Reproducing morphologies of disorderly self-assembling planar molecules with static and dynamic simulation methods by matching density

Bumstead, Matt; Arnold, Björn; Turak, Ayse

doi:10.1016/j.physa.2016.12.075

Cited by 2 publications

(2 citation statements)

References 67 publications

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“…Figure 3(b) outlines this normalization process, such that all functions now share the first maximum peak at the same distance. The package automatically extracts the residual difference spectrum, Δ g ( r ) 41 , (Fig. 3(b) top panel), which allows researchers to quickly determine the relative amount of positional fluctuation of particles relative to any reference data set.…”

Section: Resultsmentioning

confidence: 99%

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disLocate: tools to rapidly quantify local intermolecular structure to assess two-dimensional order in self-assembled systems

Bumstead

Liang

Hanta

et al. 2018

Sci Rep

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Order classification is particularly important in photonics, optoelectronics, nanotechnology, biology, and biomedicine, as self-assembled and living systems tend to be ordered well but not perfectly. Engineering sets of experimental protocols that can accurately reproduce specific desired patterns can be a challenge when (dis)ordered outcomes look visually similar. Robust comparisons between similar samples, especially with limited data sets, need a finely tuned ensemble of accurate analysis tools. Here we introduce our numerical Mathematica package disLocate, a suite of tools to rapidly quantify the spatial structure of a two-dimensional dispersion of objects. The full range of tools available in disLocate give different insights into the quality and type of order present in a given dispersion, accessing the translational, orientational and entropic order. The utility of this package allows for researchers to extract the variation and confidence range within finite sets of data (single images) using different structure metrics to quantify local variation in disorder. Containing all metrics within one package allows for researchers to easily and rapidly extract many different parameters simultaneously, allowing robust conclusions to be drawn on the order of a given system. Quantifying the experimental trends which produce desired morphologies enables engineering of novel methods to direct self-assembly.

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Section: Resultsmentioning

confidence: 99%

“…In Fig. 5(a) , we present a simulated configuration of ( N = 256) micelle particles under square confinement 41 . This type of self-assembly causes internal frustration of the particles and leads to a different ordering in the center than that around the wall 67 .…”

Section: Resultsmentioning

confidence: 99%