Research Article Estimation of n -Octanol/Water...

Hindawi Publishing CorporationJournal of ChemistryVolume 2013 Article ID 740548 8 pageshttpdxdoiorg1011552013740548

Research ArticleEstimation of n-OctanolWater Partition Coefficients(logKOW) of Polychlorinated Biphenyls by Using QuantumChemical Descriptors and Partial Least Squares

Fang-Li Zhang1 Xing-Jian Yang1 Xiu-Ling Xue2 Xue-Qin Tao3

Gui-Ning Lu14 and Zhi Dang15

1 School of Environment and Energy South China University of Technology Guangzhou 510006 China2 College of Chemical Engineering Huaqiao University Xiamen 361021 China3 School of Environmental Science and Engineering Zhongkai University of Agriculture and Engineering Guangzhou 510225 China4Guangdong Provincial Key Laboratory of Atmospheric Environment and Pollution Control Guangzhou 510006 China5The Key Lab of Pollution Control and Ecosystem Restoration in Industry Clusters Ministry of Education Guangzhou 510006 China

Correspondence should be addressed to Gui-Ning Lu lgn36qqcom

Received 15 May 2013 Revised 29 July 2013 Accepted 22 August 2013

Academic Editor Enrico Veschetti

Copyright copy 2013 Fang-Li Zhang et al This is an open access article distributed under the Creative Commons Attribution Licensewhich permits unrestricted use distribution and reproduction in any medium provided the original work is properly cited

The n-octanolwater partition coefficient (log119870OW) is a useful parameter for the assessment of the environmental fate and impact ofxenobiotic trace contaminantsQuantitative structure-activity relationship (QSAR)model for log119870OW of polychlorinated biphenyls(PCBs) was analyzed by using the density functional theory at B3LYP6-31G(d) level and the partial least squares (PLS)methodwithan optimizing procedure A PLS model with reasonably good coefficient (1198772 = 0992) and cross-validation test (1198762cum = 0988)values was obtained All the predicted values are within the range of plusmn03 log unit from the observed values The log119870OW values of7 PCBs in the test set predicted by the model are very close to those observed indicating that this model has high fitting precisionand good predictability The PLS analysis showed that PCBs with larger electronic spatial extent and lower molecular total energyvalues tend to be more hydrophobic and lipophilic

1 Introduction

Polychlorinated biphenyls (PCBs) are highly stable industrialchemical products that have been used as industrial fluidsflame retardants diluents hydraulic fluids and dielectric flu-ids Due to careless disposal practices and chemical stabilityPCBs are some of the most prevalent pollutants in the totalenvironment Generally a sum of 209 theoretical PCBs existand approximately 150 PCBs are found in the environment[1]

Despite the fact that the production and the use of PCBshave long been banned they are still considered environ-mental contaminants as they are among the most widespreadand persistent man-made products in the ecosystem It iswell known that these chemicals are toxic and lipophilic and

tend to be bioaccumulated Some interfere with mammalianand avian reproduction and others disturb humanrsquos germcell development or are promoters of cancer Some PCBs areincluded in the increasing list of environmental pollutantsthat are able to mimic estrogen action and are termedldquoenvironmental estrogensrdquo [2]

PCBs are categorized as persistent organic pollutants(POPs) [3] Individual PCB congeners exhibit differentphysicochemical properties and biological activities thatresult in different environmental distributions and toxicityprofiles The variable composition of PCB residues in envi-ronmental matrices and their different mechanisms of toxi-city complicate the development of scientifically based regu-lations for the risk assessment [4] The n-octanolwater dis-tribution coefficient (119870OW) is an important physicochemical

2 Journal of Chemistry

property suitable for the characterization of the lipophilicityof the compounds such as PCBs119870OW has been shown to cor-relate with aquatic solubility toxicity and bioaccumulation[5ndash7] and as such it is a useful parameter for the assessmentof the environmental fate and impact of xenobiotic tracecontaminants [8] During the last 50 years 119870OW has provento be a valuable tool in many areas of natural sciences chem-istry biochemistry biology pharmacology environmentalsciences and so forth During that period many hundredsor even thousands of scientific publications have been madein which the successful application of 119870OW in correlationwith many physical chemical or biological properties hasbeen demonstrated for a large variety of organic chemicalsUnfortunately many correlations have been also publishedin which the processes studied have nothing in commonwith the process of partition between water and n-octanolphases It must be emphasized that in order to correlate twoproperties do not have to be related both of themmay simplybe related to a third property The shake flask is the classicalmethod for measuring 119870OW and this procedure will givereproducible and accurate results for chemicalswith log119870OWAccurate results can also be obtained for more hydrophobicchemicals but this will need a very precise and skillfulhandling and the use of very pure n-octanol The problemwith this procedure is that the complete phase separation isdifficult to achieve and this may lead to the underestimationof119870OW particularly for themore hydrophobic chemicals [9]

It is impractical to measure the 119870OW of all the PCBsdirectly in the laboratory because of the extremely lowwater solubility of some high-chlorinated PCBs The lackof complete reliable and comparable data has led to thedevelopment of different119870OW estimation methods With theadvent of inexpensive and rapid computation there has beena remarkable growth in the area of quantitative structure-activity relationships (QSAR) which correlate the proper-ties of pollutants with relevant properties and moleculardescriptors [11] A large number of calculation methods havebeen presently developed for estimation of the partitioncoefficients with varying success and applicability Thesemethods are cable of predicting environmental behaviors notonly for common environmental pollutants but also for thosechemicals that have not yet been synthesized or those thatcannot be examined experimentally due to their extremelyhazardous nature According to the descriptors used thesemethods can be classified into two groups empirical andtheoretical methods [12] Hansen et al [1] used the degreeof chlorination in the orthoposition of the PCB molecule aspredictor variable together with either the calculated molec-ular total surface area (TSA) or the gas chromatographicrelative retention times (RRT) to predict the log119870OW andsoil sorption coefficients (log119870OC) for all 209 PCB congenerswhich were based on 53 and 48 experimental data pointsrespectively Also reporting the relationship between 119870OWand 119870OC which is promising for 119870OW could proceed with119870OC However TSA and RRT are both empirical moleculardescriptors Firstly the accurate determination of empiricalmolecular descriptorsmay be difficult and expensive in termsof cost and time or even impossible for some PCBs whichmight have been not synthesized or purified Meanwhile the

experimental errors may be introduced especially for thosecongeners difficult to be separated and identified by chro-matography Furthermore the empirical molecular descrip-tors often reflect complex and multiple physical interactionsHence the interpretation of the respective property could bedifficult and ambiguous [13] Many attempts have been madeto model the physicochemical properties and bioactivities ofPCBs by utilizingQSAR techniques [14ndash21] For instanceQinet al [17] reported the correlation of bioconcentration factorsand molecular electronegativity distance vector (MEDV)of PCBs based on molecular structure Tuppurainen andRuuskanen [18] introduced a newQSARdescriptor electroniceigenvalue (EEVA) based on molecular orbital energies topredict the Ah receptor binding affinity of PCBs polychlo-rinated dibenzo-p-dioxins and dibenzofurans (PCDDFs)Daren [19] reported QSPR studies of119870OW 119878W 119884W and119870OCof all 209 PCBs and biphenyl by the combination of geneticalgorithms and PLS analysis

Modern theoretical method in quantum chemistry suchas density functional theory (DFT) with high calculation pre-cision should have its advantages in estimating the propertiesof environmental toxics such as PCBs PCDDFs [13 22]The aim of the present study is to explore QSAR modelrelating to log119870OW of 27 PCBs by using partial least squares(PLS) analysis with optimizing procedure based on reportedlog119870OW data and quantum chemical descriptors computedby DFT contained in Gaussian 03 in an attempt to developreliable and predictive QSARmodel and identify the primarymolecular factors governing the 119870OW of PCBs

2 Materials and Methods

21 Target PCBs A total of 27 PCBs whose 119870OW andorlog119870OW were previously published were chosen to constitutethe training set and the test set in this work The trainingset consists of 20 of these 27 PCBs selected randomly andthe remaining 7 PCBs constitute the test set Their chemicalabstracts service numbers (CAS no) and reported log119870OWare listed in Table 1

22 Calculation and Selection of the Descriptors The molec-ular modeling system HyperChem (Release 70 HypercubeInc 2002) was used to construct and view all molecularstructures Molecular geometry was optimized and quantumchemical descriptors were computed using the B3LYP hybridfunctional of DFT in conjunction with 6-31G(d) a split-valence basis set with polarization function The B3LYPcalculations were performed using the quantum chemicalcomputation software Gaussian 03 [23] All calculations wererun on an Intel Core i5-2410M CPU230GHz computerequipped with 200GB of internal memory and MicrosoftWindows 7 professional operating system

According to the chemometrics theory it is suggested thataQSARmodel should include asmany relevant descriptors aspossible to increase the probability of a good characterizationfor a class of compounds [24] In this study 18 independentquantum chemical variables were selected for developingQSAR models The descriptors cover the dihedral angle of

Journal of Chemistry 3

Table 1 n-Octanolwater partition coefficients of the PCBs studied

Noa Compounds CAS no Observed log119870OWb Predicted log119870OW

Reference [10] Model I Model II1 Biphenyl 92-52-4 410 414 396 3872 2-Chlorobiphenyl 2051-60-7 456 464 456 4563 3-Chlorobiphenyl 2051-61-8 472 463 477 4774 221015840-Dichlorobiphenyl 13029-08-8 502 520 503 5055 24-Dichlorobiphenyl 33284-50-3 515 516 505 5066 2210158405-Trichlorobiphenyl 37680-65-2 564 573 557 5597 2441015840-Trichlorobiphenyl 7012-37-5 574 573 582 5808 245-Trichlorobiphenyl 15862-07-4 577 576 557 5609 23101584045-Tetrachlorobiphenyl 73557-53-8 639 630 658 65810 331015840441015840-Tetrachlorobiphenyl 32598-13-3 652 639 668 67111 2210158403451015840-Pentachlorobiphenyl 38380-02-8 685 684 685 69112 2210158404551015840-Pentachlorobiphenyl 37680-73-2 685 680 685 68613 23456-Pentachlorobiphenyl 18259-05-7 685 686 685 68814 221015840331015840441015840-Hexachlorobiphenyl 38380-07-3 744 743 745 75315 221015840441015840551015840-Hexachlorobiphenyl 35065-27-1 744 737 748 74316 221015840441015840661015840-Hexachlorobiphenyl 33979-03-2 712 739 741 73717 221015840331015840441015840551015840-Octachlorobiphenyl 35694-08-7 868 860 860 85918 221015840331015840551015840661015840-Octachlorobiphenyl 2136-99-4 842 850 858 85419 2210158403310158404410158405510158406-Nonachlorobiphenyl 40186-72-9 914 908 890 89020 221015840331015840441015840551015840661015840-Decachlorobiphenyl 2051-24-3 960 972 934 93021 4-Chlorobiphenyl 2051-62-9 469 469 450 45122 25-Dichlorobiphenyl 34883-39-1 518 516 515 51123 441015840-Dichlorobiphenyl 2050-68-2 528 529 534 52524 3441015840-Trichlorobiphenyl 38444-90-5 590 577 598 59725 221015840551015840-Tetrachlorobiphenyl 35693-99-3 626 629 639 63526 231015840410158405-Tetrachlorobiphenyl 32598-11-1 639 631 653 65727 2210158403410158405510158406-Heptachlorobiphenyl 52663-68-0 793 795 804 805aCompounds no 1ndash20 constitute the training set and compounds No 21ndash27 constitute the test set bfrom [10]

the two benzene ring plans (DA) the eigenvalue of thehighest occupied molecular orbital (119864HOMO) the eigenvalueof the lowest unoccupied molecular orbital (119864LUMO) theMulliken atomic charges on 12 carbon atoms from C1 to C12(119876C1ndash119876C12 Figure 1 shows the numbering of selected atomson themolecular structure) the electronic spatial extent (Re)the dipole moment (120583) and molecular total energy (TE) Allthe above descriptors were obtained directly in the outputfiles of Gaussian 03 calculation through a single full opti-mizing process for the molecular structure B3LYP6-31G(d)FOPT The values of these quantum chemical descriptors arelisted in Table 2 The units of dihedral angle atomic chargeelectronic spatial extent dipole moment and energy are asfollows degree atomic charge unit atom unit Debye andHartree respectively

23 Modeling Method and Evaluation Indexes Since a bignumber of descriptors were selected in this study intercor-relation of independent variables (multicollinearity) mightbecome a technical problem It was especially difficultwhen the number of independent variables was equal toor greater than the number of compounds selected in the

ClX ClY

1

2 3

4

56

9 8

7

1211

10

Figure 1 Numbering of selected atoms in the molecular structure119883 and119884 are integers between 0 and 5 inclusiveThe positions 7 8 910 11 and 12 are corresponding to 11015840 21015840 31015840 41015840 51015840 and 61015840 respectively

training set The common regression analysis frequentlyused in QSAR studies became ineffective because over-fitting readily occurred To overcome these problems weused the PLS regression a well-known multivariate methodwhich gives a stepwise solution for a regression modelIt extracts principal component-like latent variables fromoriginal independent variables (predictor variables) anddependent variables (response variables) respectively [24]This method can analyze data which are strongly collinearand more importantly it is a remedy to overfitting PLS finds


Table2Quantum

chem

icaldescrip

torsforthe

PCBs

studied

No

DA119864HOMO119864LU

MO

119876C1

119876C2

119876C3

119876C4

119876C5

119876C6

119876C7

119876C8

119876C9

119876C10

119876C11

119876C12

Re120583

TE1

000minus021730minus00330

0010126minus017937minus01339

5minus01248

7minus01339

5minus017937

010126minus017937minus01339

5minus01248

7minus01339

5minus017937

2336

596

000

00minus46

330

272

5613minus02322

3minus002

747

007

290minus01156

4minus01156

4minus012189minus013101minus01608

9006

717minus01479

3minus01354

8minus01244


12764

356

16832minus92

289

733

000minus022

671minus004

365

01034

9minus01805

3minus006


01003


1minus01230


63399

230

21220minus92

289

894

11008minus024

310minus002

654

005

227minus01026

5minus01368

5minus01192


6005

227minus01026

5minus01368

5minus01192


63242

151

200

16minus1382

489

95

5559minus023

637minus003

739

007

579minus01130

4minus01368

0minus005


7006


3minus01336

1minus01649

04150

644

21687minus1382

492

16

8121minus024

564minus00322

0004

787minus008

812minus01356


799minus014376

004

642minus009

074minus01370

2minus01179

6minus01306

2minus014315

4428

552

1857

1minus1842

084

47


578

007


3minus005


8006

733minus01448

2minus01358

7minus006

134minus01343

5minus016175

6126

716

1429

7minus1842

087

88

5616minus024

156minus004

537

007


867minus007

657minus01623

8006

469minus01476

3minus01349

4minus01225


650

70323

226

21minus1842

082

09

12687minus024

760minus005

378

007


7minus006

785minus007

710minus016166

006


033minus01236

3minus01336

4minus01428

567

00831

1948

5minus23

01677

210

000minus023

831minus006

630

01096

3minus01783

8minus007

907minus00744

6minus01334

7minus01697

001096

3minus01783

8minus007

907minus00744

6minus01334

7minus01697

080

12630

267

16minus23

016757

118360minus02529

8minus004

434

005

232minus009

797minus009

264minus006

419minus01280

4minus01343

3004

160minus008


2minus006

856minus014180

718116

2264

02minus2761263

412

8064minus02536

0minus004

744

004

992minus008

328minus01374

8minus006

497minus007

717minus01425

1004

568minus008


869minus01427

77317571

15120minus2761268

013

8995minus025

762minus004

957

006

408minus009

995minus008

788minus007

241minus008

788minus009

995

002

910minus01336

9minus013182minus012112minus013182minus01336

963

48796

21628minus2761249

114

8565minus026

235minus004

741

004

784minus009

803minus009

259minus006

444minus01280

2minus01338

8004

784minus009

803minus009

259minus006

444minus01280

2minus01338

888

80956

31952minus3220

848

115

8114minus025

785minus005

272

004

819minus008


481minus007

739minus014173

004

819minus008


481minus007

739minus014173

9135369

002

78minus3220

8572

1690

00minus026

706minus004

525

004

104minus007



511

004

104minus007



511

7602

549

000

00minus3220

859

717

8588minus026

513minus005

927

004

910minus009

391minus009

064minus007

515minus007

354minus013392

004

910minus009

391minus009

064minus007

515minus007

354minus013392

1158

265

611

648minus4140

024

218

8998minus026

008minus005

592

003

906minus008

305minus007

831minus01143

7minus007

831minus008

305

003

906minus008

305minus007

831minus01143

7minus007

831minus008

305

9319431

000

00minus4140

027

719

9083minus026

526minus006

331

005

601minus009

023minus008

906minus006

972minus008

907minus009

023

003

596minus008

464minus009

111minus00743

5minus00744

7minus01197

81208

396

910

110minus45

99608

620

9001minus026

670minus006

373

004

327minus007

929minus008

962minus006

896minus008

962minus007

929

004

327minus007

929minus008

962minus006

896minus008

962minus007

929

1257959

3000

00minus50

5919

3321

000minus022

180minus004

264

010353minus01765

2minus01354

1minus006

248minus01354

1minus01765

201034



7minus017931

3744

110

21391minus92

289

9022

5589minus023

832minus003

813

00748

7minus01129

8minus01339


823minus016185

006

624minus014755minus013515minus012312minus013355minus016419

3923

000

07279minus1382

492

223

000minus022

591minus005

162

010551minus01763

9minus01350

5minus006

209minus01350

5minus01763

9010551minus01763

9minus01350

5minus006

209minus01350

5minus01763

956

91897

000

00minus1382

495

024

000minus02322

9minus005

925

01124

1minus01806

1minus007

848minus007

511minus01338

0minus01706

7010325minus01745


122minus01348

1minus017556

6796

892

15815minus1842

0855

2582

68minus025

010minus003

958

004

559minus008


836minus014172

004

559minus008


836minus014172

5819703

02171minus23

01678

626

5556minus024

687minus005

342

00746

2minus01130


8minus006

896minus016122

007

015minus01450

7minus008

147minus007

164minus01324

1minus01556

668

27221

239

61minus23

01677

927

9059minus025

929minus005

534

005

332minus009

406minus007


793minus009

406

003168minus00745


394minus007

807minus012514

9224

954

10323minus36

80442

4


Table 3 Fitting results for PLS model I and model II

Model 119884 119883 ℎ 1198772

1198831198772

119883(cum) 1198772

1198841198772

119884(cum) Eig 1198762

1198762

cum

I log119870OW 1198831a 1 0519 0519 0928 0928 935 0922 0922

2 0172 0691 0065 0992 309 0841 0988

II log119870OW 1198832b 1 0542 0542 0928 0928 921 0921 0921

2 0181 0723 0063 0992 308 0830 0987a1198831 containing all 18 independent variables

b1198832 the variable 120583 was eliminated from matrix X for the model

the relationship between a matrix Y (containing dependentvariables often only one for QSAR studies) and a matrix X(containing predictor variables) by reducing the dimension ofthe matrices while concurrently maximizing the relationshipbetween them

SIMCA-P (Version 105 Umetrics AB 2004) software wasemployed to perform the PLS analysis The default valuesgiven by the software were used as the initial conditionsfor computation According to the userrsquos guide to SIMCA-P[25] the criterion used to determine the model dimension-ality the number of significant PLS components is cross-validation (CV) With CV the tested PLS component isthought significant if the fraction of the total variation ofthe dependent variables estimated by a component 1198762 ishigher than a significance limit (00975) for thewhole datasetThe model is thought to have a good predicting ability whenthe cumulative 1198762 for the extracted components 1198762cum ismore than 0500 [25] Model adequacy was assessed basedprimarily on the number of PLS principal components (ℎ)1198762

cum the squared correlation coefficient between observedvalues and fitted values (1198772) the standard error (SE) of theestimate the variance ratio of variance analysis (119865) and thesignificance level (119901)

3 Results and Discussion

31 Modeling and Optimizing For the 20 PCBs contained inthe training set PLS analysis was performed with log119870OWas the dependent variable and the 18 independent variablesas the predictor variables yielding QSAR model I For thismodel we have 1198772 = 0992 SE = 0145 119865 = 212 times 103and 119901 = 400 times 10minus20 The fitting results for this modelare listed in Table 3 In Table 3 1198772

119883and 1198772

119884stand for the

fraction of the sum of squares of all the Xrsquos and Y rsquos explainedby the current component 1198772

119883(cum) and 1198772

119884(cum) stand forthe fraction of the cumulative variance of all the Xrsquos and Y rsquosexplained by all extracted components respectively andEigstands for eigenvalue which denotes the importance of thePLS principal component

Variable importance in the projection (VIP) is a parame-ter that shows the importance of a variable in a model in theassistant analysis of PLS modeling According to the manualof SIMCA-P termswith large VIP (gt10) are themost relevantfor explaining dependent variables [25] To obtain an optimalmodel the following PLS analysis procedure was adopted APLSmodel with all the predictor variables was first calculatedand then the variable with the lowest VIP was eliminatedand a new PLS regression was performed yielding a new PLS

model This procedure was repeated until an optimal modelwas obtained The optimal model was selected with respectto 1198762cum 119877

2 SE 119865 and 119901 According to the statistics andmetrics theories a QSAR model with larger values of 1198762cum1198772 and 119865 and smaller values of SE and 119901 tends to be more

stable and reliable than in the opposite caseThe above described PLS analysis procedure with

log119870OW as dependent variable and the 18 independentvariables as the initial predictor variables for the 20 PCBscontained in the training set resulted in model II For thismodel we have 1198772 = 0992 SE = 0151 119865 = 197 times 103 and119901 = 762 times 10

minus20 The fitting results for this model are shownin Table 3 As shown in the table based on the estimatedindexes model I is better than model II indicating all thatthe selected 18 independent variables are necessary for thePLS modeling In model I 2 PLS principal componentswere selected in the model which explained 691 of thevariance of the predictor variables and 992 of the varianceof the dependent variable Based on the estimated indexesemployed in this study this model is statistically significant

32 Analysis and Discussion Based on the optimal modelI the predicted log119870OW for the 20 PCBs contained in thetraining set are as listed in Table 1 A comparison of theobserved and predicted log119870OW is shown in Figure 2 Ascan be seen in Table 1 and Figure 2 the predicted values arewithin the range of plusmn03 log unit from the observed valuesThe observed log119870OW values are close to those predicted bythe optimalmodel and the correlation between observed andpredicted log119870OW is significant For the PCBs studied thedifferences between observed and predicted log119870OW are verysmall so the predicted values are acceptable Note that thecross-validated 1198762cum value (=0988) of the optimal model ismuch larger than 0500 it would seem that thismodel is stableand has good prediction ability and can be used to predict the119870OW of PCBs effectively

WhenX andY are unscaled and uncentered the unscaledcoefficients of the independent variables and the constanttransformed from the PLS results are listed in Table 4 Thesigns of the coefficients of the independent variables in themodel enable one to see the direction in which each affectsthe119870OW of PCBs Based on the unscaled coefficients a QSARequation like that obtained from multiple linear regressioncan be created for the optimal model and the predicted119870OW for other PCBs can be estimated Based on model Ipredictions of 119870OW for the 7 PAHs contained in the testset were generated and listed in Table 1 and the comparisonof observed and predicted log119870OW is shown in Figure 2


4 5 6 7 8 9

4

5

6

7

8

9

Training setTest set

Observed log KOW

Pred

icte

d lo

g KO

W

Figure 2 Plot of observed values versus those predicted by theoptimal model I

Table 4The unscaled coefficients and VIPs of the optimal model I

Variables Unscaled coefficients VIPsTE minus1645 times 10minus4 1412Re 0747 times 10minus4 1404119864HOMO minus10390 1274119864LUMO minus20295 1220119876C6 3418 1082119876C12 3366 1074119876C8 1924 1009119876C11 5212 0992119876C1 minus1999 0947119876C9 7412 0940119876C7 minus1975 0926119876C2 1087 0922119876C10 4711 0834DA 5726 times 10minus4 0794119876C3 6512 0756119876C5 3434 0716119876C4 3535 0690120583 minus0047 0473Constant 6995

The correlation coefficient between observed and predictedlog119870OW in the test set was as high as 0995 larger than thatin the training set As can be seen in Table 1 and Figure 2the predicted log119870OW for the test set are very close tothose observed This indicates that the PLS model has goodpredictive ability

The VIP values for the optimal model were calculatedand listed in Table 4 We can find that the VIP values of TERe 119864HOMO and 119864LUMO are all greater than 10 indicatingthat they are important in governing the 119870OW values of

PCBs TE and Re are the two most important variablessince their VIP values are larger than 140 which is greaterthan the VIP values of the other variables It is expectedthat log119870OW increases with the increase of molecularsize Considering the following examples 4-chlorobiphenyl(log119870OW = 469 molecular weight (MW) = 1885) 4 41015840-dichlorobiphenyl (log119870OW = 528 MW = 2230) 2 4 41015840-trichlorobiphenyl (log119870OW = 574 MW = 2575) 3 31015840 4 41015840-tetrachlorobiphenyl (log119870OW = 652 MW = 2920) whichdiffer between themselves by the number of chlorine sub-stituents the increase in log119870OW as a function of the numberof chlorine substituents and molecular sizes is clearly seenWhen H atom is replaced by Cl atom on the biphenyl119870OW value will be increased [26] So the more chlorineatoms on the biphenyls molecular the greater the119870OW valueWhen isomers share the same molecular weight molecu-lar sizes differ from the arrangements of chlorine atomswhich can be expressed by molecular volume and surfacearea leading to the difference in log119870OW 2-Chlorobiphenyl(log119870OW = 456) 3-chlorobiphenyl (log119870OW = 472) and 4-chlorobiphenyl (log119870OW = 469) are isomers (MW = 1885)but they have different n-octanolwater partition coefficients

The variety of molecular size could be described bythe quantum chemical descriptors of TE and Re in thiswork Firstly for the whole compounds set in this study thedecrease of TEvalues indicates that the molecule containsmore chlorine atoms which might lead to the increase ofmolecular volume Zou et al [27] found that molecularvolume and molecular surface area were highly correlatedfor PCBs sharing similar structures So the decrease of TEvalues might lead to the increase of molecular surface areaMolecular volume and molecular surface area are measuresof molecular size In general the bigger the molecular sizethe stronger the effect of the intermolecular dispersion forceOn the other hand the molecular volume is a measure ofabsorbed energy of the molecule when it forms the holein a solvent Due to stronger polar of water moleculescontributing to stronger binding energy the larger solutemolecules need to absorbmore energy to destroy the bindingbetween water molecules when distributing in the aqueousphase resulting in tending to be allocated in weakly polarsolvents It means that the decrease of TE values might leadto larger log119870OW which is consistent with the study of Zou etal [27] Secondly Re is the expectation value of the operator120588 in the formula of int 1199032120588(119903)1198893119903 It is occasionally seen inthe literature as a measure of molecular volume Althoughin many cases Re correlates poorly with molecular volumethe correlation for PCBs sharing similar structures is quitesignificant A PCB molecule with large electronic spatialextent will have large molecular volume Therefore it can beconcluded that PCBs molecules with lower molecular totalenergy values and larger electronic spatial extent tend to bemore hydrophobic and lipophilic leading to larger log119870OWvalues This implies that the decrease of the TE value and theincrease of the Re value lead to an increase in log119870OW value

The value of log119870OW is in inverse ratio to 119864HOMO and119864LUMO that is large value of 119864HOMO and 119864LUMO would resultin small value of log119870OW for PCBs This is because 119864HOMO


represents the proton acceptance ability in forming hydrogenbond while 119864LUMO represents the proton donation ability inthe formation of hydrogen bond Therefore the compoundswith large values of 119864HOMO and 119864LUMO tend to donate oraccept protons easily In this case hydrogen bond can beeasily formed between PCBs and H

2O molecules and PCBs

are able to enter water phase resulting in low log119870OW value[13]

33 Comparison with the Results from Other Models In [13]there are 3 variables (119864HOMO 119864LUMO and 120572) obtained fromB3LYP6-31G(d) level in the model and the squared correla-tion coefficient is 1198772 = 09484 less than that of the optimalmodel in the present study It was found that there are highcorrelations relating the number of Cl substitution position(119873) to120572 and119864HOMO And taking119873 as theoretical descriptorsto correlate the three-variable models for predicting log119870OWof PCBs the achieved new model is having 1198772 = 09497and exhibiting optimum stability convenience for use andbetter predictive power than that from AM1 method [28]In contrast the squared correlation coefficient 1198772 = 0992obtained by PLS in the present study is obviously larger thanthat reported

Taking molar weight (119872) and melting point (119879119898 ∘C) as

independent variables Wang et al [10] had developed thebinary linear regression equation

log119870OW = 00155119872 + 000107 (119879119898 minus 25)

+ 171 (119899 = 27 119903 = 0997)

(1)

whose predicted log119870OW are also listed in Table 1 whichseems superior to the optimal model I in the present studyobtained solely on quantum chemical descriptors Howeverthe variable melting point in the model equation is anexperimental physicochemical parameter It is impracticalto determine the melting points of all 209 PCBs directly inthe laboratory which makes the model become of limitedapplicability Therefore our optimal model based only onquantum chemical descriptors has wider applicability thanthat based on known physicochemical propertiesThe overallhigh quality of themodel obtained in this study indicates thatit will find application in the estimation of 119870OW of PCBs forwhich experimental values are lacking

4 Conclusions

In this work based on some quantum chemical descriptorscomputed by DFT at the B3LYP6-31G(d) level by the useof PLS analysis QSAR models were obtained for logarithmicn-octanolwater partition coefficients of PCBs The squaredcorrelation coefficient of the optimalmodel is 0992Theopti-mal model has high fitting precision and good predictabilityso it can be applied in the estimation of the n-octanolwaterpartition behavior of PCBs It can generally be concludedthat PCBs with larger electronic spatial extent and lowermolecular total energy values tend to be more hydrophobicand lipophilic

Conflict of Interests

The authors declare no conflict of interests

Acknowledgments

This study was financially supported by the National NaturalScience Foundation of China (21007017) the Program forNew Century Excellent Talents in University (NCET-12-0199) the Specialized Research Fund for the Doctoral Pro-gram of Higher Education (20100172120037) the China Post-doctoral Science Foundation (201003350) and the Guang-dong Natural Science Foundation (9351064101000001)

References

[1] B G Hansen A B Paya-Perez M Rahman and B RLarsen ldquoQSARs for 119870OW and 119870OC of PCB congeners a criticalexamination of data assumptions and statistical approachesrdquoChemosphere vol 39 no 13 pp 2209ndash2228 1999

[2] S-S Liu Y Liu D-Q Yin X-D Wang and L-S WangldquoPrediction of chromatographic relative retention time ofpolychlorinated biphenyls from the molecular electronegativitydistance vectorrdquo Journal of Separation Science vol 29 no 2 pp296ndash301 2006

[3] United Nations Environment Programme ldquoStockholm Con-vention on Persistent Organic Pollutantsrdquo 2001 httpwwwpopsintdocumentsconvtextconvtext enpdf

[4] J P Giesy and K Kannan ldquoDioxin-like and non-dioxin-liketoxic effects of polychlorinated biphenyls (PCBs) implicationsfor risk assessmentrdquo Critical Reviews in Toxicology vol 28 no6 pp 511ndash569 1998

[5] C T Chiou V H Freed DW Schmedding and R L KohnertldquoPartition coefficient and bioaccumulation of selected organicchemicalsrdquo Environmental Science and Technology vol 11 no 5pp 475ndash478 1977

[6] H Konemann ldquoStructure-activity relationships and additivityin fish toxicities of environmental pollutantsrdquoEcotoxicology andEnvironmental Safety vol 4 no 4 pp 415ndash421 1980

[7] G Patil ldquoCorrelation of aqueous solubility and octanol-waterpartition coefficient based on molecular structurerdquo Chemo-sphere vol 22 no 8 pp 723ndash738 1991

[8] DMackay A BobraW Y Shiu and SH Yalkowsky ldquoRelation-ships between aqueous solubility and octanol-water partitioncoefficientsrdquo Chemosphere vol 9 no 11 pp 701ndash711 1980

[9] A Sabljic H Gusten J Hermens andAOpperhuizen ldquoModel-ing octanolwater partition coefficients by molecular topologychlorinated benzenes and biphenylsrdquo Environmental Scienceand Technology vol 27 no 7 pp 1394ndash1402 1993

[10] L S Wang Y H Zhao and H Gao ldquoPredicting aqueoussolubility and octanolwater partition coefficients of organicchemicals from molar volumerdquo Environmental Chemistry vol11 pp 55ndash70 1992

[11] G-N Lu Z Dang X-Q Tao and X-Y Yi ldquoEstimation of watersolubility of polycyclic aromatic hydrocarbons using quantumchemical descriptors and partial least squaresrdquo QSAR andCombinatorial Science vol 27 no 5 pp 618ndash626 2008

[12] G-N Lu X-Q Tao Z Dang X-Y Yi and C Yang ldquoEstimationof n-octanolwater partition coefficients of polycyclic aromatichydrocarbons by quantum chemical descriptorsrdquo Central Euro-pean Journal of Chemistry vol 6 no 2 pp 310ndash318 2008


[13] W Zhou Z Zhai Z Wang and L Wang ldquoEstimation of n-octanolwater partition coefficients (119870OW) of all PCB congenersby density functional theoryrdquo Journal of Molecular Structurevol 755 no 1ndash3 pp 137ndash145 2005

[14] F Verweij K Booij K Satumalay N Van Der Molen andR Van Der Oost ldquoAssessment of bioavailable PAH PCB andOCP concentrations in water using semipermeable membranedevices (SPMDs) sediments and caged carprdquoChemosphere vol54 no 11 pp 1675ndash1689 2004

[15] L Jantschi S Bolboaca and R Sestras ldquoMeta-heuristics onquantitative structure -activity relationships study on polychlo-rinated biphenylsrdquo Journal of Molecular Modeling vol 16 pp377ndash386 2010

[16] P Ruiz O Faroon C J Moudgal H Hansen C T DeRosa and M Mumtaz ldquoPrediction of the health effects ofpolychlorinated biphenyls (PCBs) and their metabolites usingquantitative structure-activity relationship (QSAR)rdquo ToxicologyLetters vol 181 no 1 pp 53ndash65 2008

[17] L-T Qin S-S Liu H-L Liu and H-L Ge ldquoA new predictivemodel for the bioconcentration factors of polychlorinatedbiphenyls (PCBs) based on the molecular electronegativitydistance vector (MEDV)rdquo Chemosphere vol 70 no 9 pp 1577ndash1587 2008

[18] K Tuppurainen and J Ruuskanen ldquoElectronic eigenvalue(EEVA) a new QSARQSPR descriptor for electronic sub-stituent effects based on molecular orbital energies A QSARapproach to the Ah receptor binding affinity of polychlorinatedbiphenyls (PCBs) dibenzo-p-dioxins (PCDDs) and dibenzofu-rans (PCDFs)rdquo Chemosphere vol 41 no 6 pp 843ndash848 2000

[19] Z Daren ldquoQSPR studies of PCBs by the combination of geneticalgorithms and PLS analysisrdquoComputers and Chemistry vol 25no 2 pp 197ndash204 2001

[20] E B de Melo ldquoA new quantitative structure-property rela-tionship model to predict bioconcentration factors of poly-chlorinated biphenyls (PCBs) in fishes using E-state indexand topological descriptorsrdquo Ecotoxicology and EnvironmentalSafety vol 75 no 1 pp 213ndash222 2012

[21] J Y Long H B Yi X K Liu and Y F Wang ldquoQuantitativestructure-property relationship for polychlorinated biphenylstoxicity and structure by density functional theoryrdquoActa Chim-ica Sinica vol 70 pp 949ndash960 2012

[22] G-N Lu X-Q Tao Z H I Dang W Huang and Z LildquoQuantitative structureproperty relationships on dissolvabilityof pcddfs using quantum chemical descriptors and partial leastsquaresrdquo Journal of Theoretical and Computational Chemistryvol 9 no 1 pp 9ndash22 2010

[23] M J Frisch G W Trucks H B Schlegel et al Gaussian 03Revision B 01 Gaussian Pittsburgh Pa USA 2003

[24] S Wold M Sjostrom and L Eriksson ldquoPLS-regression a basictool of chemometricsrdquoChemometrics and Intelligent LaboratorySystems vol 58 no 2 pp 109ndash130 2001

[25] A B Umetrics USerrsquos Guide to SIMCA-P SIMCA-P+ Version10 0 Umea Sweden 2005

[26] B Z Zhao Y Y Li X R Hao et al ldquoTheoretical calculation ofthe octanolwater partition coefficient for polychlorobiphenylsrdquoJournal of Northeast Normal University vol 36 no 2 pp 41ndash442004

[27] J-W Zou Y-J Jiang G-X Hu M Zeng S-L Zhuang and Q-S Yu ldquoQSPRQSAR studies on the physicochemical propertiesand biological activities of polychlorinated biphenylsrdquo ActaPhysico vol 21 no 3 pp 267ndash272 2005

[28] X-Y Han Z-YWang Z-C Zhai and L-S Wang ldquoEstimationof n-octanolwater partition coefficients (119870OW) of all PCBcongeners by ab initio and a Cl substitution position methodrdquoQSAR and Combinatorial Science vol 25 no 4 pp 333ndash3412006

Submit your manuscripts athttpwwwhindawicom

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Inorganic ChemistryInternational Journal of

Hindawi Publishing Corporation httpwwwhindawicom Volume 2014

International Journal ofPhotoenergy


Carbohydrate Chemistry

International Journal of


Journal of

Chemistry


Advances in

Physical Chemistry

Hindawi Publishing Corporationhttpwwwhindawicom

Analytical Methods in Chemistry

Journal of

Volume 2014

Bioinorganic Chemistry and ApplicationsHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

SpectroscopyInternational Journal of


The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014

Medicinal ChemistryInternational Journal of


Chromatography Research International


Applied ChemistryJournal of



Theoretical ChemistryJournal of


Journal of

Spectroscopy

Analytical ChemistryInternational Journal of


Journal of


Quantum Chemistry


Organic Chemistry International

ElectrochemistryInternational Journal of



CatalystsJournal of


property suitable for the characterization of the lipophilicityof the compounds such as PCBs119870OW has been shown to cor-relate with aquatic solubility toxicity and bioaccumulation[5ndash7] and as such it is a useful parameter for the assessmentof the environmental fate and impact of xenobiotic tracecontaminants [8] During the last 50 years 119870OW has provento be a valuable tool in many areas of natural sciences chem-istry biochemistry biology pharmacology environmentalsciences and so forth During that period many hundredsor even thousands of scientific publications have been madein which the successful application of 119870OW in correlationwith many physical chemical or biological properties hasbeen demonstrated for a large variety of organic chemicalsUnfortunately many correlations have been also publishedin which the processes studied have nothing in commonwith the process of partition between water and n-octanolphases It must be emphasized that in order to correlate twoproperties do not have to be related both of themmay simplybe related to a third property The shake flask is the classicalmethod for measuring 119870OW and this procedure will givereproducible and accurate results for chemicalswith log119870OWAccurate results can also be obtained for more hydrophobicchemicals but this will need a very precise and skillfulhandling and the use of very pure n-octanol The problemwith this procedure is that the complete phase separation isdifficult to achieve and this may lead to the underestimationof119870OW particularly for themore hydrophobic chemicals [9]

It is impractical to measure the 119870OW of all the PCBsdirectly in the laboratory because of the extremely lowwater solubility of some high-chlorinated PCBs The lackof complete reliable and comparable data has led to thedevelopment of different119870OW estimation methods With theadvent of inexpensive and rapid computation there has beena remarkable growth in the area of quantitative structure-activity relationships (QSAR) which correlate the proper-ties of pollutants with relevant properties and moleculardescriptors [11] A large number of calculation methods havebeen presently developed for estimation of the partitioncoefficients with varying success and applicability Thesemethods are cable of predicting environmental behaviors notonly for common environmental pollutants but also for thosechemicals that have not yet been synthesized or those thatcannot be examined experimentally due to their extremelyhazardous nature According to the descriptors used thesemethods can be classified into two groups empirical andtheoretical methods [12] Hansen et al [1] used the degreeof chlorination in the orthoposition of the PCB molecule aspredictor variable together with either the calculated molec-ular total surface area (TSA) or the gas chromatographicrelative retention times (RRT) to predict the log119870OW andsoil sorption coefficients (log119870OC) for all 209 PCB congenerswhich were based on 53 and 48 experimental data pointsrespectively Also reporting the relationship between 119870OWand 119870OC which is promising for 119870OW could proceed with119870OC However TSA and RRT are both empirical moleculardescriptors Firstly the accurate determination of empiricalmolecular descriptorsmay be difficult and expensive in termsof cost and time or even impossible for some PCBs whichmight have been not synthesized or purified Meanwhile the

experimental errors may be introduced especially for thosecongeners difficult to be separated and identified by chro-matography Furthermore the empirical molecular descrip-tors often reflect complex and multiple physical interactionsHence the interpretation of the respective property could bedifficult and ambiguous [13] Many attempts have been madeto model the physicochemical properties and bioactivities ofPCBs by utilizingQSAR techniques [14ndash21] For instanceQinet al [17] reported the correlation of bioconcentration factorsand molecular electronegativity distance vector (MEDV)of PCBs based on molecular structure Tuppurainen andRuuskanen [18] introduced a newQSARdescriptor electroniceigenvalue (EEVA) based on molecular orbital energies topredict the Ah receptor binding affinity of PCBs polychlo-rinated dibenzo-p-dioxins and dibenzofurans (PCDDFs)Daren [19] reported QSPR studies of119870OW 119878W 119884W and119870OCof all 209 PCBs and biphenyl by the combination of geneticalgorithms and PLS analysis

Modern theoretical method in quantum chemistry suchas density functional theory (DFT) with high calculation pre-cision should have its advantages in estimating the propertiesof environmental toxics such as PCBs PCDDFs [13 22]The aim of the present study is to explore QSAR modelrelating to log119870OW of 27 PCBs by using partial least squares(PLS) analysis with optimizing procedure based on reportedlog119870OW data and quantum chemical descriptors computedby DFT contained in Gaussian 03 in an attempt to developreliable and predictive QSARmodel and identify the primarymolecular factors governing the 119870OW of PCBs

2 Materials and Methods

21 Target PCBs A total of 27 PCBs whose 119870OW andorlog119870OW were previously published were chosen to constitutethe training set and the test set in this work The trainingset consists of 20 of these 27 PCBs selected randomly andthe remaining 7 PCBs constitute the test set Their chemicalabstracts service numbers (CAS no) and reported log119870OWare listed in Table 1

22 Calculation and Selection of the Descriptors The molec-ular modeling system HyperChem (Release 70 HypercubeInc 2002) was used to construct and view all molecularstructures Molecular geometry was optimized and quantumchemical descriptors were computed using the B3LYP hybridfunctional of DFT in conjunction with 6-31G(d) a split-valence basis set with polarization function The B3LYPcalculations were performed using the quantum chemicalcomputation software Gaussian 03 [23] All calculations wererun on an Intel Core i5-2410M CPU230GHz computerequipped with 200GB of internal memory and MicrosoftWindows 7 professional operating system

According to the chemometrics theory it is suggested thataQSARmodel should include asmany relevant descriptors aspossible to increase the probability of a good characterizationfor a class of compounds [24] In this study 18 independentquantum chemical variables were selected for developingQSAR models The descriptors cover the dihedral angle of







ClX ClY

1

2 3

4

56

9 8

7

1211

10




Table2Quantum

chem

icaldescrip

torsforthe

PCBs

studied

No


MO

119876C1

119876C2

119876C3

119876C4

119876C5

119876C6

119876C7

119876C8

119876C9

119876C10

119876C11

119876C12

Re120583

TE1


0010126minus017937minus01339

5minus01248

7minus01339

5minus017937

010126minus017937minus01339

5minus01248

7minus01339

5minus017937

2336

596

000

00minus46

330

272

5613minus02322

3minus002

747

007

290minus01156

4minus01156


9006

717minus01479

3minus01354

8minus01244


12764

356

16832minus92

289

733

000minus022

671minus004

365

01034

9minus01805

3minus006


01003


1minus01230


63399

230

21220minus92

289

894

11008minus024

310minus002

654

005

227minus01026

5minus01368

5minus01192


6005

227minus01026

5minus01368

5minus01192


63242

151

200

16minus1382

489

95

5559minus023

637minus003

739

007

579minus01130

4minus01368

0minus005


7006


3minus01336

1minus01649

04150

644

21687minus1382

492

16

8121minus024

564minus00322

0004

787minus008

812minus01356


799minus014376

004

642minus009

074minus01370

2minus01179

6minus01306

2minus014315

4428

552

1857

1minus1842

084

47


578

007


3minus005


8006

733minus01448

2minus01358

7minus006

134minus01343

5minus016175

6126

716

1429

7minus1842

087

88

5616minus024

156minus004

537

007


867minus007

657minus01623

8006

469minus01476

3minus01349

4minus01225


650

70323

226

21minus1842

082

09

12687minus024

760minus005

378

007


7minus006

785minus007

710minus016166

006


033minus01236

3minus01336

4minus01428

567

00831

1948

5minus23

01677

210

000minus023

831minus006

630

01096

3minus01783

8minus007

907minus00744

6minus01334

7minus01697

001096

3minus01783

8minus007

907minus00744

6minus01334

7minus01697

080

12630

267

16minus23

016757

118360minus02529

8minus004

434

005

232minus009

797minus009

264minus006

419minus01280

4minus01343

3004

160minus008


2minus006

856minus014180

718116

2264

02minus2761263

412

8064minus02536

0minus004

744

004

992minus008

328minus01374

8minus006

497minus007

717minus01425

1004

568minus008


869minus01427

77317571

15120minus2761268

013

8995minus025

762minus004

957

006

408minus009

995minus008

788minus007

241minus008

788minus009

995

002

910minus01336


963

48796

21628minus2761249

114

8565minus026

235minus004

741

004

784minus009

803minus009

259minus006

444minus01280

2minus01338

8004

784minus009

803minus009

259minus006

444minus01280

2minus01338

888

80956

31952minus3220

848

115

8114minus025

785minus005

272

004

819minus008


481minus007

739minus014173

004

819minus008


481minus007

739minus014173

9135369

002

78minus3220

8572

1690

00minus026

706minus004

525

004

104minus007



511

004

104minus007



511

7602

549

000

00minus3220

859

717

8588minus026

513minus005

927

004

910minus009

391minus009

064minus007

515minus007

354minus013392

004

910minus009

391minus009

064minus007

515minus007

354minus013392

1158

265

611

648minus4140

024

218

8998minus026

008minus005

592

003

906minus008

305minus007

831minus01143

7minus007

831minus008

305

003

906minus008

305minus007

831minus01143

7minus007

831minus008

305

9319431

000

00minus4140

027

719

9083minus026

526minus006

331

005

601minus009

023minus008

906minus006

972minus008

907minus009

023

003

596minus008

464minus009

111minus00743

5minus00744

7minus01197

81208

396

910

110minus45

99608

620

9001minus026

670minus006

373

004

327minus007

929minus008

962minus006

896minus008

962minus007

929

004

327minus007

929minus008

962minus006

896minus008

962minus007

929

1257959

3000

00minus50

5919

3321

000minus022

180minus004

264

010353minus01765

2minus01354

1minus006

248minus01354

1minus01765

201034



7minus017931

3744

110

21391minus92

289

9022

5589minus023

832minus003

813

00748

7minus01129

8minus01339


823minus016185

006


3923

000

07279minus1382

492

223

000minus022

591minus005

162

010551minus01763

9minus01350

5minus006

209minus01350

5minus01763

9010551minus01763

9minus01350

5minus006

209minus01350

5minus01763

956

91897

000

00minus1382

495

024

000minus02322

9minus005

925

01124

1minus01806

1minus007

848minus007

511minus01338

0minus01706

7010325minus01745


122minus01348

1minus017556

6796

892

15815minus1842

0855

2582

68minus025

010minus003

958

004

559minus008


836minus014172

004

559minus008


836minus014172

5819703

02171minus23

01678

626

5556minus024

687minus005

342

00746

2minus01130


8minus006

896minus016122

007

015minus01450

7minus008

147minus007

164minus01324

1minus01556

668

27221

239

61minus23

01677

927

9059minus025

929minus005

534

005

332minus009

406minus007


793minus009

406

003168minus00745


394minus007

807minus012514

9224

954

10323minus36

80442

4



Model 119884 119883 ℎ 1198772

1198831198772

119883(cum) 1198772

1198841198772

119884(cum) Eig 1198762

1198762

cum

I log119870OW 1198831a 1 0519 0519 0928 0928 935 0922 0922

2 0172 0691 0065 0992 309 0841 0988

II log119870OW 1198832b 1 0542 0542 0928 0928 921 0921 0921








119883and 1198772

119884stand for the


119883(cum) and 1198772











4 5 6 7 8 9

4

5

6

7

8

9


Observed log KOW

Pred

icte

d lo

g KO

W
















log119870OW = 00155119872 + 000107 (119879119898 minus 25)

+ 171 (119899 = 27 119903 = 0997)

(1)


4 Conclusions




Acknowledgments


References







































Journal of

Chemistry


Advances in

Physical Chemistry



Journal of

Volume 2014














Journal of

Spectroscopy



Journal of


Quantum Chemistry






CatalystsJournal of







ClX ClY

1

2 3

4

56

9 8

7

1211

10




Table2Quantum

chem

icaldescrip

torsforthe

PCBs

studied

No


MO

119876C1

119876C2

119876C3

119876C4

119876C5

119876C6

119876C7

119876C8

119876C9

119876C10

119876C11

119876C12

Re120583

TE1


0010126minus017937minus01339

5minus01248

7minus01339

5minus017937

010126minus017937minus01339

5minus01248

7minus01339

5minus017937

2336

596

000

00minus46

330

272

5613minus02322

3minus002

747

007

290minus01156

4minus01156


9006

717minus01479

3minus01354

8minus01244


12764

356

16832minus92

289

733

000minus022

671minus004

365

01034

9minus01805

3minus006


01003


1minus01230


63399

230

21220minus92

289

894

11008minus024

310minus002

654

005

227minus01026

5minus01368

5minus01192


6005

227minus01026

5minus01368

5minus01192


63242

151

200

16minus1382

489

95

5559minus023

637minus003

739

007

579minus01130

4minus01368

0minus005


7006


3minus01336

1minus01649

04150

644

21687minus1382

492

16

8121minus024

564minus00322

0004

787minus008

812minus01356


799minus014376

004

642minus009

074minus01370

2minus01179

6minus01306

2minus014315

4428

552

1857

1minus1842

084

47


578

007


3minus005


8006

733minus01448

2minus01358

7minus006

134minus01343

5minus016175

6126

716

1429

7minus1842

087

88

5616minus024

156minus004

537

007


867minus007

657minus01623

8006

469minus01476

3minus01349

4minus01225


650

70323

226

21minus1842

082

09

12687minus024

760minus005

378

007


7minus006

785minus007

710minus016166

006


033minus01236

3minus01336

4minus01428

567

00831

1948

5minus23

01677

210

000minus023

831minus006

630

01096

3minus01783

8minus007

907minus00744

6minus01334

7minus01697

001096

3minus01783

8minus007

907minus00744

6minus01334

7minus01697

080

12630

267

16minus23

016757

118360minus02529

8minus004

434

005

232minus009

797minus009

264minus006

419minus01280

4minus01343

3004

160minus008


2minus006

856minus014180

718116

2264

02minus2761263

412

8064minus02536

0minus004

744

004

992minus008

328minus01374

8minus006

497minus007

717minus01425

1004

568minus008


869minus01427

77317571

15120minus2761268

013

8995minus025

762minus004

957

006

408minus009

995minus008

788minus007

241minus008

788minus009

995

002

910minus01336


963

48796

21628minus2761249

114

8565minus026

235minus004

741

004

784minus009

803minus009

259minus006

444minus01280

2minus01338

8004

784minus009

803minus009

259minus006

444minus01280

2minus01338

888

80956

31952minus3220

848

115

8114minus025

785minus005

272

004

819minus008


481minus007

739minus014173

004

819minus008


481minus007

739minus014173

9135369

002

78minus3220

8572

1690

00minus026

706minus004

525

004

104minus007



511

004

104minus007



511

7602

549

000

00minus3220

859

717

8588minus026

513minus005

927

004

910minus009

391minus009

064minus007

515minus007

354minus013392

004

910minus009

391minus009

064minus007

515minus007

354minus013392

1158

265

611

648minus4140

024

218

8998minus026

008minus005

592

003

906minus008

305minus007

831minus01143

7minus007

831minus008

305

003

906minus008

305minus007

831minus01143

7minus007

831minus008

305

9319431

000

00minus4140

027

719

9083minus026

526minus006

331

005

601minus009

023minus008

906minus006

972minus008

907minus009

023

003

596minus008

464minus009

111minus00743

5minus00744

7minus01197

81208

396

910

110minus45

99608

620

9001minus026

670minus006

373

004

327minus007

929minus008

962minus006

896minus008

962minus007

929

004

327minus007

929minus008

962minus006

896minus008

962minus007

929

1257959

3000

00minus50

5919

3321

000minus022

180minus004

264

010353minus01765

2minus01354

1minus006

248minus01354

1minus01765

201034



7minus017931

3744

110

21391minus92

289

9022

5589minus023

832minus003

813

00748

7minus01129

8minus01339


823minus016185

006


3923

000

07279minus1382

492

223

000minus022

591minus005

162

010551minus01763

9minus01350

5minus006

209minus01350

5minus01763

9010551minus01763

9minus01350

5minus006

209minus01350

5minus01763

956

91897

000

00minus1382

495

024

000minus02322

9minus005

925

01124

1minus01806

1minus007

848minus007

511minus01338

0minus01706

7010325minus01745


122minus01348

1minus017556

6796

892

15815minus1842

0855

2582

68minus025

010minus003

958

004

559minus008


836minus014172

004

559minus008


836minus014172

5819703

02171minus23

01678

626

5556minus024

687minus005

342

00746

2minus01130


8minus006

896minus016122

007

015minus01450

7minus008

147minus007

164minus01324

1minus01556

668

27221

239

61minus23

01677

927

9059minus025

929minus005

534

005

332minus009

406minus007


793minus009

406

003168minus00745


394minus007

807minus012514

9224

954

10323minus36

80442

4



Model 119884 119883 ℎ 1198772

1198831198772

119883(cum) 1198772

1198841198772

119884(cum) Eig 1198762

1198762

cum

I log119870OW 1198831a 1 0519 0519 0928 0928 935 0922 0922

2 0172 0691 0065 0992 309 0841 0988

II log119870OW 1198832b 1 0542 0542 0928 0928 921 0921 0921








119883and 1198772

119884stand for the


119883(cum) and 1198772











4 5 6 7 8 9

4

5

6

7

8

9


Observed log KOW

Pred

icte

d lo

g KO

W
















log119870OW = 00155119872 + 000107 (119879119898 minus 25)

+ 171 (119899 = 27 119903 = 0997)

(1)


4 Conclusions




Acknowledgments


References







































Journal of

Chemistry


Advances in

Physical Chemistry



Journal of

Volume 2014














Journal of

Spectroscopy



Journal of


Quantum Chemistry






CatalystsJournal of


Table2Quantum

chem

icaldescrip

torsforthe

PCBs

studied

No


MO

119876C1

119876C2

119876C3

119876C4

119876C5

119876C6

119876C7

119876C8

119876C9

119876C10

119876C11

119876C12

Re120583

TE1


0010126minus017937minus01339

5minus01248

7minus01339

5minus017937

010126minus017937minus01339

5minus01248

7minus01339

5minus017937

2336

596

000

00minus46

330

272

5613minus02322

3minus002

747

007

290minus01156

4minus01156


9006

717minus01479

3minus01354

8minus01244


12764

356

16832minus92

289

733

000minus022

671minus004

365

01034

9minus01805

3minus006


01003


1minus01230


63399

230

21220minus92

289

894

11008minus024

310minus002

654

005

227minus01026

5minus01368

5minus01192


6005

227minus01026

5minus01368

5minus01192


63242

151

200

16minus1382

489

95

5559minus023

637minus003

739

007

579minus01130

4minus01368

0minus005


7006


3minus01336

1minus01649

04150

644

21687minus1382

492

16

8121minus024

564minus00322

0004

787minus008

812minus01356


799minus014376

004

642minus009

074minus01370

2minus01179

6minus01306

2minus014315

4428

552

1857

1minus1842

084

47


578

007


3minus005


8006

733minus01448

2minus01358

7minus006

134minus01343

5minus016175

6126

716

1429

7minus1842

087

88

5616minus024

156minus004

537

007


867minus007

657minus01623

8006

469minus01476

3minus01349

4minus01225


650

70323

226

21minus1842

082

09

12687minus024

760minus005

378

007


7minus006

785minus007

710minus016166

006


033minus01236

3minus01336

4minus01428

567

00831

1948

5minus23

01677

210

000minus023

831minus006

630

01096

3minus01783

8minus007

907minus00744

6minus01334

7minus01697

001096

3minus01783

8minus007

907minus00744

6minus01334

7minus01697

080

12630

267

16minus23

016757

118360minus02529

8minus004

434

005

232minus009

797minus009

264minus006

419minus01280

4minus01343

3004

160minus008


2minus006

856minus014180

718116

2264

02minus2761263

412

8064minus02536

0minus004

744

004

992minus008

328minus01374

8minus006

497minus007

717minus01425

1004

568minus008


869minus01427

77317571

15120minus2761268

013

8995minus025

762minus004

957

006

408minus009

995minus008

788minus007

241minus008

788minus009

995

002

910minus01336


963

48796

21628minus2761249

114

8565minus026

235minus004

741

004

784minus009

803minus009

259minus006

444minus01280

2minus01338

8004

784minus009

803minus009

259minus006

444minus01280

2minus01338

888

80956

31952minus3220

848

115

8114minus025

785minus005

272

004

819minus008


481minus007

739minus014173

004

819minus008


481minus007

739minus014173

9135369

002

78minus3220

8572

1690

00minus026

706minus004

525

004

104minus007



511

004

104minus007



511

7602

549

000

00minus3220

859

717

8588minus026

513minus005

927

004

910minus009

391minus009

064minus007

515minus007

354minus013392

004

910minus009

391minus009

064minus007

515minus007

354minus013392

1158

265

611

648minus4140

024

218

8998minus026

008minus005

592

003

906minus008

305minus007

831minus01143

7minus007

831minus008

305

003

906minus008

305minus007

831minus01143

7minus007

831minus008

305

9319431

000

00minus4140

027

719

9083minus026

526minus006

331

005

601minus009

023minus008

906minus006

972minus008

907minus009

023

003

596minus008

464minus009

111minus00743

5minus00744

7minus01197

81208

396

910

110minus45

99608

620

9001minus026

670minus006

373

004

327minus007

929minus008

962minus006

896minus008

962minus007

929

004

327minus007

929minus008

962minus006

896minus008

962minus007

929

1257959

3000

00minus50

5919

3321

000minus022

180minus004

264

010353minus01765

2minus01354

1minus006

248minus01354

1minus01765

201034



7minus017931

3744

110

21391minus92

289

9022

5589minus023

832minus003

813

00748

7minus01129

8minus01339


823minus016185

006


3923

000

07279minus1382

492

223

000minus022

591minus005

162

010551minus01763

9minus01350

5minus006

209minus01350

5minus01763

9010551minus01763

9minus01350

5minus006

209minus01350

5minus01763

956

91897

000

00minus1382

495

024

000minus02322

9minus005

925

01124

1minus01806

1minus007

848minus007

511minus01338

0minus01706

7010325minus01745


122minus01348

1minus017556

6796

892

15815minus1842

0855

2582

68minus025

010minus003

958

004

559minus008


836minus014172

004

559minus008


836minus014172

5819703

02171minus23

01678

626

5556minus024

687minus005

342

00746

2minus01130


8minus006

896minus016122

007

015minus01450

7minus008

147minus007

164minus01324

1minus01556

668

27221

239

61minus23

01677

927

9059minus025

929minus005

534

005

332minus009

406minus007


793minus009

406

003168minus00745


394minus007

807minus012514

9224

954

10323minus36

80442

4



Model 119884 119883 ℎ 1198772

1198831198772

119883(cum) 1198772

1198841198772

119884(cum) Eig 1198762

1198762

cum

I log119870OW 1198831a 1 0519 0519 0928 0928 935 0922 0922

2 0172 0691 0065 0992 309 0841 0988

II log119870OW 1198832b 1 0542 0542 0928 0928 921 0921 0921








119883and 1198772

119884stand for the


119883(cum) and 1198772











4 5 6 7 8 9

4

5

6

7

8

9


Observed log KOW

Pred

icte

d lo

g KO

W
















log119870OW = 00155119872 + 000107 (119879119898 minus 25)

+ 171 (119899 = 27 119903 = 0997)

(1)


4 Conclusions




Acknowledgments


References







































Journal of

Chemistry


Advances in

Physical Chemistry



Journal of

Volume 2014














Journal of

Spectroscopy



Journal of


Quantum Chemistry






CatalystsJournal of



Model 119884 119883 ℎ 1198772

1198831198772

119883(cum) 1198772

1198841198772

119884(cum) Eig 1198762

1198762

cum

I log119870OW 1198831a 1 0519 0519 0928 0928 935 0922 0922

2 0172 0691 0065 0992 309 0841 0988

II log119870OW 1198832b 1 0542 0542 0928 0928 921 0921 0921








119883and 1198772

119884stand for the


119883(cum) and 1198772











4 5 6 7 8 9

4

5

6

7

8

9


Observed log KOW

Pred

icte

d lo

g KO

W
















log119870OW = 00155119872 + 000107 (119879119898 minus 25)

+ 171 (119899 = 27 119903 = 0997)

(1)


4 Conclusions




Acknowledgments


References







































Journal of

Chemistry


Advances in

Physical Chemistry



Journal of

Volume 2014














Journal of

Spectroscopy



Journal of


Quantum Chemistry






CatalystsJournal of


4 5 6 7 8 9

4

5

6

7

8

9


Observed log KOW

Pred

icte

d lo

g KO

W
















log119870OW = 00155119872 + 000107 (119879119898 minus 25)

+ 171 (119899 = 27 119903 = 0997)

(1)


4 Conclusions




Acknowledgments


References







































Journal of

Chemistry


Advances in

Physical Chemistry



Journal of

Volume 2014














Journal of

Spectroscopy



Journal of


Quantum Chemistry






CatalystsJournal of








log119870OW = 00155119872 + 000107 (119879119898 minus 25)

+ 171 (119899 = 27 119903 = 0997)

(1)


4 Conclusions




Acknowledgments


References







































Journal of

Chemistry


Advances in

Physical Chemistry



Journal of

Volume 2014














Journal of

Spectroscopy



Journal of


Quantum Chemistry






CatalystsJournal of



























Journal of

Chemistry


Advances in

Physical Chemistry



Journal of

Volume 2014














Journal of

Spectroscopy



Journal of


Quantum Chemistry






CatalystsJournal of

Research Article Estimation of n -Octanol/Water...

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