International
Tables for Crystallography Volume H Powder diffraction Edited by C. J. Gilmore, J. A. Kaduk and H. Schenk © International Union of Crystallography 2018 
International Tables for Crystallography (2018). Vol. H, ch. 3.6, pp. 291296
Section 3.6.2.6. Broadening components^{a}Department of Civil, Environmental and Mechanical Engineering, University of Trento, via Mesiano 77, 38123 Trento, Italy 
A brief account is given of the main sources of broadening that can be encountered in practice. An accent will be placed on Xrays, but extension to electrons and neutrons is in most cases straightforward. Concerning electron diffraction, precession data can be used in a straightforward way, whereas for traditional data, containing dynamical effects, further calculations, for example of the intensity, are in principle needed.
Each of the components of the diffraction instrument (i.e. source, optics, specimen stage, measurement geometry and detector) can have a dramatic impact both on the position and the broadening of the peaks. Axial divergence, for instance, introduces both an asymmetric broadening and an apparent shift of the lowangle peaks. When microstructure (i.e. specimenrelated effects) is the focus of the analysis, the primary recommendation is to try to limit the instrumental influence. Alternatively, it is preferred to have an instrumental profile (no matter how complex) that can be well described and properly simulated: for instance the profile of an instrument with a Kα_{1} primary monochromator (apparently advantageous) might be hard to model if the Kα_{2} removal is not perfect. This becomes more and more important when the instrumental effects are of the same order of magnitude as the specimenrelated broadening.
Two possible paths can be followed when dealing with the instrumental contribution: modelling using the fundamental parameters approach (see, for example, Cheary & Coelho, 1992; Kern & Coelho, 1998) or parameterization of the pattern of an ideal specimen. In the fundamental parameters approach, the geometry of the instrument and the effects of each optical component on the peak profile are described mathematically in 2θ. Most of the formulae for the various optical elements can be found, for example, in the work of Wilson (1963), Klug & Alexander (1974) and Cheary & Coelho (1992, 1994, 1998a,b). The aberration profiles are folded into the (Xray) source emission profile (Hölzer et al., 1997; Deutsch et al., 2004) to generate a combined instrumental profile.
When no information on the instrument is available, it is possible to predict the instrumental profile just by using the nominal data for the optical components. It is however advised, whenever possible, to tune the instrumental parameters using the pattern of a lineprofile standard [e.g. NIST LaB_{6} SRM 660(x) series; Cline et al., 2010] showing negligible specimen effects. These instrumentonly parameters must then be kept fixed for any subsequent microstructure refinement. It is of paramount importance that all instrumental features are well reproduced when dealing with microstructure effects. Provided that this condition is met, we can therefore employ any arbitrary function to describe the instrumental profile. Thus, as an alternative to FPA, we can either `learn' the instrumental profile from a standard (Bergmann & Kleeberg, 2001) or use a Voigtian to model it. The Voigtian is particularly convenient as it can be defined directly in L space and thus directly enter the Fourier product of equation (3.6.13).
For Xrays, the source emission profile at an energy E_{l} can be well described by a Lorentzian of energy width Γ_{l} (Hölzer et al., 1997; Deutsch et al., 2004),As dE/E = dλ/λ = ds/s, the function can also be represented as a function of s:For a laboratory tube emitting simultaneously a set of N_{λ} wavelengths, we havewhere w_{l} is the relative intensity of the lth wavelength component (referred, for example, to w_{1} = 1). The corresponding Fourier transform entering (3.6.13) can be written asThe complex term in (3.6.17) accounts for the shift of each emission component with respect to the reference one. For more flexibility (for example to consider the nonideal behaviour of the instrument), we can use a pseudoVoigt (pV) in place of the Lorentzian in equation (3.6.14).
The equation of Caglioti et al. (1958), modified by Rietveld (1969) and originally developed for constantwavelength neutron diffraction, is frequently employed for parameterization of the instrumental profile. The FWHM and the pV mixing parameter η (replacing the Lorentzian and Gaussian widths of the Voigt) are then parameterized according to functions in tan(θ) and θ, respectively (Caglioti et al., 1958; Leoni et al., 1998; Scardi & Leoni, 1999), The parameters of the Fourier transform of a Voigt or pseudoVoigt are then constrained to those of equations (3.6.18) and (3.6.19). This is particularly convenient, as the Fourier transform of a Voigtian is analytical. In fact, for the pV case we havewhere σ = FWHM/2 and where (Langford & Louër, 1982; Scardi & Leoni, 1999)Equation (3.6.18) can be found in the literature in a different form and with additional terms accounting, for example, for size effects: besides forcing a symmetry of the profile in 2θ space, these extra terms are a contradiction as they have nothing to do with the instrument itself.
In nanostructured materials, the finite size of the scattering domains is usually the dominant source of lineprofile broadening. Actually, when dealing with size, we should consider a size and a shape distribution of the domains. In most cases, one or more distributions of similar objects are considered. For an uptodate description of issues related to size broadening, see Chapter 5.1 . The domain shape is not a property of the material and therefore the use of symmetry constraints [e.g. spherical harmonics to describe the shape of the scattering object as in the model of Popa (1998) or as a size extension of Stephens' (1999) work] is not justified in the general case (Nye, 1987). Exceptions, however, exist.
The sizebroadening contribution in WPPM follows the ideas of Bertaut (1949a,b, 1950) and of Stokes & Wilson (1942). Bertaut proposed the division of the domains into columns and the analysis of the independent scattering of these columns. The columnlength distribution can always be extracted from the data: more complex models involving given shapes or distributions simply modify the way in which the columns are rearranged. Stokes and Wilson introduced the concept of a ghost to calculate the Fourier transform for a given shape: the volume common to a domain of shape c and its ghost, i.e. a copy of the same domain displaced by a quantity L along the scattering direction hkl, is proportional to the (size) Fourier transform for the given domain. The calculation has been already carried out analytically for several simple shapes characterized by a single length parameter (Stokes & Wilson, 1942; Lele & Anantharaman, 1966; Wilson, 1969; Langford & Louër, 1982; Vargas et al., 1983; Grebille & Bérar, 1985; Scardi & Leoni, 2001), and can be performed numerically in the general case (Leonardi et al., 2012). It is possible to relate the Fourier coefficients to the size values obtained from traditional methods. In particular, the areaweighted average size <L>_{S} (Warren–Averbach method) and the volumeweighted average size <L>_{V} (Williamson–Hall method) are obtained aswhere β(s) is the integral breadth and K_{k} and K_{β} are the initial slope and integral breadth Scherrer constants, respectively (Langford & Wilson, 1978; Scardi & Leoni, 2001).
The average size might have little physical significance in real cases: the size (and shape) distribution can in fact play a key role in determining both the properties and the diffraction lineprofile shapes of the powder under analysis. Fortunately, the Fourier coefficients for the polydisperse case can be easily calculated for any given distribution: the lognormal and the gamma distributions are the most common (and the most flexible). The equations and the corresponding moments areThe scattered intensity for the given distribution g_{i}, and the given shape c, iswhere w(D) = g_{i}(D)V_{c}(D) and where
With a suitable definition of the Fourier coefficients, the polydisperse case therefore becomes analogous to the monodisperse case. Analytic expressions can be obtained in particular cases. For instance, the Fourier coefficients for the lognormal and gamma distributions (Scardi & Leoni, 2001) are and respectively, wherewith the definitions already given in equations (3.6.24) and (3.6.25).
The functional forms of equations (3.6.28) and (3.6.29) clearly suggest that the profile for a lognormal distribution of domains (which is frequently encountered in practice) is not Voigtian: all traditional lineprofile analysis methods (based on Voigt or pseudoVoigt functions) are therefore unable to correctly deal with a lognormally dispersed powder.
By analogy to the monodisperse case, it is possible to relate the parameters of the polydisperse system to the size obtained with traditional methods (Warren–Averbach and Williamson–Hall, respectively). The following holds (Krill & Birringer, 1998; Scardi & Leoni, 2001):
Here, it is clear that diffraction does not provide the first moment of the distribution directly: ratios between highorder moments are involved.
Using an analytical expression for the description of a size distribution can help in stabilizing the results (as the size distribution curve is forced to be zero at very small and very large size values). Some doubts can, however, arise as to the physical validity of this forcing. An example is the case of a multimodal system. The traditional LPA techniques are unable to directly deal with multimodal size distributions. In cases where the multimodal character is clear and the various distribution are well behaved (i.e. when they can be modelled with analytical functions), the pattern can be usually modelled by considering the material as made of different fractions, each of them characterized by a different size distribution.
A possible alternative has been proposed in the literature: replacing the analytical distribution with a histogram. The ability of this model to fit the experimental data has been demonstrated (Leoni & Scardi, 2004; Matěj et al., 2011); a regularization might be necessary to stabilize the shape and/or smoothness of the size distribution. The quality of the measurement and the availability of models describing all contributions to the peak broadening are in most cases the limiting factors for extensive use of the histogram model: correlations of small sizes with the background and with features such as thermal diffuse scattering (Beyerlein et al., 2012) can in fact occur. So far, this is the only available method for exploring cases where the analytical models are unable to correctly describe the observed broadening.
A local variation of the lattice spacing (due, for example, to the presence of a defect) leads to an average phase term that, in general, is a complex quantity:The strain _{{hkl}}(L) represents the relative displacement of atoms at a (coherence) distance L along the scattering vector hkl. Knowledge of the actual source of distortion allows the explicit calculation of the various terms (van Berkum, 1994). It is quite customary to assume that the strain is the same for symmetryequivalent reflections [_{hkl}(L) = _{{hkl}}(L)]: this is a reasonable hypothesis for a powder, where we assume that any configuration is equally probable.
Traditional LPA methods such as the Warren–Averbach method (Warren & Averbach, 1950, 1952; Warren, 1990) take a firstorder MacLaurin expansion of equation (3.6.31) to extract the microstrain contribution from the measured data:The term in equation (3.6.33) would cause peak asymmetry. However, we usually consider only the secondorder moment of the strain distribution (rootmean strain or microstrain) and thus symmetric peaks. Owing to the anisotropy of the elastic properties, the broadening described by equation (3.6.32) is in general anisotropic: an extensive discussion of strain anisotropy and of the order dependence of strain broadening can be found, for example, in Leineweber & Mittemeijer (2010) and Leineweber (2011). It should be stressed that in their original form, traditional lineprofile methods are unable to deal with this anisotropy (corrections have been proposed for particular cases, for example, in the socalled modified Williamson–Hall (MWH) and modified Warren–Averbach (MWA) analyses; Ungár & Borbély, 1996).
Dislocations are often the main source of strain broadening. The magnitude of this broadening depends not only on the elastic anisotropy of the material, but also on the relative orientation of the Burgers and diffraction vectors with respect to the dislocation line (Wilkens, 1970a,b). This problem was analysed in the 1960s by Krivoglaz and Ryaboshapka (Krivoglaz & Ryaboshapka, 1963; Krivoglaz, 1969) and then subsequently reprised and completed by Wilkens (1970a,b). Further elements have been added to put it into the present form (see, for example, Krivoglaz et al., 1983; Groma et al., 1988; Klimanek & Kuzel, 1988; van Berkum, 1994; Kamminga & Delhez, 2000). For the purpose of WPPM, the distortion Fourier coefficients caused by dislocations can be written aswhere b is the modulus of the Burgers vector, is the socalled average contrast factor of the dislocations, ρ is the density of the dislocations and is an effective outer cutoff radius. Only the lowL trend of equation (3.6.34) is well reproduced by Wilkens' theory: a decaying function has thus been introduced to guarantee a proper convergence to zero of the Fourier coefficients for increasing L. Actually, the function is mostly quoted in place of , where : the multiplicative term can however be dropped, considering that the cutoff radius is an effective value [some discussion of the meaning of the f and functions and of the effective cutoff radius can be found in Scardi & Leoni (2004), Armstrong et al. (2006) and Kaganer & Sabelfeld, 2010)].
The most complete definition of is from Wilkens (1970a,b):For η < 1, the integral in (3.6.35) can be calculated in terms of special functions aswhere Li_{2}(z) and Cl_{2}(z) are the dilogarithm function (Spence's function) and the Clausen integral, respectively:The approximation proposed by van Berkum (1994) for (3.6.35) and (3.6.36),should not be employed, as the derivative is discontinuous at η = 1. A simpler approximation, valid over the whole η range, was provided by Kaganer & Sabelfeld (2010):With η_{0} = 2.2, the results of equation (3.6.41) are similar to those of (3.6.35) and (3.6.36).
Together with dislocation density and outer cutoff radius, a parameter traditionally quoted for the dislocations ensemble is Wilkens' dislocation arrangement parameter (Wilkens, 1970a). By combining the information on dislocation screening and dislocation distance, it gives an idea of the interaction of dislocations (strength of the dipole character; Ungár, 2001). A value close to or below unity indicates highly interacting dislocations (for example, dipole configurations or dislocation walls), whereas a large value is typical of a system with randomly dispersed dislocations (weak dipole character).
The anisotropic broadening caused by the presence of dislocations is mainly taken into account by the contrast (or orientation) factor C_{hkl}. The contrast factor depends on the strain field of the dislocation and therefore on the elastic anisotropy and orientation of the scattering vector with respect to the slip system considered. The average of the contrast factor over all equivalent slip systems, , is often used in the analysis of powders. The averaging is usually performed under the assumption that all equivalent slip systems are equally populated. The calculation of the contrast factor can be lengthy: full details can be found in the literature (Wilkens, 1970a,b, 1987; Krivoglaz et al., 1983; Kamminga & Delhez, 2000; Groma et al., 1988; Klimanek & Kuzel, 1988; Kuzel & Klimanek, 1989) for the cubic and hexagonal cases. For a generalization, the reader is referred to the recent work of MartinezGarcia et al. (2007, 2008, 2009). It is possible to show that the contrast factor of a given material has the same functional form as the fourthorder invariant of the Laue class (Popa, 1998; Leoni et al., 2007):In the general case, 15 coefficients are thus needed to describe the strain anisotropy effects. Symmetry reduces the number of independent coefficients: for instance, two coefficients survive in the cubic case, and the average contrast factor is (Stokes & Wilson, 1944; Popa, 1998; Scardi & Leoni, 1999)The values of A and B can be calculated from the elastic constants and slip system according to the literature (Klimanek & Kuzel, 1988; Kuzel & Klimanek, 1989; MartinezGarcia et al., 2007, 2008, 2009). Excluding the case of , the parameterization proposed by Ungár & Tichy (1999) can also be used. Some calculated values for cubic and hexagonal materials can be found in Ungár et al. (1999) and Dragomir & Ungár (2002), respectively.
As the calculation of the contrast factor for a dislocation of general character is not trivial, it is customary to evaluate it for the screw and edge case and to refine an effective dislocation character ϕ (Ungár et al., 1999),where the geometric term H is the same as in equation (3.6.43). Although not completely correct, the approach proposed in equation (3.6.44) allows the case where a mixture of dislocations of varying character are acting on equivalent slip systems to be dealt with. For a proper refinement, however, the active slip systems as well as the contrast factors of the edge and screw dislocations should be known.
It is worth mentioning that the invariant form proposed by Popa (1998) has been reprised by Stephens (1999) to describe the strainbroadening anisotropy, for example, within the Rietveld method: the formula correctly accounts for the relative broadening (i.e. for the anisotropy), but it does not give any information on the actual shape of the profiles. This is the major reason why the Stephens model can be considered as only phenomenological (it captures the trend but not the details): when the source of microstrain broadening is known, we can obtain the functional form of the profile (as proposed, for example, here for dislocations) and the model can become exact.
Planar defects, i.e. a mismatch in the regular stacking of crystallographic planes, are quite frequent in a vast family of technologically important materials and, in some cases, are responsible for their macroscopic properties. In the general case, the analysis of faulting using a Braggtype method is troublesome. The local change in the structure causes the appearance of diffuse scattering (i.e. extra intensity) between the Bragg spots. This can be handled in the singlecrystal case (Welberry, 2004), but can be challenging in a powder, where the reciprocal space is rotationally averaged and the (weak) diffuse scattering is lost in the background. The handling of diffuse phenomena is the main difference between the Rietveld (1969) and the pair distribution function (PDF) (Billinge, 2008) methods.
A simple description of the broadening effects of faulting, useful for WPPM, is available only for a restricted class of systems, namely facecentred cubic (f.c.c.) (), bodycentred cubic (b.c.c.) () and hexagonal close packed (h.c.p.) (P6_{3}/mmc) lattices. Monatomic metals with f.c.c. (e.g. Cu, Ni and Au), h.c.p. (e.g. Ti, Co and Zr) and b.c.c. (e.g. W and Mo) structures fall into this list. Faulting in the wurtzite structure (P6_{3}mc) leading to a local transformation into sphalerite () can be handled with rules completely analogous to those for the h.c.p./f.c.c. case. The main types of faults in all of these systems are the socalled deformation and twin faults: looking at the planes on the two sides of the faulting, a deformation fault appears as a shear, whereas twinning causes a mirroring of the atomic positions. The effect of these defects can be modelled using recurrence equations for the stacking. Initially proposed for the h.c.p. case by Wilson (Edwards & Lipson, 1942; Wilson, 1942), this idea was then extended to the f.c.c. case (Paterson, 1952; Gevers, 1954a,b; Warren, 1959, 1963). More recently, EstevezRams et al. (2003, 2008) improved the accuracy and extended the validity range by including all terms in the stacking probability formulae, whereas Velterop et al. (2000) corrected the formalism to properly take the various hkl components of a peak into account.
In an f.c.c. system, reliable information can be obtained up to a few per cent of faults on the {111} plane. The trick is to describe the lattice with hexagonal axes, effectively transforming the problem into that of <001> stacking on the {111} plane. Under these hypotheses, the average phase term due to faulting can be written aswhere L_{0} = h + k + l and . The lattice symmetry influences the definitions of these two parameters. Faulting is one of the typical cases where a complex (sine) term is present, as peak shift and asymmetry in the profiles is expected (unless twin faults are absent). Following the treatment of Warren (see, for example, Warren, 1963), a set of recurrence equations can be written for the probability of the occurrence of faulting. The solution of the recurrence equations is used to generate the Fourier coefficients for faulting. In particular, if the probabilities of deformation and twin fault are α and β, respectively, thenand, introducing the sign function,the Fourier coefficients can be obtained asBesides being asymmetric, each profile subcomponent can also be shifted with respect to the average Bragg position. For the subcomponent hkl the shift isIn a given reflection family {hkl}, reflections affected and unaffected by faulting coexist, leading to peculiar shapes of the corresponding peak profiles.
Analogous formulae can be obtained for the b.c.c. and h.c.p. cases. In the former, the selection rule becomes L_{0} = − h − k + 2l, whereas for the latter L_{0} = l and the condition for faulting is based on h − k = 3N ± 1. Implementation requires the application of the proper formula to the particular reflection hkl considered in the analysis.
Analysing faults by observing just the peak shift, as in the original treatment of Warren (1959, 1963) or within the Warren–Averbach method (Warren & Averbach, 1950, 1952), would be erroneous, as it does not take the fine details of the broadening into account.
An alternative to the adoption of Warren's formalism was proposed by Balogh et al. (2006). Instead of performing the calculation explicitly, the authors parameterized the profiles obtained from the DIFFaX software (Treacy et al., 1991) calculated for increasing quantities of faulting. The DIFFaX software is based on a recursive description of the stacking: the intensity is calculated along rods in reciprocal space using the tangent cylinder approximation. The parameterization, which is performed in terms of a sum of Lorentzian curves, is then employed for the evaluation of the faultbroadening profile at any angle. The modelling should be performed on a profile that contains a faultingonly contribution: note that for high faulting probabilities, it becomes arbitrary whether to assign the diffuse scattering part to one or another Bragg reflection. This introduces some arbitrariness in the subsequent (directional) convolution of the faulting profile with the other broadening effects. When applicable, however, this parameterization has several advantages: it takes the actual shape of the reciprocalspace rods into account (in an effective way), it does not necessitate any hkl selection rule and an analytical treatment can be employed, as the Lorentzian has an analytical transform. With the above caveats, it is in principle not even necessary to decompose the DIFFaXgenerated profile if a numerical convolution is employed. This would also correspond to an extension of WPPM to the DIFFaX+ idea (Leoni, Gualtieri & Roveri, 2004; Leoni, 2008), or vice versa, where DIFFaX+ uses a corrected and improved version of the recursive approach of DIFFaX to generate the profiles, but allows the refinement of all model parameters. [DIFFaX+ is available from the author (matteo.leoni@unitn.it) on request.]
In the diffraction pattern of an ordered alloy, a dissimilar broadening can often be observed for structure and superstructure peaks (with the former being present in both the ordered and disordered states). The superstructure peaks, in fact, bear microstructural information on the interface between the ordered regions in the material: broadening occurs when domains meet out of phase, creating an antiphase domain boundary (APB or APDB). A general formula for APDBrelated broadening does not exist: for a given ordered structure, the Fourier coefficients correspond to the normalized value of , where F(0) is the structure factor of a cell positioned at L = 0 and is the complex conjugate of the structure factor of a cell at a distance L along the direction [hkl]. Being the result of a combination of probabilities, the peak is always expected to be Lorentzian.
Explicit formulae have been derived for the Cu_{3}Au ordered alloy (L1_{2} phase; Wilson, 1943; Wilson & Zsoldos, 1966; Scardi & Leoni, 2005). Several types of boundaries can form, depending on the way that the domains meet: the broadening depends both on the boundary plane and on the local arrangement of Au atoms leading to conservative (no Au atoms in contact) or nonconservative (Au atoms in contact) boundaries. By arranging the indices in such a way that h ≥ k ≥ l and that l is always the unpaired index, the broadening of the superstructure reflections can be described as (Scardi & Leoni, 2005)In this formula, δ = γ_{APDB}/a_{0} is the probability of occurrence of an APDB, a_{0} is the unitcell parameter and f(h, k, l) is a function of hkl defined in Table 3.6.2, obtained from the results of Wilson (1943) and Wilson & Zsoldos (1966).

The average distance between two APDBs is given by 1/δ. For a random distribution of faults, the broadening is Lorentzian and A^{APDB} = exp(−4Lδ/3).
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