Relationship between molecular connectivity indices and soil sorption coefficients of polycyclic aromatic hydrocarbons

1982 ◽  
Vol 28 (2) ◽  
pp. 162-165 ◽  
Author(s):  
Aleksandar Sabljić ◽  
Miroslava Protić
Keyword(s):  
Polycyclic Aromatic Hydrocarbons ◽  
Aromatic Hydrocarbons ◽  
Molecular Connectivity ◽  
Soil Sorption ◽  
Molecular Connectivity Indices ◽  
Connectivity Indices ◽  
Polycyclic Aromatic

scholarly journals The General (α,3)-Path Connectivity Indices of Polycyclic Aromatic Hydrocarbons

2018 ◽  
Vol 2018 ◽  
pp. 1-5
Author(s):  
Haiying Wang ◽  
Chuantao Li
Keyword(s):  
Applied Sciences ◽  
Polycyclic Aromatic Hydrocarbons ◽  
Aromatic Hydrocarbons ◽  
Molecular Graph ◽  
Three Dimensional ◽  
3D Qsar ◽  
Medical Sciences ◽  
Connectivity Indices ◽  
Polycyclic Aromatic ◽  
Extremal Values

The general (α,t)-path connectivity index of a molecular graph originates from many practical problems such as three-dimensional quantitative structure-activity (3D QSAR) and molecular chirality. It is defined as Rtα(G)=∑Pt=vi1vi2⋯vit+1⊆G[d(vi1)d(vi2)⋯d(vit+1)]α, where the summation is taken over all possible paths of length t of G and we do not distinguish between the paths vi1vi2⋯vit+1 and vit+1⋯vi2vi1. In this paper, we focus on the structures of Polycyclic Aromatic Hydrocarbons (PAHn), which play a role in organic materials and medical sciences. We try to compute the exact general (α,3)-path connectivity indices of this family of hydrocarbon structures. Furthermore, we exactly derive the monotonicity and the extremal values of R3α(PAHn) for any real number α. These valuable results could produce strong guiding significance to these applied sciences.

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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