Crystallite size is the mean extent of coherently diffracting domains within a powder or polycrystal, a quantity distinct from particle size because one particle may contain many such domains. Diffraction line broadening is the usual access route: the Scherrer relation converts peak width into a domain dimension, and its extensions recover a size distribution rather than a single number [1]. Size broadening overlaps with strain broadening in the same profile, so separating the two requires several orders of reflection or a full-profile treatment [2]. Different routes to the same quantity give systematically different answers, as comparisons on hydroxyapatite show [3], and the same caution applies when the phase sits in a deformed matrix [4]. Whether the value should be read as a physical domain dimension or as an instrumental index of peak width depends on how much of the profile has been modelled; current surveys place these classical treatments alongside machine-learning analysis of diffraction data [5].
- scherrer equation
- x-ray line broadening
- coherent diffraction domain
- microstrain
- peak profile fitting
- powder diffraction
- domain size distribution
References
- Bienenstock, A.; Determination of Crystallite Size Distributions from X-Ray Line Broadening. Journal of Applied Physics 1961, 32, 187-189, 10.1063/1.1735975.
- Ferguson, I.F.; Crystallite size, strain and shape determinations on thoria from X-ray line broadening studies. Philosophical Magazine 1965, 12, 1115-1122, 10.1080/14786436508228664.
- Rabiei, M.; Palevicius, A.; Monshi, A.; Nasiri, S.; Vilkauskas, A.; Janusas, G.; Comparing Methods for Calculating Nano Crystal Size of Natural Hydroxyapatite Using X-Ray Diffraction. Nanomaterials 2020, 10, 1627, 10.3390/nano10091627.
- Feijoo, I.; Cabeza, M.; Merino, P.; Pena, G.; Pérez, M.; Cruz, S.; Rey, P.; Estimation of crystallite size and lattice strain in nano-sized TiC particle-reinforced 6005A aluminium alloy from X-ray diffraction line broadening. Powder Technology 2019, 343, 19-28, 10.1016/j.powtec.2018.11.010.
- Surdu, V.A.; Győrgy, R.; X-ray Diffraction Data Analysis by Machine Learning Methods—A Review. Applied Sciences 2023, 13, 9992, 10.3390/app13179992.