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.
Blue phosphorescent organic light-emitting diodes (PHOLEDs) were fabricated with tin oxide (SnOx) nano-particles (NPs) deposited at the ITO anode to improve their electrical and optical performances. SnOx NPs helped ITO to increase the work function enhancing hole injection capability. Charge balance of the device was achieved using p- and n-type mixed host materials in emissive layer and the devices’ luminance and maximum external quantum efficiency (EQE) increased about nearly 30%. Tuning the work function using solution processed NPs allows rapid optimization of device efficiency.
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