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Langmuir 2008, 24, 9082-9088
Predictive Modeling of Electrocatalyst Structure Based on Structure-to-Property Correlations of X-ray Photoelectron Spectroscopic and Electrochemical Measurements Kateryna Artyushkova,* Svitlana Pylypenko, Tim S. Olson, Julia E. Fulghum, and Plamen Atanassov UniVersity of New Mexico, Chemical and Nuclear Engineering Department ReceiVed April 7, 2008. ReVised Manuscript ReceiVed May 27, 2008 Chemical structure and catalytic activity of nonplatinum porphyrin-based electrocatalyst for oxygen reduction is characterized by combination of X-ray photoelectron spectroscopy (XPS) and rotating disk electrode. The goal of the study is to show how modifications in the molecular structure affect catalytic characteristics and how to use these structural modifications in a purposeful manner to increase catalytic activity. Initial correlation of structure to electrochemical performance is achieved through the application of principal component analysis (PCA) to curve-fits of high-resolution XPS spectra combined with results of electrochemical measurements. Furthermore, a predictive model that describes this correlation is build using the combination of genetic algorithm (GA) and multiple linear regression (MLR). Based on structure-to-property correlations, two types of active sites responsible for the catalytic activity, i.e., Co associated with pyropolymer and Co particles covered by oxide layer, are determined, and a dual-site for oxygen reduction on cobalt porphyrins is hypothesized, allowing for designing a catalyst structure with optimal performance characteristics.
Introduction Pyrolyzed chelates of transition metals are one of the groups of non-noble inexpensive oxygen reduction reaction catalyst that is considered as an alternative to currently utilized noble materials. It is known that significant increase of ORR activity along with long-term stability can be achieved through heat-treatment of these materials. However, the nature of the active ORR sites, despite several decades of studies by various groups,1–7 is still in debate. Understanding the structure of electrocatalysts, and linking structure to properties is essential for identification of the active catalytic sites and important for optimization of catalyst performance and elucidation of failure mechanisms. It is very important, thus, to develop new strategies and ultimately, universal methodology for structure-to-properties correlations. X-ray photoelectron spectroscopy (XPS) is a surface-sensitive analytical technique providing not only elemental but chemical information. The ability to discriminate between different surface oxidation states and chemical environments is one of the primary advantages of the use of XPS in the characterization of electrocatalyst structures.5,8,9 The distribution of chemical species is usually obtained via deconvolution procedures of high resolution spectra. Dealing with the overlapping peaks for chemical species and ambiguities in the assignment of components * To whom correspondence should be addressed. E-mail: kartyush@ unm.edu. (1) Bron, M.; Radnik, J.; Fieber-Erdmann, M.; Bogdanoff, P.; Fiechter, S. J. Electroanal. Chem. 2002, 535(1-2), 113–119. (2) Gouerec, P.; Savy, M. Electrochim. Acta 1999, 44(15), 2653–2661. (3) He, P.; Lefevre, M.; Faubert, G.; Dodelet, J. P. J. New Mater. Electrochem. Syst. 1999, 2(4), 243–251. (4) Jiang, D. E.; Zhao, B. Y.; Huang, H. Z.; Xie, Y. C.; Pan, G. C.; Ran, G. P.; Min, E. Z. Appl. Catal., A 2000, 192(1), 1–8. (5) Sawai, K.; Suzuki, N. J. Electrochem. Soc. 2004, 151(12), A2132-A2137. (6) Villers, D.; Jacques-Bedard, X.; Dodelet, J. P. J. Electrochem. Soc. 2004, 151(9), A1507-A1515. (7) Wei, G.; Wainright, J. S.; Savinell, R. F. J. New Mater. Electrochem. Syst. 2000, 3(2), 121–129. (8) Artyushkova, K.; Levendosky, S.; Atanassov, P.; Fulghum, J. Top. Catal. 2007, 46(3-4), 263–275. (9) Venezia, A. M. Catal. Today 2003, 77(4), 359–370.
is an important challenge. Multivariate statistical methods of data analysis (MVA) become, thus, of critical importance in developing unambiguous methods of XPS data interpretation.10 Correlation of XPS structural data to any other property and performance characteristics represents a multivariate problem. PCA is a well-known multivariate analysis technique that analyzes multiple variables simultaneously.11–13 It is widely used to decrease dimensionality of large data sets and to assist in the interpretation of spectroscopic data. PCA also facilitates the visualization of the variables responsible for the correlations and anticorrelations among the samples. The application of PCA for structural characterization of porphyrin-based electrocatalysts was recently demonstrated and the advantages of the combination of curve-fitting of XPS high-resolution spectra and PCA over conventional curve-fitting for structural characterization of complex materials were highlighted.8 In order to learn about the relationship between several independent variables and a dependent variable and to determine the magnitude of those relationships Multiple Regression is widely used.14,15 The models then can be used to make predictions of dependent variable. One of the main problems in MLR is that the covariance matrix may be ill-conditioned, which occurs when there is a high correlation between variables. Another problem is that number of variables often exceeds the number of samples. The only way to build a stable MLR models is to apply variable selection techniques. Variables that are completely irrelevant to the objective of model, correlated variables and variable with (10) Artyushkova, K.; Fulghum, J. E. J. Electron Spectrosc. Relat. Phenom. 2001, 121(1-3), 33–55. (11) Adams, M. J. Chemometrics in Analytical Spectroscopy; Royal Society of Chemistry: Cambridge, 1995. (12) Balcerowska, G.; Siuda, R.; Engelhard, H. Surf. Interface Anal. 2000, 29(8), 492–499. (13) Malinowski, E. R. Factor Analysis in Chemistry; Wiley: New York, 1991. (14) Centner, V.; Verdu-Andres, J.; Walczak, B.; Jouan-Rimbaud, D.; Despagne, F.; Pasti, L.; Poppi, R.; Massart, D. L.; de Noord, O. E. Appl. Spectrosc. 2000, 54(4), 608–623. (15) Kapur, G. S.; Ecker, A.; Meusinger, R. Energy Fuels 2001, 15(4), 943– 948.
10.1021/la801089m CCC: $40.75 2008 American Chemical Society Published on Web 07/12/2008
PredictiVe Modeling of Electrocatalyst Structure
low signal-to-noise will be excluded from the model when variable selection is applied. Genetic algorithm, one of the widely utilized variable selection algorithms, utilizes operations fundamental to genetic algorithm that are reproduction, mutation and selection based on fitness until a particular stopping criterion has been reached.16–19 Given an X-block of independent data and a Y-block of values to be predicted, one can choose a random subset of variables from X and, through the use of cross-validation and regression method, determine the root-mean-square error of cross validation (RMSECV) obtained when using only that subset of variables in a regression model. At the end of the training and calibration, the frequency of selection of variables is determined. Variables with higher frequency in the solutions are more likely to contribute to low fitness value of the solutions. Combining Genetic Algorithms and MLR provides a reasonable tool in many areas of multivariate analysis. The application of this hybrid method is effective for wavelength selection in spectroscopy.18 In this paper, we describe correlation of structural composition and electrochemical performance and a predictive model based on these correlations. The importance of various moieties for catalytic activity of the pyrolyzed cobalt tetramethoxyphenyl porphyrin, CoTMPP, is evaluated by means of selective modification of the material. Characterization of the changes in surface chemistry of pyrolyzed porphyrins that occur as a function of catalyst modification is based on XPS analysis. Electrocatalytic activity is evaluated using rotating ring-disk technique. This method provides semiquantitative measurement of oxygen reduction on the catalytic material. Data analysis, data comparison and structure-to-property correlation issues associated with necessity to link large and overlapped XPS spectroscopic data with performance are effectively addressed by implementing a combination of curve-fitting and multivariate analysis. The PCA results are used as an initial step to the construction of a regression model correlating spectral characteristics and electrocatalytic activity. Genetic algorithm is used to select variables, which could describe the correlation between the predictor variables (amounts of species determined from XPS curve fitted spectra) and the response variables (RRDE halfwave potential E). MLR is utilized to build the predictive model. Through this hybrid GA-MLR method, we have determined how modifications in the molecular structure affect catalytic characteristics, with the ultimate goal of controlling these modifications in a purposeful manner to increase activity. This approach can be extended to other porphyrin-based catalysts, as well as to electrocatalysts of other classes. It is also believed to benefit a number of technology fields that require surface characterization of complex materials and/or building a predictive model based on structure-properties correlations derived from a variety of techniques.
Experimental Section Synthesis. The pyrolyzed CoTMPP catalyst was synthesized as follows. First, the cobalt tetramethoxyphenyl porphyrin precursor, CoTMPP (from Aldrich) dissolved in tetrahydrofuran and fumed silica (from Cabot Superior Micropowders) were added in a 1:1 wt ratio. The solvent was allowed to evaporate, inducing precipitation of the precursor on the silica particles. The templated precursor was then pyrolyzed at 700 °C for four hours under nitrogen gas and (16) Broadhurst, D.; Goodacre, R.; Jones, A.; Rowland, J. J.; Kell, D. B. Anal. Chim. Acta 1997, 348(1-3), 71–86. (17) Meusinger, R.; Mores, R. Chemom. Intell. Lab. Syst. 1999, 46(1), 67–78. (18) Smith, M. R.; Jee, R. D.; Moffat, A. C.; Rees, D. R.; Broad, N. W. Analyst 2003, 128(11), 1312–1319. (19) Tolvi, J. Soft Computing 2004, 8(8), 527–533.
Langmuir, Vol. 24, No. 16, 2008 9083 quench cooled in ambient conditions by removing the reactor tube and pyrolyzed material from the tube furnace. The silica was then removed using a 7 M KOH etch. CoTMPP, pyrolyzed at 700 °C, was subjected to various modifications. Three acid treatments were performed by boiling 100 mg of the pyrolyzed CoTMPP catalyst in 100 mL of 1 M acids (HCl, H2SO4, and HNO3) under reflux. Another sample of the pyrolyzed CoTMPP catalyst was left in the 7 M KOH etchant for an extended period of time. Further, “porphyrin rich” catalysts were also synthesized by following the same procedure where the precursor was formed by mixing a 1:1 molar ratio of CoTMPP and metalless porphyrin H2TPP. As-modified samples were used for both electrochemical measurements and XPS surface analysis. XPS. XPS spectra were acquired on a Kratos Axis Ultra X-ray photoelectron spectrometer using a monochromatic Al KR source operating at 300 W, and charge compensation using low energy electrons. The base pressure was about 2 × 10-10 torr, and operating pressure was around 2 × 10-9 torr. Survey and high-resolution spectra were acquired at pass energies of 80 and 20 eV respectively. Acquisition time for survey spectra was 2 min, for C1s and O1s spectra - 5 min, for N1s - 10 min, and for Co2p -30 min. Data analysis and quantification were performed using CasaXPS software. A linear background subtraction was used for quantification of C1s, O1s and N1s spectra, while a Shirley background was applied to Co2p spectra. Sensitivity factors provided by the manufacturer were utilized. All the spectra were charge-referenced to aliphatic carbon at 285 eV. A 70% Gaussian/30% Lorentzian line shape was utilized in the curve-fit. Curve-fitting was carried out using individual peaks of constrained width and shape. The width of peaks in the curve-fit of C 1s, O1s, N1s and Co2p were set to be 1.0, 1.3, 1.0 and 1.4 eV respectively. In our experiments, the higher intensity 2p3/2 component of the Co2p spectrum was curve-fit. The peaks at binding energies higher than 785 eV are X-ray satellites, which are mainly associated with Co2+ environment. The magnitude of satellite peaks is directly proportional to the amount of Co2+ registered from the main peak. Satellite peaks have very insignificant absolute contribution into the assessment of the total amounts of Co which is ∼0.5-1.5%. As we are mainly interested in relative distribution of Co species among the modified samples, we are excluding satellite peaks from the quantitative assessment of Co in various states. RDDE. The RRDE data were obtained with a Pine bipoteniostat model AFCBP1 and Pine analytical rotor under the following conditions. A thin film of nonplatinum catalyst (approximately 68 µg) and 5% Nafion solution is applied to the glassy carbon disk electrode. The potential of the disk electrode is swept from 0.9 V to -0.3V vs Ag/AgCl at 10 mV/s. The ring electrode is held at 1.1 V vs Ag/AgCl. The limiting currents for both the disk and ring are measured at increasing rotation rates (0-3000 RPM). Multivariate Analysis and Modeling. Multivariate analysis and regression modeling was performed in the MATLAB environment using an in-house graphical user interface based on the PLS_Toolbox 4.0. PCA, MLR and GA routines are available in the PLS_Toolbox.20 Autoscaling was utilized as a preprocessing tool for PCA of data sets consisted of curve-fits and values of half-wave potential. For regression modeling, the data for 6 samples were divided in to two sets. For each catalyst type, two samples and two points per sample were analyzed by XPS. One point per sample was chosen (6 observations) for building up the model equations. This set is called the calibration data set. Using this data set, XPS curve fit results have been correlated with the halfway potential for oxygen reduction reaction determined by RDE method. GA was applied to this data set for variable selection purposes. The variables selected were then correlated with RDE potential by MLR analysis. Intercept was chosen at 0. XPS curve fit data for the remaining samples (2-3 per each sample type, called the validation data set) were used for the purpose of validation of the developed model. GA based on MLR model was selected. Both X and Y blocks were autoscaled. Cross-validation by contiguous block, which selects (20) Eigenvector Research, I. PLS_Toolbox 4.0, Wenatchee, WA.
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Table 1. Atomic % from Curve Fits Accounted for Elemental Composition
CoTMPP HCl H2SO4 HNO3 KOH 50/50 CoTMPP HCl H2SO4 HNO3 KOH 50/50 CoTMPP HCl H2SO4 HNO3 KOH 50/50 CoTMPP HCl H2SO4 HNO3 KOH 50/50
Co2O3
CoO, Co3O4
780.0 0.152 0.077 0.051 0.037 0.098 0.104 CoO, C-O-C 530.9 0.800 0.179 0.361 0.685 0.699 0.752 N pyrid 398.8 0.770 0.591 0.782 0.568 0.789 0.688 CdC 284.4 24.9 27.2 26.9 18.9 18.9 19.6
780.9 0.477 0.179 0.171 0.129 0.319 0.334 Co2O3, OdC-N 532.0 1.780 1.911 3.793 4.993 1.734 2.511 N-Co 399.2 1.285 0.983 1.234 0.895 1.323 1.028 C-C 285.0 28.0 29.3 27.1 27.5 29.9 25.4
pCo-N4
Co(CO)x
782.0 783.0 0.321 0.179 0.148 0.081 0.170 0.091 0.090 0.055 0.313 0.188 0.222 0.128 O*-CdO O-CH3 533.0 533.8 1.328 3.587 2.005 2.851 3.293 3.273 5.546 4.876 2.333 4.726 1.957 6.003 Npyrol, N-CdO 399.8 400.5 1.236 0.850 1.282 1.050 1.117 0.713 1.350 1.368 1.235 0.981 1.236 0.930 C*-C-O CH3-O 285.8 286.7 11.7 8.9 12.6 7.3 11.3 6.8 11.4 6.5 12.8 9.5 12.0 11.2
the position of first block randomly to reduce the likelihood of overfitting, was applied. Three replicate runs were chosen (the results from each run are concatenated) to optimize the selection. Other important parameters that were used are the following: population size-256 (largest to provide a better representation of different variable combinations), % of initial terms - 30-50% (limit the number of variables included into the model at each time), mutation rate - 0.01 (largest to including underrepresented variables or excluding overrepresented variables), number of generations -100. GA model was first applied to all 24 variables. Then the variables above threshold of frequency of inclusion into the model of 0.1 were chosen for a second step GA with the same settings.
Results and Discussion Structure-to-Property Correlations. The structure of pyrolyzed CoTMPP was previously thoroughly investigated using XPS.8 The evolution of the species that are being formed and/or removed in the catalyst as a function of pyrolysis temperature was followed by combination of PCA and high resolution XPS. Here we present summary of these XPS results along with structural assignment.8 High-resolution Co2p, O1s, N1s and C1s spectra of CoTMPP pyrolyzed at 700 °C are shown in 8 peaks are required to fit the Co2p spectrum, however, only 5 peaks located at the lower BE side were included in quantification. About 38% of detected cobalt is at a BE of 780.9 eV. Most probably a mixture of cobalt oxides of different valency (Co2O3, CoO and their mixture Co3O4), with the highest contribution from CoO, contributes to the XPS signal at 780.9 eV. The other peak due to Co2O3 and some of the mixed oxide Co3O4 is at 780.0 eV (14%). The second largest contribution to the Co2p peak is from Co-N4 at 782 eV. The peak at 782 eV due to Co-N centers present in the precursor after pyrolysis comprises ∼25%. The possible assignment of peaks at 783 and 785 eV (17 and 9%) is cobalt bound to oxygen in Co(CO)x.
785.0 0.111 0.035 0.056 0.034 0.118 0.062 -(CO)x, O2, H2O 534.5 2.017 0.583 0.738 1.187 1.776 2.909 Ngraph 401.3 0.599 0.786 1.460 0.766 0.637 0.838 CdO 287.6 4.8 4.2 3.9 4.3 5.1 5.6
Co total 1.24 0.52 0.54 0.35 1.04 0.85 H2 O 535.0 1.173 1.323 1.287 1.476 0.761 0.975 N(CH3)3+ 402.2 0.297 0.373 0.260 0.263 0.576 0.492 N-CdO 288.5 3.2 3.3 3.4 4.6 3.3 3.7
O total 10.7 8.9 12.7 18.8 12.0 15.1 N total 5.0 5.1 5.6 5.2 5.5 5.2 O-C(dO)O 289.4 1.5 1.7 1.7 2.5 2.0 1.4
C total 83.0 85.6 81.2 75.7 81.4 78.8
The main peak in O 1s spectrum at 533.8 eV (32%) is due to combination of CH3-O species, side chain of the CoTMPP, and methoxy benzene. Peaks at 532 eV (17%) and 530.9 eV (8%) can be assigned to Co2O3 and CoO, mostly. Both of these peaks contain contributions from O)C-N type of carbon. Oxygen species detected at high BE of 533, 534.5 and 535 might have several sources of origin, including molecular oxygen, water and multiple types of species containing oxygen, nitrogen and carbon. Two main components in N1s spectrum at 399.2 eV (25%) and 399.8 eV (27%) are due to Co-N centers present in the precursor prior to the pyrolysis and pyrrolic nitrogen, respectively. Pyridinic nitrogen is identified at 398.8 eV in the amount of 14%. Nitrogen peak located at 400.5 eV can be assigned to combination of N-CdO and pyrrolic nitrogen. Peak at 401.3 eV is assigned to graphitic nitrogen.21 And peak at 402.2 eV is due to quaternary amine (N(CH3)3)+. About 60% of the carbon is almost evenly divided between aromatic carbon, the main component of unpyrolized CoTMPP, and unsaturated carbon with graphite-like structure. 14% of carbon is bound to nitrogen in C-N centers and includes C-N centers present in the unpyrolyzed CoTMPP, pyrrolic and pyridinic types of nitrogens. Their presence is confirmed in the N1s curve-fit. The peak at 286.7 eV (11%) is carbon bound to oxygen in O-CH3 side chain. Species that contribute to spectra at 287.6, 288.5 and 289.4 eV are oxygen and oxygen-nitrogen containing species such as CdO, N-CdO, which are also detected in the N1s and O1s curve-fits. Thus, pyrolyzed CoTMPP represents a carbon-nitrogen polymer-like network. Part of the cobalt is present in Co-Nx centers associated with pyropolymer, while another part of the cobalt decorates the pyropolymer network in the form of metallic (21) Matter, P.; Zhang, L.; Ozkan, U. J. Catal. 2006, 239(1), 83–96.
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Langmuir, Vol. 24, No. 16, 2008 9085
Figure 2. Half-way potential determination from RDE data shown for CoTMPP. Figure 1. Co 2p, O 1s, N1s and C 1s photoelectron spectra for CoTMPP pyrolyzed at 700 °C. For curve fit details please refer to text.
cobalt oxides of mixed valency covering metal nanoparticles, as confirmed by SEM and XRD. Nitrogen is found in several forms, including nitrogen bonded to cobalt, and pyridinic and pyrollic forms that may or may not be associated with cobalt. CoTMPP Modifications. The catalytic activity can be affected by selective depletion or selective enrichment of pyrolyzed material in various species. In this study several treatments were utilized to modify the CoTMPP composition. Creating a set of samples with range of electrochemical activities due to enrichment/depletion of various types of species, allows for building structure-to-property relationship and, therefore, suggesting the active sites and mechanism of oxygen reduction. CoTMPP modifications include CoTMPP treated in strong acids, HCl, H2SO4 and HNO3, and base, KOH. CoTMPP was also modified by pyrolyzing a mixture of CoTMPP and H2TPP to create a “porphyrin rich” catalyst. Curve-fitting procedure was applied to Co2p, O1s, N1s and C1s spectra in order to identify the types of chemical species that are altered by modifications. Atomic quantification results obtained from curve-fits of highresolution XPS spectra of each element are shown in Table 1. Structural reorganization of types of species present as well as leaching of Co is observed as result of various treatments. Such reorganization and changes in total metal present is reflected in electrochemical activity allowing for structure-to-property correlation. The half-wave potential for a catalyst can be used as a quantitative measure of its activity. It is defined here as the potential at which the oxygen reduction reaction current is onehalf of the diffusion limiting current. The half-wave potential for the non-Pt catalysts was determined graphically as shown in Figure 2. Values of half-wave potential are shown in Table 2. The results of PCA applied to data matrix combining Table 1 (24 variables) and Table 2 (1 variable) show the contribution of scores (samples) and loadings (concentrations of chemical components and values of half-wave potentials) to first two principal components (PC’s) (Figure 3). This figure visualizes the effect that modifications have on both the structural composition and oxygen reduction activity. On this plot, the starting material, CoTMPP is found in close proximity to halfwave potential (E1/2) and cobalt species, including cobalt associated with pyropolymer (at 782 eV) and cobalt oxides that cover cobalt nanoparticles (at 780.9 eV). Acid treated samples are separated from CoTMPP. Among the acid treated samples,
Table 2. Half-Wave Potentials Measured with RRD vs Ag/AgCl sample
E1/2, V
CoTMMP Treat. HCl Treat. H2SO4 Treat. HNO3 Treat. KOH CoTMMP/H2TPP
0.415 0.325 0.395 0.286 0.390 0.410
HCl and H2SO4 treated samples are closest to each other. They correlate with unsaturated carbon (at 284.4 eV), graphitic nitrogen (at 401.3 eV) and anticorrelate with cobalt species (at 782 and 780.9 eV), suggesting that their proximity can be explained by the degrees of cobalt removal and graphiticity of the material. HNO3 treated sample, correlates with various carbon-oxygennitrogen bonds (at 288.5, 289.4, and 533 eV), signatures of highly oxidized material, and highly anticorrelates with cobalt species. The separation of acid treated materials from E1/2, thus, can be associated with the amount of the cobalt in the form of Co-Nx centers and cobalt oxides covering metallic cobalt that is removed by the treatment. The largest concentration of cobalt in these two
Figure 3. PCA applied to curve-fits of cobalt, oxygen, nitrogen and carbon of CoTMPP-H2TPP, CoTMPP and acid and base treated CoTMPP. Data set consist of chemical species and half-wave potential E1/2 determined with RRDE. Biplot: PC1xPC2. Scores are samples, marked in black, loadings are chemical species and half-wave potential, marked in red.
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forms is in the CoTMPP that has highest oxygen reduction activity. HNO3 treatment is the most effective in removing cobalt, and results in the material with the lowest catalytic activity toward oxygen reduction. Another group of samples is formed by CoTMPP-H2TPP and CoTMPP treated with KOH. These materials also show some loss of the activity comparing to untreated CoTMPP. These samples are grouped with CoTMPP based on the relatively preserved structure compared to acid-treated samples, but separated from CoTMPP based on some changes in the structure and some loss of activity compared to CoTMPP. Though the total amount of Co-Nx centers and cobalt oxides is lower in CoTMPP-H2TPP than in CoTMPP, their relative amounts are the same in both materials. KOH treatment, on the other hand, does change the distribution of Co species, but in comparison with acid treatment, it causes much lesser effect and, more importantly, it predominantly removes cobalt oxides. Correlations of cobalt in Co-Nx centers and CoxOy covering metallic cobalt and half-wave potential suggest the importance of both types of cobalt species for oxygen reduction activity. Removal of one and/or both of types of cobalt lowers oxygen reduction activity. The predominant removal of CoxOy has a higher effect on the catalytic activity compared with the removal of equivalent amount of Co in both forms, suggesting that Co-N centers present in the material in abundance, while the CoxOy is the limiting moiety. Based on conclusion about the need for two types of cobalt moieties for oxygen reduction activity, we can hypothesize a dual site mechanism, where oxygen reduction follows 2 × 2 electron pathway. Cobalt associated with pyropolymer serves as the first site, where oxygen is reduced to peroxide. Cobalt particles covered by cobalt oxides are the second site, where peroxide molecules can be reduced further to water. The second site can limit the reduction of peroxide and the condition for reduction of peroxide molecules on cobalt particles is the close proximity of these particles to the cobalt associated with pyropolymer. We will attempt to confirm this hypothesis by building direct quantitative relationship between electrochemical activity and structure of catalysts in next section. Predictive Modeling. Figure 4a shows an output from GA applied to a whole set of 24 variables shown in the table 1 against a vector of half-wave potentials in Table 2. The height of the bar corresponds to the normalized frequency the particular variable was included in the model. Based on the threshold of 0.2, the variables with highest inclusion (no. 10, 11, 15 and 19) can be used for building a regression model. Alternatively, the smaller threshold can be selected, i.e. 0.1, and variables 2, 4, 9, 10, 11, 15, 16, 19, 20 can be subjected to a second step of GA. This ensures that variables whose frequency is at the level of threshold (i.e., 9 and 20) are not overlooked. Figure 4b) shows results from the second step applied to 9 variables. Variables 1, 4, 6, 7, 8 and 9 (set#1 Co780.9, O534.5, N400.5, N401.3, C285, C285.8) were selected from GA to be used for building a MLR model. Figure 5 shows MLR results applied to variable set 1. RMSE for calibration is 1.80409 × 10-14 and for cross-validation (CV) is 0.811819. The equation describing the potential obtianed from the model 1 is
E ) 0.1817Co780.9 + 0.037O534.5 - 0.091N400.5 + 0.0876N401.3 + 0.0093C285 + 0.0023C285.8 (1) The cross-validation results show that model 1 does not describe the data outside the model, therefore it cannot be used reliably for predictive purposes. The percent contribution of each type of variables into the response variable was estimated and C285.8, which has a smallest percent of 8%, was excluded and MLR
ArtyushkoVa et al.
Figure 4. GA results. (a) Step 1 from all 24 variables, (b) second step to 9 variables selected from step 1.
model was rebuilt with 5 left variables (set 2). Figure 6 shows the output for cross-validation for this set of variables (model 2). RMSE of Calibration for this model is 0.00051, while RMSECV has improved to 0.00543. Much better %R2 for this model 2 indicates much better reliability for predictive purposes. The next smallest contribution into the models 1 and 2 is that from O534.5 types of species. Exclusion of this variable from models 1 and 2 causes an increase in RMSECV to 0.4996 and 0.056 and decrease in %R2 to 96.6724 and 94.9468%, respectively. This indicates that O534.5 is significant for predicting the response variable and it should be left in the model. Model 2 was, thus, accepted as the final for describing relationship between XPS structural data and electrochemical performance of the catalyst:
E1⁄2 ) 0.169Co780.9 + 0.0358O534.5 - 0.095N400.5 + 0.086N401.3 + 0.0104C285 (2) The coefficient in models 1 and 2 (1 and 2) are very close to each other, indicating that even though contribution of C285.8 into the model 1 was not very significant, it was considerable enough to break the model’s predictability. Table 3 shows the predicted versus measured values of E together with %R2 for calibration set. An average error of the model is less than 0.2%.
PredictiVe Modeling of Electrocatalyst Structure
Langmuir, Vol. 24, No. 16, 2008 9087 Table 4. Predicted vs Measured E for Validation Set CoTMPP s.1. p.2 CoTMPPs.2 p.1 CoTMPP s.2 p.2. HCl s.2 p.1 HCl s.2 p.2 H2SO4,s.2 p.1 H2SO4,s.2 p.2 HNO3s.1.p.2 HNO3s.2 p.1 HNO3s.2 p.2 CoTMPP/H2TPP s.1 CoTMPP/H2TPP s.2
Figure 5. MLR model 1 from variable set 1. (a) Actual vs predicted for calibration, (b) actual vs predicted from CV.
Figure 6. MLR model 2 from variable set 2. Actual vs predicted from CV.
E1/2, measured, V
E1/2, predicted, V
%R2
0.415 0.415 0.415 0.325 0.325 0.395 0.395 0.286 0.286 0.286 0.410 0.410
0.406 0.404 0.400 0.321 0.326 0.400 0.388 0.289 0.301 0.278 0.396 0.440
2.25 2.60 3.57 1.18 0.24 1.39 1.66 1.27 5.59 2.78 3.52 7.37
graphitic environment and O, which is most probably, associated with oxygen in water adsorbed on Co oxide. As discussed in PCA results above, the oxidation reaction is a two-step reaction, where oxygen is first reduced to hydrogen peroxide with participation of Co-Nx species and, during second step hydrogen peroxide is being further reduced to water at Co particles, covered with oxides. Those two types of moieties are located within a pyropolymer consisting of aromatic carbon and different types of nitrogen. What GA/MLR model suggests is that the electrochemical activity is directly related to amount of cobalt oxide particles and pyropolymer, specifically, aromatic carbon and graphitic nitrogen. And it is inversely related to amount of pyrrolic N/N-CdO. It is interesting to note, that amount of Co-Nx centers which are responsible for the first step of the reaction are not important for predicting the RDE potential, which might be due to excess of this types of species for the oxygen reduction reaction to start. The limiting factors, thus, are amounts of cobalt oxide particles in direct proximity with Co-Nx centers and structure of the pyropolymer, those particles are embedded in. This is also confirmed by lower activity of KOH treated sample, which, in spite of high total amount of Co, has lower relative amounts of Co oxides (less than 40% of total metal amount), comparing with other samples (∼50%). Figure 7 shows PCA results applied to amount of these species used to build a predictive model and RRDE half-wave potential combined. PCA shows the splitting of the samples into category of high, medium and low activity along y-axis (PC1). Variables selected by GA cover the range of all samples, therefore explaining the source of variability between them. High activity is associated with cobalt oxide and water, medium, with aromatic C and graphitic N, and low, with pyrrolic N/N-CdO. The predictive
Table 3. Predicted vs Measured E1/2 for Calibration Set CoTMPP HCl H2SO4 HNO3 KOH CoTMPP/H2TPP
E1/2, measured, V
E1/2, predicted, V
%R2
0.415 0.325 0.395 0.286 0.410 0.410
0.414 0.323 0.394 0.286 0.390 0.409
0.17 0.47 0.13 0.08 0.01 0.08
The model was then applied to validation set consisting of 2-3 samples/positions on the sample, as described in Experimental section. Table 4 shows the results together with %R2. The %R2 ranges from 0.2% to 7.5% indicating a very powerful and accurate predictive model for structure-to-property relationships. The spread in %R2 is related to error in XPS (∼2%) and RDDE (∼5%) measurements themselves, as well as to heterogeneity of samples which is introduced by chemical modifications. The following species are included in the predictive modelsCo oxide, aromatic C, pyrrolic N and /or N-CdO, N embedded in
Figure 7. PCA applied to variable used for building predictive model and RDDE potential.
9088 Langmuir, Vol. 24, No. 16, 2008
equation obtained through MLR modeling is overimposed on PCA biplot. All species, except for N400.5, contribute positively into E potential suggesting a threshold between active/non active catalyst depending on amount of pyrrolic N and/or N-CdO present. We will further investigate the reason for importance of this type of N on electrochemical activity.
Summary Correlation of structure and catalytic activity of nonplatinum porphyrin-based electrocatalyst for oxygen reduction is achieved through the application of principal component analysis (PCA) to curve-fits of high-resolution XPS spectra combined with results of electrochemical measurements on set of samples with range of electrochemical activities created by selective removal of active species. A predictive model that describes this correlation is successfully built using the combination of genetic algorithm (GA) and multiple linear regression (MLR).To our knowledge this is a first successful attempt to directly relate structural and performance characteristics.
ArtyushkoVa et al.
Two types of active sites responsible for the catalytic activity, i.e. Co associated with pyropolymer and Co particles covered by oxide layer, are determined, and a dual-site for oxygen reduction on cobalt porphyrins is hypothesized. Predictive model suggests that the electrochemical activity is directly related to amount of cobalt oxide particles and pyropolymer, specifically, aromatic carbon and graphitic nitrogen. High activity is associated with cobalt oxide particles. Lower amounts of Co oxides and enrichment with aromatic carbon and graphitic nitrogen result in medium activity. The lowest activity is associated with the smallest amounts of Co oxides and high amounts of pyrrolic N. This information allows designing a catalyst structure with optimal performance characteristics. This approach can be extended to electrocatalysts of different classes. It can also benefit in material design by building predictive models based on structure-properties correlations derived from a variety of analytical techniques. LA801089M
