IV, part (a) generates the upper end, rmax of the small crystallite length scale distribution P (r).
Finite Size Effects on Spin Glass Dynamics
As the magnetic ﬁeld change increases, there will come a point when the energy difference ∆ − Ez becomes com3χf cH 2 for the largest of the small parable to ∆(r) − rmax crystallites.
Finite Size Effects on Spin Glass Dynamics
For longer tw, the crystallite with dimension rmin is at equilibrium, and for that part of the sample, aging is over.
Finite Size Effects on Spin Glass Dynamics
This paper discusses spin glass dynamics for crystallites or amorphous particles of ﬁnite size.
Finite Size Effects on Spin Glass Dynamics
Ip (s, D) is the intensity proﬁle given by the dimensions of the crystallite, D = {Di ; i = 1, 2, 3}.
Bayesian inference of nanoparticle-broadened x-ray line profiles
The boundary conditions for the common-volume function are V (0, D) = V0, where V0 is the volume of the crystallite, and V (±τ, D) = 0.
Bayesian inference of nanoparticle-broadened x-ray line profiles
Figure 1: The crystallite (solid line) and its ‘ghost’ (dashed line) shifted a distance t in the direction of the scattering vector [hk l].
Bayesian inference of nanoparticle-broadened x-ray line profiles
The shaded region represents the common volume between the crystallite and ghost.
Bayesian inference of nanoparticle-broadened x-ray line profiles
In (6) a generalized measure, DD, has been used which is dependent on the crystallite shape and coordinate system.
Bayesian inference of nanoparticle-broadened x-ray line profiles
Uniform model The Fourier coeﬃcients A(t) of the fuzzy pixel/MaxEnt specimen proﬁles (not shown here) suggest the maximum size of the crystallites is ∼ 60 nm, since A(t) ∼ 0 at this length.
Bayesian inference of nanoparticle-broadened x-ray line profiles
Figure 5: Bayesian/MaxEnt crystallite size distributions using a uniform a priori model.
Bayesian inference of nanoparticle-broadened x-ray line profiles
Figure 6: Bayesian/MaxEnt crystallite size distributions using a log-normal a priori model.
Bayesian inference of nanoparticle-broadened x-ray line profiles
CeO2 specimen on average consists of spherical crystallites.
Bayesian inference of nanoparticle-broadened x-ray line profiles
Crystallites become mostly spherical only well beyond the over-critical size.
Nucleation and crystallization process of silicon using Stillinger-Weber potential
The journey of hydrogen from a molecular gas to crystallites that are trapped on quantized vortices encompasses a hierarchy of problems on many length scales.
The journey of hydrogen to quantized vortex cores
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