Sources of systematic error are discussed, and a method for extending the technique towards simultaneous measurements of ensembles of grains is outlined.
Representative ensembles of grains in 2D or 3D are created by a lattice Monte Carlo (MC) method, which minimizes the interfacial energy of grains starting from an initial grain distribution.
Solutions to the gradient flow of this free energy can be interpreted as ensembles of grains (in which the phase of the order parameter is approximately constant in space) separated by mobile grain boundaries.
We present a two-dimensional granular model for the mechanical behavior of an ensemble of globular grains during solidification.
The degree of the alignment is defined by an order parameter 〈m〉, which is an average of 1/2 3cos2 θ - 1) over an ensemble of measured grains.
Here we use an optimally sized26,27 multi-model ensemble of six grain maize models and eight winter wheat models, hereafter maize and wheat, respectively, to analyze the drivers of current (1984 2009) yield variability and projected (2040 2069) yield changes.
Or in other words, the specimens are not single crystal layers, but rather a large ensemble of small MoS2 grains with uniform thickness and excellent lateral coherence.
The result is a ensemble of structural coarse-grain subsets, either double or single stranded, of the target molecule (we show examples in Fig. 1D).
We consider an ensemble of rigid, arbitrarily shaped grains as a set with structure.
