Relation between C1s XPS binding energy and calculated partial charge of carbon atoms in polymers

Journal of Molecular Structure: THEOCHEM 725 (2005) 5–8 www.elsevier.com/locate/theochem

Relation between C1s XPS binding energy and calculated partial charge of carbon atoms in polymers Eufrozina A. Hoffmanna, Tama´s Ko¨rtve´lyesia, Eugene Wiluszb, Ljiljana S. Korugic-Karaszc, Frank E. Karaszc, Zoltan A. Feketea,* a

Department of Physical Chemistry, University of Szeged, P.O. Box 105, Szeged H-6701, Hungary b US Army Natick Soldier Center, Natick, MA 01760, USA c Department of Polymer Science and Engineering, University of Massachusetts, Amherst MA 01003, USA Received 29 October 2004; accepted 16 February 2005 Available online 31 May 2005

Abstract A correlation has been developed for corrected experimental C1s XPS binding energies of polymers vs. calculated partial charges of carbon atom in model oligomers. Representative polymers containing a wide variety of heteroatoms such as Cl, F, O, N, Si have been selected. Molecular geometry was determined with the semiempirical valence orbital AM1 method, and Mulliken population analysis was applied to calculate partial atomic charges. The resulting charge-potential model equation can be used to predict core electron binding energies in polymers and deconvolute XPS spectra. q 2005 Elsevier B.V. All rights reserved. Keywords: Semiempirical quantum-chemical calculations; AM1; Core electron binding energy; ESCA/XPS; Polymers

1. Introduction X-ray photoelectron spectroscopy (XPS) is a widely used method for surface analysis of polymer materials [1–3]. Theoretical considerations can greatly aid assignment of the peaks in complex spectra arising from contributions of different atoms in the molecules. It has long been recognized that partial atomic charges play a decisive role in determining the core binding energy Eb in small molecules [4]. For fluoropolymers Clark et al. used calculated charges to correlate chemical shifts of C1s energies [5]. Early calculations applied the CNDO approximation to theoretically estimate charges, which method provided good fits within groups of related compounds, however the correlation was less satisfactory for reducing spectra from systems containing widely different chemical substituents [5]. Recently there have been applications of ab initio methods to calculating core electron binding energies * Corresponding author. Tel.: C36 62544628; fax: C1 7816235997. E-mail address: [email protected] (Z.A. Fekete).

0166-1280/$ - see front matter q 2005 Elsevier B.V. All rights reserved. doi:10.1016/j.theochem.2005.02.021

[6–8] for polymers, based ultimately on oligomer models containing a few monomer units. Although such a calculation can provide Eb directly in principle, it was found that an added empirical shift was necessary for the results to agree with experiment. For systems of moderate size this method provides good results, but the computational time grows rapidly if the molecules contain many heavier (non-hydrogen) atoms. In contrast, the semiempirical AM1 method yields adequate geometries for large molecules with relatively modest computational effort [9–12]. Sleigh et al. demonstrated the utility of AM1 calculations for determining partial atomic charges to correlate with XPS core binding energies [13] for the elements H, C, N, O, F as well as a number of metals. They did not apply the additional correction related to the socalled intermolecular Madelung potential (see below), and halogenated polymers were excluded from the correlation of C1s binding energies. It was found in our previous work [14] that the chargepotential method combined with AM1 calculated geometries and NDDO charges yields good correlation for polymer structures whose XPS data has been collated in standard databases. This calculation makes it feasible to estimate chemical shifts for various molecular architectures

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Table 1 Experimental binding energies, Eb [eV], calculated group parameters qi [e] and Vm(i) [eV], and corrected binding energies EbKVm(i) [eV] for selected polymers Polymer name

Group formula

Eb [eV]

qi [e]

Vm(i) [eV]

EbKVm(i) [eV]

Fomblin Y (perfluoro-polyether)

(–OC†F(CF3)CF2)n(–OCF2)m (–OCF(CF3)C†F2)n(–OCF2)m (–OCF(C†F3)CF2)n(–OCF2)m (–OCF(CF3)CF2)n(–OC†F2)m (–C6H2(CH3)2O–)n, C(3,5)Z† (–C6H2(C†H3)2O–)n (–C6H2(CH3)2O–)n, C(2,6)Z† (–C6H2(CH3)2O–)n, C(1,4)Z† (–C†H2C(CH3)(C(O)OCH2CH2Cl)–)n (–CH2C†(CH3)(C(O)OCH2CH2Cl)–)n (–CH2C(CH3)(C(O)OC†H2CH2Cl)–)n (–CH2C(CH3)(C(O)OCH2C†H2Cl)–)n (–CH2C(CH3)(C†(O)OCH2CH2Cl)–)n (–OCH2CH(CH2OCH2CH2C†H2Si(OCH3)3)–)n (–OCH2CH(CH2OCH2C†H2CH2Si(OCH3)3)–)n (–OCH2CH(CH2OCH2CH2CH2Si(OC†H3)3)–)n (–OCH2CH(CH2OC†H2CH2CH2Si(OCH3)3)–)n (–OCH2CH(C†H2OCH2CH2CH2Si(OCH3)3)–)n (–OC†H2CH(CH2OCH2CH2CH2Si(OCH3)3)–)n (–OCH2C†H(CH2OCH2CH2CH2Si(OCH3)3)–)n (–CH2C((CH3)C(O)OCH2CH2C†H2Si(OCH3)3)–)n (–CH2C((CH3)C(O)OCH2C†H2CH2Si(OCH3)3)–)n (–C†H2C((CH3)C(O)OCH2CH2CH2Si(OCH3)3)–)n (–CH2C((CH3)C(O)OCH2CH2CH2Si(OC†H3)3)–)n (–CH2C((CH3)C(O)OC†H2CH2CH2Si(OCH3)3)–)n (–CH2C((CH3)C†(O)OCH2CH2CH2Si(OCH3)3)–)n (–C†H2(C(O)NH2–)n (–Si(C†H3)2–O–)n (–C†H2C†H2NH–)n (–CH2C†H2O–)n (–C†H2C(O)O–)n (–CH2C†(O)O–)n (–CH2CH(C†6H4OH)–)n (–C†H2C†H(C6H4OH)–)n (–CH(C†H3)CH2O–)n (–CH(CH3)C†H2O–)n (–C†H(CH3)CH2O–)n (–C†H2C(CH3)(CONH2)–)n (–CH2C(C†H3)(CONH2)–)n (–CH2C†(CH3)(CONH2)–)n (–CH2C(CH3)(C†ONH2)–)n (–CH2C†H(C(O)OCH3)–)n (–CH2CH(C(O)OC†H3)–)n (–CH2CH(C†(O)OCH3)–)n (–CH2C†(O)–)n (–Si(C†6H5)(CH3)O–)n, C(1)Z† (–Si(C6H5)(C†H3)O–)n (–Si(C6H5)(CH3)O–)n, C(2–6)Z†

291.4 293.2 294.1 295.2 284.7 285.0 285.0 285.8 285.0 285.8 286.8 287.0 289.1 284.8

0.263 0.434 0.645 0.644 K0.252 K0.312 K0.041 K0.001 K0.255 K0.048 K0.133 K0.260 0.356 K0.819

K0.490 K3.774 K7.432 K6.798 1.656 3.788 K2.593 K3.039 3.054 K1.195 0.562 2.866 K7.303 10.788

291.9 297.0 301.5 302.0 283.1 281.2 287.6 288.9 281.9 287.0 286.2 284.2 296.4 274.0

284.8

K0.287

1.253

283.5

286.7

K0.154

1.426

285.3

286.7

K0.134

0.288

286.4

286.7

K0.106

K0.230

286.9

286.7

K0.109

K0.677

287.4

286.7

K0.049

K1.800

288.5

284.8

K0.807

10.860

273.9

284.8

K0.250

2.621

282.2

284.8

K0.270

1.570

283.2

286.6

K0.146

1.368

285.2

286.6

K0.098

K0.447

287.0

288.1

0.353

K7.274

295.4

285.8 284.4 285.6 287.9 286.6 289.0 284.8 286.5 284.8 286.3 286.3 285.0 285.0 285.6 288.3 285.4 286.4 288.8 287.6 283.2 284.4 284.7

K0.246 K0.921 K0.175 0.038 K0.095 0.336 K0.179 0.069 K0.374 K0.103 K0.020 K0.345 K0.096 K0.229 0.405 K0.174 K0.196 0.347 0.285 K0.681 K0.919 K0.212

2.204 13.511 0.835 K1.728 2.621 K6.078 0.288 K3.932 4.407 K0.144 K2.233 3.961 K1.426 1.930 K8.541 1.570 2.290 K7.216 K5.373 8.066 13.755 0.317

283.6 270.9 284.7 289.6 284.0 295.0 284.5 290.5 280.4 286.5 288.5 281.0 286.4 283.7 296.8 283.8 284.1 296.0 293.0 275.2 270.6 284.4

poly(2,6-dimethyl-1,4-phenylene oxide)

poly(2-chloroethyl methacrylate)

poly(3-glycidoxypropyl trimethoxysilane)

poly(3-methacryloxypropyl trimethoxysilane)

poly(acrylamide) poly(dimethyl siloxane) poly(ethyleneimin) poly(ethylene oxide) poly(glycolide) poly(hydroxystyrene) poly(isopropylene glycol)

poly(methacrylamide)

poly(methyl acrylate)

poly(methylene ketone) poly(methyl phenyl siloxane)

(continued on next page)

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Table 1 (continued) Polymer name poly(sarcosine)

poly(styrene) poly(tetrafluoroethylene) poly(tetramethylene glycol) poly(vinyl alcohol) poly(vinyl chloride) poly(vinylidene chloride) poly(vinylidene fluoride) Viton A (poly-hexafluoropropylene-co-vinylidenefluoride)

Group formula

Eb [eV]

qi [e]

Vm(i) [eV]

EbKVm(i) [eV]

(–C(O)–CH2–N(C†H3)–)n (–C(O)–C†H2–N(CH3)–)n (–C†(O)–CH2–N(CH3)–)n (–CH2CH(C†6H5)–)n (–C†H2C†H(C6H5)–)n (–C†F2–)n (–CH2CH2C†H2CH2O–)n (–C†H2C(OH)H–)n (–CH2C†(OH)H–)n (–CHClC†H2–)n (–C†HClCH2–)n (–CCl2C†H2–)n (–C†Cl2CH2–)n (–CF2C†H2–)n (–C†F2CH2–)n (–CF(CF3)CF2–)x(–CF2C†H2–)y (–C†F(CF3)CF2–)x(–CF2CH2–)y (–CF(CF3)CF2–)x(–C†F2CH2–)y (–CF(CF3)C†F2–)x(–CF2CH2–)y (–CF(C†F3)CF2–)x(–CF2CH2–)y

284.8 286.3 287.6 284.6 285.0 292.0 286.4 285.0 286.5 285.9 287.0 286.2 288.6 286.4 290.9 286.8 289.8 291.2 291.8 293.9

K0.313 K0.106 0.380 K0.170 K0.181 0.240 K0.093 K0.299 K0.001 K0.275 K0.135 K0.271 0.007 K0.371 0.315 K0.368 K0.007 0.317 0.285 0.469

3.342 1.123 K7.764 K0.519 0.086 K0.461 K0.461 2.751 K2.607 3.342 0.576 4.191 K1.440 6.842 K4.883 7.533 0.000 3.327 K4.119 K1.455

281.5 285.2 295.4 285.2 284.9 292.5 286.8 282.2 289.1 282.6 286.4 282.0 290.0 279.6 295.8 279.2 286.5 295.3 293.2 297.9

and to guide peak fitting and resolution of a measured spectrum. It has been demonstrated [14] how this procedure can be utilized to analyze the very complicated C1s spectrum of ion-implanted Nafionw which had not been reported before. In this contribution the number of polymers on which the predictive correlation is based has been extended to include a total of 26 polymers with 69 different C1s data points. More types of functional groups have been added, including silicon, nitrogen containing and aromatic moieties. These classes of polymers include many technologically important materials as well as biopolymers. Since the formal charge (of both NDDO and Mulliken type, see below) of carbon atoms bound to silicon is much more negative than of those considered previously, this also expands the range of charges covered by our method. Moreover, we applied charge calculation based on the more sophisticated Mulliken population analysis instead of the simple NDDO approach used earlier.

2. Calculation To calculate the structural data and the partial (Mulliken) charges, the semiempirical quantum chemical formalism AM1 was utilized, as implemented in the MOPAC program (version 6.0) [15,16] and MOPAC93 [17]. After full geometry optimization, normal coordinate analysis were performed and found only positive force constants. The oligomers used in this calculation contained 7–20 repeating units (around 100 atoms). Chain ends were terminated by methyl groups and were excluded from the data evaluated. Partial atomic charges and the coordinates of the molecules at the optimized geometry were taken from the MOPAC

output, and the intramolecular Madelung potential of the ith atom (Vm(i)) was calculated [4,5] according to Eq. (1). X qj VmðiÞ Z (1) r jsi ij where j is the index of atoms other than the one whose binding energy is considered, qj denotes the charge of the other atoms and rij are the respective distances. We wrote a small script (available as supplementary material http:// www.staff.u-szeged.hu/~fekete/mopac6xps/) to process the MOPAC output, extracting the qj and yielding Vm(i). This calculated potential was used to correct the experimental binding energies, which were taken from a standard database [1,2]. PROSTAT (Poly Software International) was used for statistical analysis.

3. Results and discussion Table 1 shows the polymers considered. The statistical analysis supports a linear dependence between the Mulliken charges and the corrected binding energy (EbKVm(i)). We obtained the following equation ðEb K VmðiÞ Þ½eV Z ð288:13G0:11Þ C ð19:26G0:33Þqi ðMullikenÞ n Z 69; R2 Z 0:9818; F Z 3767:26; s Z 0:4 where n is the number of data points we considered in the calculations, R 2 is the adjusted correlation coefficient square, F is the Fisher number, s is the standard error. The goodness of fit is comparable to the uncertainty of experimental XPS peak positions usually

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305

Acknowledgements

300

FEK thanks AFOSR for support. TK thanks OTKA (Grant No. T032190 and T034184) for support. EAH and ZAF are grateful to U. Mass. Amherst for their hospitality, and to U. Szeged for leave of absence while this work was initiated.

(Eb - Vm(i))/eV

295 290 285 280

References

275 270 –1.2

–0.8

–0.4

0.0

0.4

0.8

q(Mulliken) Fig. 1. Correlation between corrected C1s binding energies, EbKVm(i), and calculated Mulliken charges, qi

cited as 0.2–0.4 eV [1,2] for non-monochromatic XPS. A plot of the corrected binding energy vs. the calculated partial charge can be seen in Fig. 1.

4. Conclusion To summarize, the AM1 calculations with the charge potential model correlate well C1s core electron binding energies for a wide variety of polymers. This simple theoretical procedure provides a generally useful methodology for predicting unknown C1s binding energy in polymers over a broad range of partial atomic charges. Interpreting XPS spectra is facilitated by these results. Moreover, deconvolution of known compounds can also be guided by such calculations. This is useful because analyzing complicated spectra without the aid of good starting values may be a difficult task.

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