research papers
Comparative analysis of XANES and
for local structural characterization of disordered metal oxidesaMaterials Science and Chemical Engineering, Stony Brook University, 100 Nicolls Road, Stony Brook, NY 11794, USA, and bDepartment of Materials and Interfaces, Weizmann Institute of Science, Rehovot 7610001, Israel
*Correspondence e-mail: anatoly.frenkel@stonybrook.edu
In functional materials, the local environment around active species that may contain just a few nearest-neighboring atomic shells often changes in response to external conditions. Strong disorder in the local environment poses a challenge to commonly used extended X-ray absorption fine structure (EXAFS) analysis. Furthermore, the dilute concentrations of absorbing atoms, small sample size and the constraints of the experimental setup often limit the utility of
for structural analysis. X-ray absorption near-edge structure (XANES) has been established as a good alternative method to provide local electronic and geometric information of materials. The pre-edge region in the XANES spectra of metal compounds is a useful but relatively under-utilized resource of information of the chemical composition and structural disorder in nano-materials. This study explores two examples of materials in which the transition metal environment is either relatively symmetric or strongly asymmetric. In the former case, results agree with those obtained from the pre-edge XANES analysis, whereas in the latter case they are in a seeming contradiction. The two observations are reconciled by revisiting the limitations of in the case of a strong, asymmetric bond length disorder, expected for mixed-valence oxides, and emphasize the utility of the pre-edge XANES analysis for detecting local heterogeneities in structural and compositional motifs.Keywords: functional materials; XAFS; pre-edge analysis; local symmetry.
1. Introduction
In functional materials, the local environment around various atomic species may change dynamically under different operando conditions, such as elevated temperature, pressure and application of external electric fields (Vaccari et al., 2009; Dalba et al., 2004). The local atomic displacements introduce distortions in the bond lengths, which affect specific functional properties such as ferroelectricity and (Abrahams, 1978). A variety of experimental methods such as atomic scale microscopy, X-ray diffraction (XRD) and (XPS) have been developed and utilized to study these changes (Ueda, 2013; Feldmann, 2003; Wang et al., 2013). Nevertheless, the choice of tools for detecting and interpreting small structural changes in functional materials is limited. is a premier technique to probe the structure at the atomic scale (Rehr & Albers, 2000; Sayers et al., 1971; Bingham et al., 2014; Wende, 2004; Penner-Hahn, 1999; Grunes, 1983; Farges et al., 2004; Sayers et al., 1969; Ankudinov et al., 2002; Frenkel et al., 2001). In particular, extended X-ray absorption fine structure (EXAFS) provides essential information on the distribution of neighboring atoms around the absorbing atom, which has excellent sensitivity to local atomic displacement and is element-specific. analysis commonly assumes Gaussian distribution of bond lengths, which is usually sufficient to understand the of well defined materials, such as homogeneous, single-phase bulk solids (Timoshenko et al., 2019). However, interatomic distance distribution can be more complicated in many materials, such as metallic nanoparticles, mesoporous materials and metals in molten salts (Boubnov et al., 2020; Prasai et al., 2015; Billinge & Levin, 2007; McGreevy & Pusztai, 1990; Dias et al., 2021), where the local atomic environment is characterized by strong asymmetry in the bond length distribution. Such an asymmetry has been shown to be a source of artifacts in conventional structural analysis methods by (Yevick & Frenkel, 2010). In these cases, the non-Gaussian distribution of bond length needs to be accounted for in order to understand the local environment, which introduces the inaccuracy in quantitative analysis of experimental spectra. The alternative approaches used to circumvent the limitations of conventional analysis generate the theoretical spectra based on atomic level simulations, e.g. (MD) or reverse Monte Carlo simulations (RMC) methods (Chill et al., 2015; Yancey et al., 2013; Gurman & McGreevy, 1990). The quality of the agreement between theoretical and experimental spectra can be used to determine the atomic structures. In the MD simulation method, the interatomic forces are derived from ab initio calculation or empirical force fields (Chill et al., 2015; Cicco et al., 2002 & Roscioni et al., 2011). Although this approach can be employed to study the local structure by modeling thermal disorder and anharmonic effects, the MD simulation method is limited to simulations of the smaller clusters (1–2 nm in size) (Mazzone et al., 2008). In RMC, the configuration-averaged is calculated after the positions of the atoms in the assumed 3D structure model are randomly modified at each interaction, and then, the optimized structure is generated after minimizing the difference with the experimental and calculated (McGreevy & Pusztai, 1988; Timoshenko & Frenkel, 2017).
X-ray absorption near edge structure (XANES) can be employed as a complementary method to et al., 1992; Ankudinov et al., 2002). Furthermore, the pre-edge features in the XANES spectra also contain information about the electronic and structural properties of materials (Srivastava & Nigam, 1973). The pre-edge features and the main-edge features of the experimental XANES spectra of some transition metals are well studied through understanding the features in theoretical XANES spectra, which are typically obtained by the so-called forward modeling approach (Rehr & Albers, 2000; Joly, 2001; Vinson et al., 2011). For example, Farges et al. (1997) successfully modeled the Ti K-edge XANES spectra for selected Ti compounds using the ab initio real space multi-scattering approach (FEFF) to interpret the features of the experimental XANES spectra, including the pre-edge features and main-edge features, providing a quantitative description of the Ti K-edge XANES spectra. Farges et al. show that the shapes of the XANES spectra are affected by the size and the types of clusters around the Ti atoms. In addition, the characteristic pre-edge features associated with the (CN) of a central atom have been utilized as a powerful tool to help understand the (Farges et al., 1997). As explained by Farges et al., for the Ti4+ species, the pre-edge energy position and pre-edge intensity are well separated for the different CNs. Other works discussed the pre-edge features according to group theory calculations and indicated that, although the oxidation states can be the same, the different types of symmetries, e.g. tetrahedral (Td) and octahedral (Oh), affect the intensities of the pre-edge features differently (Yamamoto, 2008). More recent applications of the sensitivity of the pre-edge features to the local environment of metal oxides were used as a basis of the machine learning based approaches to predict the XANES spectra of metal oxides with quantitative accuracy (Liu et al., 2019; Carbone et al., 2020; Torrisi et al., 2020).
to obtain local electronic and geometric information because it is sensitive to the electronic transitions and the multiple-scattering contributions from neighboring atoms (RehrIn general, the results obtained by XANES such as charge states and local geometry are used to support the information extracted by e.g. reaction cell walls) pose obstacles toward collecting the experimental spectrum with sufficient quality for adequate structural analysis. In these cases when analysis is unavailable or unreliable, the pre-edge analysis stands out as the sole source of information on the from X-ray absorption measurement.
analysis, such as CNs. However, as for the materials with strong asymmetry in the bond length distribution, the results obtained by XANES and could be inconsistent. In addition, for functional materials, dilute concentrations of absorbing atoms, low dimensionality of sample size and the experimental setup (In this study, we illustrate this problem – the inconsistency in the determination of the local environment by XANES and i.e. relatively symmetric, or strongly asymmetric. In the former case, results agree with those obtained from the pre-edge analysis. In the latter case, they are inconsistent because conventional is affected by the strong and asymmetric disorder. We focus on the Ti-doped Ce0.8Gd0.2O1.9 (TiGDC20) and V-doped Ce0.8Gd0.2O1.9 (VGDC20) nanocomposites, which are shown to have different local symmetries for transitional metal's environment (vide infra). In the case of the Ti environment, although there is a high disorder, the environment is a distorted octahedral. We demonstrate that, in this case, the results and pre-edge analysis results are consistent. TiGDC20 composites were recently shown to have exceptionally high oxygen diffusion coefficients at room temperature (≃10−14 cm2 s−1) and reversible oxidation–reduction cycling. This unique property led to the demonstration of an electro-chemo-mechanical (ECM) actuator working at room temperature (Makagon et al., 2020). Thus, understanding the local structure of such composites may have direct and immediate implications for advancement of ECM materials and applications. For the V environment, conventional analysis shows an underestimation of CNs. The possible explanations for the apparent inconsistency are that they have a heterogeneous environment with a multimodal distribution of bonds or a unimodal but strongly asymmetric environment. To investigate these effects, we performed a more in-depth pre-edge analysis to study the of the above materials, as discussed in the rest of the article.
when strongly asymmetric disorder in the bonding distribution is present – using an example of two different metal oxide nanocomposite materials, in which the local environment of the transition metal is either distorted octahedral,2. Experimental
Nanocomposite thin films were fabricated according to the previously published method (Makagon et al., 2020). Namely, TiGDC20 (GDC20: 20% Gd-doped ceria) composites were deposited by a magnetron co-sputtering method on SiO2 substrates with a 100 nm Al adhesion layer. Sample 1 was deposited from a Ti 2" metallic target and a GDC20 3" stoichiometric oxide target (Ar flow: 30 sccm, deposition pressure: 20 mTorr, DC power on Ti: 150 W, RF power on GDC20: 100 W, deposition time: 4 h, estimated thickness:1 µm). In Sample 2, the oxide target was replaced with a GDC20 alloy (Ar flow: 35 sccm, O2 flow: 3 sccm, deposition pressure: 15 mTorr, DC power on Ti: 150 W, RF power on GDC20: 100 W, deposition time: 3 h, estimated thickness: 0.5 µm). VGDC20 samples were deposited under similar conditions to Sample 1 (DC power on V 2" metallic target: 100 W, RF power on GDC20: 100 W, deposition time: 4 h, estimated thickness: 200–500 nm). All samples were annealed under vacuum at 430°C for 4 h. Ti oxidation states and the overall grain morphology are expected to vary significantly between the samples. Ti (4966 eV) and V (5465 eV) K-edge X-ray absorption spectra of the TiGDC20 and VGDC20 thin films were measured at the QAS (7-BM) Beamline, National Synchrotron Light Source II (NSLS-II) at Brookhaven National Laboratory. X-ray absorption spectra were collected in fluorescence mode at grazing incidence. The experimental setup is shown in Fig. S1 of the supporting information. The raw data were analyzed utilizing the Athena and Artemis interfaces of the Demeter software package (Ravel & Newville, 2005). The spectra were energy-aligned, merged and normalized.
3. Results
3.1. The local structure of Ti in nanocomposites
For the Ti K-edge XANES spectra of Sample 1 and Sample 2, as shown in Fig. 1, the rising-edge positions resemble that of amorphous BaTiO3 (Frenkel et al., 2005), suggesting that the of Ti in Sample 1 and Sample 2 is close to Ti4+. As shown in Fig. S2, the of Ti in Sample 1 is slightly lower than that in Sample 2. The inset in Fig. 1 shows the pre-edge feature A corresponding to the 1s to 3d transition in Ti. As demonstrated earlier (Farges et al., 1997; Frenkel et al., 2005), the intensities and energy positions of pre-edge peaks for Ti4+ species are well separated for different types of Ti local environment (Fig. 2). The energy position of Sample 1 and Sample 2 are aligned to compare with the components in the literature (Farges et al., 1997; Frenkel et al., 2005), which allows us to conclude that local environments of Ti in Sample 1 and Sample 2 are octahedral.
. Hence, the local environment of Ti estimated by analysis and taking into account the error bars is consistent with that of TiO6 as also inferred from the XANES pre-edge features. The experimental data and theoretical fits of Sample 1 and Sample 2 are shown in Fig. 3 in r-space. analysis also indicates that that Sample 1 and Sample 2 differ with respect to the next-nearest neighbor (Ti–Ti) distribution. As shown in Fig. 3, data for Sample 1 has a prominent second peak that corresponds to the Ti–Ti photoelectron path, which is not observed in Sample 2.
analysis provides quantitative local structural information around Ti atoms. The CNs for the Ti—O bonds obtained by fitting of Sample 1 and Sample 2 are 5.5 ± 1.0 and 6.1 ± 1.0, respectively, as shown in Table 1
|
In addition to the CN and bond length, 6 octahedron. Related information which can be obtained by a more in-depth pre-edge analysis is on the off-center displacement di of the Ti atom from the center of the TiO6 octahedron. We now discuss the relationship between the off-center displacement of the Ti atom to the disorder (σ2) obtained by the analysis in Sample 1 and Sample 2. The pre-edge area provides quantitative information on the off-center displacement. The area Ai of this pre-edge feature is proportional to the square of the off-center displacement (Ravel et al., 1998; Ravel, 1995), as shown in equation (1),
analysis also provides information on the disorder in the TiOAt 300 K, the experimentally measured constants γBaTiO3 and γEuTiO3 are 11.2 eV Å−2 and 13.6 eV Å−2, respectively. We assume the value of γTiOx to be the average of γBaTiO3 and γEuTiO3: 12.4 eV Å−2. EuTiO3 is a centrosymmetric perovskite. The off-center displacement of EuTiO3 is 0.103 Å at 300 K (Ravel et al., 1998; Ravel, 1995), which refers to vibrational disorder within the TiO6 octahedra. When calculating the off-center displacement of our samples, the of EuTiO3 is subtracted to correct for the thermal motion, as shown in equation (2),
The off-center displacements of Ti obtained for Sample 1 and Sample 2 are 0.455 Å and 0.433 Å, respectively, as shown in Table 2. According to the XANES and analyses, Ti has a six-coordinated octahedral structure. Hence, the displacement change reflects that the Ti position changes from the octahedral center without any change in the overall Furthermore, the magnitude and direction of the off-center displacements of Ti atoms are the sources of the changes of individual Ti—O bond lengths (r), and must be related to the mean square relative displacement (σ2), as shown in equation (3)
We note however that a direct comparison of the displacements estimated by and pre-edge analysis [equation (1)] is very complicated. In order to compare the two values, it is essential to take into account the correlation between the displacement of Ti and O atomic positions. Such analysis can be done, e.g. by simulations or multi-technique Reverse Monte Carlo refinements (Noordhoek et al., 2013).
from equation (3)
|
In the above analysis, the Ti samples have relatively ordered environment around the Ti atoms due to the overall octahedral symmetry. In these cases, the results obtained by et al., 2020).
and pre-edge XANES analyses are consistent with each other. The above analysis also suggests that Ti with somewhat different shares similar local coordination, which may be a hint to the ability of these composites to undergo rapid oxidation and reduction in the ECM effect (Makagon3.2. The local structure of V in nanocomposites
We also employed V K-edge XANES and data collected on the VGDC20 sample to obtain the valence states and CNs. As shown in Fig. 4, the rising edge position is between V2O4 and V2O5, indicating the average of V in the sample is between V4+ and V5+. The typical symmetry of tetravalent V can be square pyramidal (Py) or octahedral (Oh). Pentavalent V may be present in several symmetries, such as Td, Oh and Py. Therefore, for the VGDC20 sample, the average CN of V is expected to be higher than 4 (the exact number should depend on the mixing fraction of multiple states with different symmetries). However, the CN obtained by analysis is much smaller: 2.0 ± 0.7 (Table 3). The experimental spectra and theoretical fits in r-space are shown in Fig. 5.
|
To resolve the apparent discrepancy between et al. followed the procedure described by Farges et al. (1997), but found that intensity and energy position of pre-edge peaks for tetravalent and pentavalent V could not be well separated because each of them contains various symmetries (Giuli et al., 2004; Levina et al., 2014). Therefore, an interpretation of the pre-edge peak in terms of the distortion of V atoms from the inversion symmetry center should be made with extreme caution when more than one state of V can be present in the sample. If ignored, such heterogeneity will result in an erroneous assumption that there is an `effective' V atom environment that such a method would characterize. Thus, the prevailing strategy is to use the pre-edge peak position and area for the task of speciation of structures and oxidation states of V in the sample. Only when a pure state is established, its analysis, based on the pre-edge peak intensity [equation (1)], can be undertaken, similar to that done above with Ti compounds.
and XANES results for V compounds, we compare the two possible explanations: (1) the V environment is heterogeneous, containing a mixture of local structures and oxidation states, and hence, resulting in a multimodal distribution of V—O bond distances; or (2) V has the unimodal distribution of bonds but a strongly asymmetric environment. To discriminate between the two possibilities, we rely on the pre-edge analysis. GiuliTo evaluate the oxidation states and the symmetry of V oxides, we followed the procedure described by Chaurand et al. (2007). The evaluations of the oxidation states and the of V are performed by employing the normalized pre-edge and the pre-edge peak centroid energy, as shown in Fig. 6. Centroid energy is defined as the area-weighted average of the position in energy (E − E0) of each component contributing to the pre-edge peak. E0 is the maximum value of the first peak of the vanadium metal derivative spectrum, which marks the threshold or onset of photo-excitation of the 1s electron in vanadium metal (Bearden & Burr, 1967). The total pre-edge area was derived by calculating the sum of the integrated areas in each component in the vanadium pre-edge peak region. To correct for possible differences in experimental conditions, we assume that the normalized pre-edge of V2O5 measured in this work is the same as the value in the literature (Chaurand et al., 2007). The normalized pre-edge in the VGDC20 sample is placed on the curve connecting the V4+(Py) and V5+(Td) states, indicating the coexistence of phases characterized by the Td and Py environments (Fig. 6). Due to the strong difference in the CNs and the symmetries between the Td and Py environments of V coexisting in this sample, the ensemble-average data will be expected to have a non-symmetric distribution of nearest neighboring V—O bonds, explaining the underestimation of the V—O CN by conventional analysis (Table 3).
4. Discussion and conclusions
In functional materials, the asymmetric bond length distribution poses the challenge to obtain reliable results by conventional
analysis. Furthermore, the low concentration of absorbing atoms, small size and experimental setup prevent us from collecting analyzable experimental spectra. In this paper, we demonstrated the efficacy of pre-edge XANES analysis by comparing two types of materials, with symmetric and asymmetric local structures. This capability is important for studies of a large class of materials with local disorder such as nanoscale oxides and nanocomposites studied under realistic operating conditions, including recently investigated materials exhibiting electro-chemo-mechanical effects. For example, though there are no reports to date on the use of V-based nanocomposites in electro-chemo-mechanical actuation, the results shown in this work suggest that, by comparison with the Ti-based composites featuring the same locally tetrahedral environment while sustaining large bonding disorder, strongly non-symmetric distribution in V-based composites may be non-conducive for applications that require rapid oxidation–reduction.The analyses presented has proven useful for two cases, where the environment around all absorbing atoms is either similar (a homogeneous case, as illustrated using Ti oxide nanocomposites) or changes across the sample (a heterogeneous case, shown using V oxide nanocomposites). In the former case, supervised machine learning-assisted analysis has been demonstrated as a powerful tool for structural ).
well beyond the relatively limited result achieved by either analysis or the pre-edge XANES fitting approach. In the latter case, special care is needed for machine learning analysis, due to the presence of multiple inequivalent environments. For that, deconvolution of the spectra using unsupervised approaches is required (Timoshenko & Frenkel, 20195. Related literature
The following reference is cited in the supporting information: McKeown et al. (2002).
Supporting information
Tables S1 and S2; Figures S1 and S2. DOI: https://doi.org/10.1107/S1600577521007025/hf5421sup1.pdf
Acknowledgements
We gratefully acknowledge help of Dr Steven Ehrlich and Dr Lu Ma with beamline experiments at the QAS beamline of NSLS-II.
Funding information
AIF, JL, YL and PKR acknowledge support by National Science Foundation, Directorate for Mathematical and Physical Sciences (grant No. DMR-1911592). IL and AIF acknowledge the NSF-BSF program (grant No. 2018717). This research used beamline 7-BM (QAS) of the National Synchrotron Light Source II, a US DOE Office of Science User Facility operated for the DOE Office of Science by Brookhaven National Laboratory (contract No. DE-SC0012704).
References
Abrahams, S. C. (1978). Mater. Res. Bull. 13, 1253–1258. CrossRef CAS Web of Science Google Scholar
Ankudinov, A. L., Rehr, J. J., Low, J. J. & Bare, S. R. (2002). J. Chem. Phys. 116, 1911–1919. Web of Science CrossRef CAS Google Scholar
Bearden, J. A. & Burr, A. F. (1967). Rev. Mod. Phys. 39, 125–142. CrossRef CAS Web of Science Google Scholar
Billinge, S. J. & Levin, I. (2007). Science, 316, 561–565. Web of Science CrossRef PubMed CAS Google Scholar
Bingham, P. A., Hannant, O. M., Reeves-McLaren, N., Stennett, M. C. & Hand, R. J. (2014). J. Non-Cryst. Solids, 387, 47–56. Web of Science CrossRef CAS Google Scholar
Boubnov, A., Timoshenko, J., Wrasman, C. J., Hoffman, A. S., Cargnello, M., Frenkel, A. I. & Bare, S. R. (2020). Radiat. Phys. Chem. 175, 108304. Web of Science CrossRef Google Scholar
Carbone, M. R., Topsakal, M., Lu, D. & Yoo, S. (2020). Phys. Rev. Lett. 124, 156401. Web of Science CrossRef PubMed Google Scholar
Chaurand, P., Rose, J., Briois, V., Salome, M., Proux, O., Nassif, V., Olivi, L., Susini, J., Hazemann, J. L. & Bottero, J. Y. (2007). J. Phys. Chem. B, 111, 5101–5110. Web of Science CrossRef PubMed CAS Google Scholar
Chill, S. T., Anderson, R. M., Yancey, D. F., Frenkel, A. I., Crooks, R. M. & Henkelman, G. (2015). ACS Nano, 9, 4036–4042. Web of Science CrossRef CAS PubMed Google Scholar
Cicco, A., Minicucci, M., Principi, E., Witkowska, A., Rybicki, J. & Laskowski, R. (2002). J. Phys. Condens. Matter, 14, 3365–3382. CrossRef Google Scholar
Dalba, G., Fornasini, P., Grisenti, R. & Rocca, F. (2004). J. Non-Cryst. Solids, 345–346, 7–15. Web of Science CrossRef Google Scholar
Dias, E. T., Gill, S. K., Liu, Y., Halstenberg, Ph., Dai, S., Huang, J., Mausz, J., Gakhar, R., Phillips, W. C., Mahurin, S., Pimblott, S. M., Wishart, J. F. & Frenkel, A. I. (2021). J. Phys. Chem. Lett. 12, 157–164. Web of Science CrossRef CAS PubMed Google Scholar
Farges, F., Brown, G. E. & Rehr, J. J. (1997). Phys. Rev. B, 56, 1809–1819. CrossRef CAS Web of Science Google Scholar
Farges, F., Lefrère, Y., Rossano, S., Berthereau, A., Calas, G. & Brown, G. E. (2004). J. Non-Cryst. Solids, 344, 176–188. Web of Science CrossRef CAS Google Scholar
Feldmann, C. (2003). Adv. Funct. Mater. 13, 101–107. Web of Science CrossRef CAS Google Scholar
Frenkel, A. I., Feldman, Y., Lyahovitskaya, V., Wachtel, E. & Lubomirsky, I. (2005). Phys. Rev. B, 71, 024116. Web of Science CrossRef Google Scholar
Frenkel, A. I., Hills, C. W. & Nuzzo, R. G. (2001). J. Phys. Chem. B, 105, 12689–12703. Web of Science CrossRef CAS Google Scholar
Giuli, G., Paris, E., Mungall, J., Romano, C. & Dingwell, D. (2004). Am. Mineral. 89, 1640–1646. Web of Science CrossRef CAS Google Scholar
Grunes, L. A. (1983). Phys. Rev. B, 27, 2111–2131. CrossRef CAS Web of Science Google Scholar
Gurman, S. J. & McGreevy, R. L. (1990). J. Phys. Condens. Matter, 2, 9463–9473. CrossRef Web of Science Google Scholar
Joly, Y. (2001). Phys. Rev. B, 63, 125120. Web of Science CrossRef Google Scholar
Levina, A., McLeod, A. I. & Lay, P. A. (2014). Chem. Eur. J. 20, 12056–12060. Web of Science CrossRef CAS PubMed Google Scholar
Liu, Y., Marcella, N., Timoshenko, J., Halder, A., Yang, B., Kolipaka, L., Pellin, M., Seifert, S., Vajda, S., Liu, P. & Frenkel, A. I. (2019). J. Chem. Phys. 151, 164201. Web of Science CrossRef PubMed Google Scholar
Makagon, E., Wachtel, E., Houben, L., Cohen, S. R., Li, Y., Li, J., Frenkel, A. I. & Lubomirsky, I. (2020). Adv. Funct. Mater. 31, 2006712. Web of Science CrossRef Google Scholar
Mazzone, G., Rivalta, I., Russo, N. & Sicilia, E. (2008). J. Phys. Chem. C, 112, 6073–6081. Web of Science CrossRef CAS Google Scholar
McGreevy, R. L. & Pusztai, L. (1988). Mol. Simul. 1, 359–367. Web of Science CrossRef Google Scholar
McGreevy, R. L. & Pusztai, L. (1990). Proc. Math. Phys. Eng. Sci. 430, 241–261. CAS Google Scholar
McKeown, D. A., Muller, I. S., Matlack, K. S. & Pegg, I. L. (2002). J. Non-Cryst. Solids, 298, 160–175. Web of Science CrossRef CAS Google Scholar
Noordhoek, M. J., Krayzman, V., Chernatynskiy, A., Phillpot, S. R. & Levin, I. (2013). Appl. Phys. Lett. 103, 022909. Web of Science CrossRef Google Scholar
Penner-Hahn, J. E. (1999). Coord. Chem. Rev. 190–192, 1101–1123. CAS Google Scholar
Prasai, B., Wilson, A. R., Wiley, B. J., Ren, Y. & Petkov, V. (2015). Nanoscale, 7, 17902–17922. Web of Science CrossRef CAS PubMed Google Scholar
Ravel, B. & Newville, M. (2005). J. Synchrotron Rad. 12, 537–541. Web of Science CrossRef CAS IUCr Journals Google Scholar
Ravel, B., Stern, E. A., Vedrinskii, R. I. & Kraizman, V. (1998). Ferroelectrics, 206, 407–430. Web of Science CrossRef Google Scholar
Ravel, B. (1995). PhD thesis. University of Washington, USA. Google Scholar
Rehr, J. J. & Albers, R. C. (2000). Rev. Mod. Phys. 72, 621–654. Web of Science CrossRef CAS Google Scholar
Rehr, J. J., Albers, R. C. & Zabinsky, S. I. (1992). Phys. Rev. Lett. 69, 3397–3400. CrossRef PubMed CAS Web of Science Google Scholar
Roscioni, O. M., Zonias, N., Price, S. W. T., Russell, A. E., Comaschi, T. & Skylaris, C. K. (2011). Phys. Rev. B, 83, 115409. Web of Science CrossRef Google Scholar
Sayers, D. E., Lytle, F. W. & Stern, E. A. (1969). Adv.X-ray Anal. 13, 248–271. Google Scholar
Sayers, D. E., Stern, E. A. & Lytle, F. W. (1971). Phys. Rev. Lett. 27, 1204–1207. CrossRef CAS Web of Science Google Scholar
Srivastava, U. C. & Nigam, H. L. (1973). Coord. Chem. Rev. 9, 275–310. CrossRef CAS Web of Science Google Scholar
Timoshenko, J., Duan, Z., Henkelman, G., Crooks, R. M. & Frenkel, A. I. (2019). Annu. Rev. Anal. Chem. 12, 501–522. Web of Science CrossRef CAS Google Scholar
Timoshenko, J. & Frenkel, A. I. (2017). Catal. Today, 280, 274–282. Web of Science CrossRef CAS Google Scholar
Timoshenko, J. & Frenkel, A. I. (2019). ACS Catal. 9, 10192–10211. Web of Science CrossRef CAS Google Scholar
Torrisi, S. B., Carbone, M. R., Rohr, B. A., Montoya, J. H., Ha, Y., Yano, J., Suram, S. K. & Hung, L. (2020). Npj Comput. Mater. 6, 109. Web of Science CrossRef Google Scholar
Ueda, S. (2013). J. Electron Spectrosc. Relat. Phenom. 190, 235–241. Web of Science CrossRef CAS Google Scholar
Vaccari, M., Aquilanti, G., Pascarelli, S. & Mathon, O. (2009). J. Phys. Condens. Matter, 21, 145403. Web of Science CrossRef PubMed Google Scholar
Vinson, J., Rehr, J. J., Kas, J. J. & Shirley, E. L. (2011). Phys. Rev. B, 83, 115106. Web of Science CrossRef Google Scholar
Wang, L., Zhang, Z. & Han, X. (2013). NPG Asia Mater. 5, e40. Google Scholar
Wende, H. (2004). Rep. Prog. Phys. 67, 2105–2181. Web of Science CrossRef CAS Google Scholar
Yamamoto, T. (2008). X-ray Spectrom. 37, 572–584. Web of Science CrossRef CAS Google Scholar
Yancey, D. F., Chill, S. T., Zhang, L., Frenkel, A. I., Henkelman, G. & Crooks, R. M. (2013). Chem. Sci. 4, 2912–2921. Web of Science CrossRef CAS Google Scholar
Yevick, A. & Frenkel, A. I. (2010). Phys. Rev. B, 81, 115451. Web of Science CrossRef Google Scholar
This article is published by the International Union of Crystallography. Prior permission is not required to reproduce short quotations, tables and figures from this article, provided the original authors and source are cited. For more information, click here.