Profile fitting parameters peak shape

When all the phases present were identified, we can quantify their volume fraction in the analyzed volume similarly to the way the Rietveld-method is used for phase analysis in XRD. A whole profile fitting is used in ProcessDifraction, modeling background and peak-shapes, and fitting the shape parameters, thermal parameters and volume fractions. Since the kinematic approximation is used for calculating the electron diffraction intensities, the grain size of both phases should be below 10 nm (as a rule of... 

The NMRD profile of the protein adduct shows a largely increased relaxivity, with the dispersion moved at about 1 MHz and a relaxivity peak in the high field region. This shape is clearly related to the fact that the field dependent electron relaxation time is now the correlation time for proton relaxation even at low fields. The difference in relaxivities before and after the dispersion is in this case very small, and therefore the profile cannot be well fit with the SBM theory, and the presence of a small static ZFS must be taken into account 103). The best fit parameters obtained with the Florence NMRD program are D = 0.01 cm , A = 0.017 cm , t = 18x10 s, and xji =0.56 X 10 s. Such values are clearly in agreement with those obtained with fast-motion theory 101). 

In general, three different approaches to the description of peak shapes can be used. The first employs empirical peak shape functions, which fit the profile without attempting to associate their parameters with physical quantities. The second is a semi-empirical approach that describes instrumental and wavelength dispersion functions using empirical functions, while specimen properties are modeled using realistic physical parameters. In the third, the so-called fundamental parameters approach, all three components of the peak shape function (Eq. 2.45) are modeled using rational physical quantities. 

In Eq. 2.61 a is a free variable, i.e. the asymmetry parameter, which is refined during profile fitting and z,- is the distance fi om the maximum of the symmetric peak to the corresponding point of the peak profile, i.e. z,-= 20yfc - 20 . This modification is applied separately to every individual Bragg peak, including Kaj and Ka2 components. Since Eq. 2.61 is a simple intensity multiplier, it may be easily incorporated into any of the peak shape functions considered above. Additionally, in the case of the Pearson-VII function, asymmetry may be treated differently. It works nearly identical to Eq. 2.61 and all variables have the same meaning as in this equation but the expression itself is different ... 

Depending on the quality of the pattern, profile fitting can be conducted in several different ways. They differ in how peak positions and peak shape parameters are handled, assuming that integrated intensities are always refined independently for each peak, and a single set of parameters describes a background within the processed range ... 

In this example, lattice parameters and the zero shift correction have a substantial impact on the quality of the fit and the weighted profile residual, Rwp, decreases nearly two-fold (from 24 to 12 %), while the refinement of peak shape parameters decreases R p by only 4 %. Therefore, in this case lattice parameters should have been refined first. However, it is not always obvious which parameters are more important and should be released at a particular stage of the least squares refinement. Because of this, in complex cases a trial-and-error approach is often employed. ... 

Full profile refinement is computationally intense and employs the nonlinear least squares method (section 6.6), which requires a reasonable initial approximation of many fi ee variables. These usually include peak shape parameters, unit cell dimensions and coordinates of all atoms in the model of the crystal structure. Other unknowns (e.g. constant background, scale factor, overall atomic displacement parameter, etc.) may be simply guessed at the beginning and then effectively refined, as the least squares fit converges to a global minimum. When either Le Bail s or Pawley s techniques were employed to perform a full pattern decomposition prior to Rietveld refinement, it only makes sense to use suitably determined relevant parameters (background, peak shape, zero shift or sample displacement, and unit cell dimensions) as the initial approximation. 

Moment analysis offers only two global parameters to characterize the peak while the exact peak shape is not taken into account, which makes this method more sensitive to signal distortions. By fitting of either analytical equations (Section 6.5.33) or simulation results to the peak shape this drawback may be overcome. It also allows an easy comparison between the calculated peak and the measured concentration profile. 

The above description is actually a simplified version of reality since a high-resolution analysis of the spectral lines of Cu Koc shows that both the oci and 0C2 peaks are distinctly asymmetric. An understanding of the origin of this asymmetry is important in implementing the so-called fundamental parameters approach to the profile fitting of powder diffraction data peaks, described in Chapters 5, 6, 9 and 13, in which the detailed spectrum of the incident X-rays must be known. A combination of five Lorentzian functions is commonly used to model the peak shape of Cu radiation, though detailed investigations to characterize the X-ray spectrum continue. ... 

Further aspects, pros and cons of WPPF, are discussed in Chapter 5. Here it is important to underline the fact that the validity of profile fitting is limited by the basic assumption of using an a priori selected profile function without any sound hypothesis that the specific functional form is appropriate to the case of study. The consequence of this arbitrary assumption can be quite different. For example, in most practical cases, profile fitting can provide reliable values of peak position and area, whereas the effects on the profile parameters are less known and rarely considered. The arbitrary choice of a profile function tends to introduce systematic errors in the width and shape parameters, which invariably introduce a bias in a following LPA, whose consequences can hardly be evaluated. It is therefore a natural tendency, for complex problems and to obtain more reliable results, to remove the a priori selected profile functions - leading to the following section dedicated to Whole Powder Pattern Modelling methods. 

It is a straightforward matter to fit various model profiles to realistic, exact computed profiles, selecting a greater or lesser portion near the line center of the exact profile for a least mean squares fit. In this way, the parameters and the root mean square errors of the fit may be obtained as functions of the peak-to-wing intensity ratio, x = G(0)/G(comax)- As an example, Fig. 5.8 presents the root mean square deviations thus obtained, in units of relative difference in percent, for two standard models, the desymmetrized Lorentzian and the BC shape, Eqs. 3.15 and 5.105, respectively. 

In this technique, structural parameters are refined to fit the overall profile of the powder, neutron-diffraction pattern, which is assumed to consist of Gaussian-shaped peaks, centered at the Bragg-angle positions. The data consist of the point-intensity counts over the angular scan, and overlapping peaks are treated separately, using their contributions to the point intensities. 

In Fig. 6.15 two different models for parameter estimation are used and the resulting simulated concentration profiles are compared with the measurements. In one case ideal plug-flow (Eq. 6.116) and in the other axial dispersive flow (Eq. 6.117) is assumed for the pipe system, while both models use the C.S.T. model (Eq. 6.121) to describe the detector system. Figure 6.15 shows that the second model using axial dispersion provides an excellent fit for this set-up, while the other cannot predict the peak deformation. Because of the asymmetric shape a model without a tank would also be inappropriate. 

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