analysis with an optimizing procedure were used to generate QSPR models for log K OC of PAHs. The correlation coefficient of the optimal model was 0.993, and the results of a cross-validation test ([Formula: see text]) showed this optimal model had high fitting precision and good predicting ability. The log K OC values predicted by the optimal model are very close to those observed. The PLS analysis indicated that PAHs with larger electronic spatial extent tend to more easily adsorb and accumulate in soils and sediments, whereas those with higher molecular total energy and larger energy gap between the lowest unoccupied and the highest occupied molecular orbital adsorb and accumulate in soils and sediments less readily.

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