EXAFS data evaluation

Estimated experimental errors are 0.02 A for atomic distance rand 0.2 for coordination number n in the EXAFS data evaluation. 

The reflectivity spectra R(E) and the reflectivity-EXAFS Xr(E) = R(E) — Rq(E)]/R()(E) are similar, but not identical, to the absorption spectra and x(E) obtained in transmission mode. R(E) is related to the complex refraction index n(E) = 1 — 8(E) — ifl(E) and P(E) to the absorption coefficient /i(E) by ji fil/An. P and 8 are related to each other by a Kramers-Kronig transformation, p and 8 may be also separated in an oscillatory (A/ , AS) and non-oscillatory part (P0,80) and may be used to calculate Xr- This is, briefly, how the reflectivity EXAFS may be calculated from n(E). which itself can be obtained by experimental transmission EXAFS of standards, or by calculation with the help of commercial programs such as FEFF [109] with the parameters Rj, Nj and a, which characterize the near range order. The fit of the simulated to measured reflectivity yields then a set of appropriate structure parameters. This method of data evaluation has been developed and has been applied to a few oxide covered metal electrodes [110, 111], Fig. 48 depicts a condensed scheme of the necessary procedures for data evaluation. 

The coordinative environment of manganese in sediment-trap material was evaluated with EXAFS spectra recorded at the Mn K-edge. Figure 7 shows the radial distribution function (RDF) around Mn atoms of a sediment-trap sample. The RDFs of pyrochroite, manganite, Na-bimessite, todorokite, and vemadite are also shown. A comparison of EXAFS data with results from X-ray diffraction (XRD) is shown in Table III for all three oxidation states of manganese. 

Spadini, L. et al., Hydrous ferric oxide Evaluation of Cd-HFO surface complexation models combining Cd EXAFS data, potentiometric titration results and surface site structures identified from mineralogical knowledge, J. Colloid Interf. Sci., 266, 1, 2003. 

As stated above, knowledge of the functions Fj(A) and 4> k) [or 4>i(k) and j(A), from which

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In experimental EXAFS studies one measures the x-ray absorption the extraction of the EXAFS spectrum from the absorption spectrum is straightforward. In the further data evaluation the Fourier transformation of the measured EXAFS weighted by a factor X" is performed in practice, = 1 and n = 3 are used routinely. The Fourier transform, which is a function of the length R, peaks approximately at those distances that correspond to the positions of the scattering atoms (Figure 22). EXAFS measurements make it possible to determine distances between neighboring atoms with an accuracy of up to 0.01-0.03 A, if calibration... 

A powerful but expensive tool for tackling this problem is the technique of EXAFS. but it requires that the constituents have high scattering cross sections [56j. From an analysis of EXAFS data the number of atoms of a particular kind at a particular distance from a chosen absorber atom. i.e.. those in coordination spheres, can be evaluated. An EXAFS determination for each alloy constituent can provide information on the individual environment of atoms of that constituent on the support, thus indicating whether alloy formation has occurred. 

The usual procedure of the evaluation of an EXAFS data of the XA spectrum involves first the background subtraction and the evaluation of % (Figure 1.4c). Then one has to separate the contribution of the various coordination shells to visualize their data. This is done by a Fourier transform to the distance space. Figure 1.4d clearly shows four well-developed shells of crystalline Ni metal, R to R, which are characteristic of a cubic face-centered metal. This result still needs a correction by the phase shift <1> and the evaluation of Ni and o. This occurs for specific specimens by comparison with EXAFS data of well-characterized standards like nickel metal in this case or by calculation of the EXAFS results with a program like FEFF [12] or data analysis packages from the Internet with reasonable assumptions [13]. For this purpose, the % data of each shell are separated and... 

Hie evaluation of the data yields Rjy Nj, and Sjy i.e., the near-range order parameters of the material seen from the absorber atom. XAS permits the evaluation of the near-range order in the vicinity of the atoms of various elements of one specimen if the energies of their absorption edges are different enough and thus are well separated within the spectrum. It should be mentioned that XAS in reflection looks similar to XAS in transmission mode, however it is different and the evaluation of measurements requires the comparison with reflectivity data calculated form transmission EXAFS spectra. These evaluation procedures involving Kramers-Kronig transform are described in the literature [i-v]. 

More recently, several reports on the structure of molten CuBr have appeared. Using EXAFS, the authors have evaluated Cu-Cu, Br-Br, and Cu-Br distances. Overall the data seem to suggest that, unlike in the above example, the liquid and the solid structures are quite similar and are dominated by covalent interactions. Similar results have been found for AgBr [99, 120, 121]. 

Figure 4.30 Determination of interatomic distances in platinum-iridium catalysts by evaluation of the quality of fit of the expression for the sum of the platinum L EXAFS function and the function I,(K) arising from the iridium L m EXAFS to the corresponding sum derived from experimental data (48). (Reprinted with permission from the American Institute of Physics.)...

The final task in the evaluation of SCM for predicting sorption behavior was to assess its ability to predict sorption for a weakly sorbing (outer-sphere) divalent metal ion. Because previous studies reporting EXAFS results for Sr(II) sorption to aluminum oxides were not available in the literature prior to our modeling efforts, Sr(II) XAS data were collected as part of this work. The Sr(II) XAS data were collected for a strontium nitrate solution and the samples shown in Table 7-7. 

Iron nanoparticles prepared by pyrolysis of poly(ferrocenylsilanes) inside periodic mesoporous sihca displayed the absence of room-temperature hysteresis in the magnetization curves which shows their superparamagnetic behavior [55]. However, magnetic properties cannot always be easily interpreted. For example, for this material data analysis of magnetization curves resulted in the ambiguous conclusion that either particle size distribution is bimodal, or iron particles have an oxide layer which behaves as small superparamagnetic nanoparticles. So magnetic measurements should be combined with other techniques (probably, in this case, EXAFS may be useful) to allow more accurate evaluation of particle structure. 

Data analysis was performed with a program package, specially designed for the evaluation of liquid or amorphous systems [109]. The background removal was done by use of a modified smoothing spline algorithm, and subsequent normalization with the determined spline. The k ranees of the weiehted EXAFS functions of... 

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