A random scattering approach to enhance light extraction in white top-emitting organic light-emitting diodes (OLEDs) is reported. Through solution processing from fluorinated solvents, a nano-particle scattering layer (NPSL) can be deposited directly on top of small molecule OLEDs without affecting their electrical performance. The scattering length for light inside the NPSL is determined from transmission measurements and found to be in agreement with Mie scattering theory. Furthermore, the dependence of the light outcoupling enhancement on electron transport layer thickness is studied. Depending on the electron transport layer thickness, the NPSL enhances the external quantum efficiency of the investigated white OLEDs by between 1.5 and 2.3-fold. For a device structure that has been optimized prior to application of the NPSL, the maximum external quantum efficiency is improved from 4.7% to 7.4% (1.6-fold improvement). In addition, the scattering layer strongly reduces the undesired shift in emission color with viewing angle.

Can we really “read the mind in the eyes”? Moreover, can AI assist us in this task? This paper answers these two questions by introducing a machine learning system that predicts personality characteristics of individuals on the basis of their face. It does so by tracking the emotional response of the individual’s face through facial emotion recognition (FER) while watching a series of 15 short videos of different genres. To calibrate the system, we invited 85 people to watch the videos, while their emotional responses were analyzed through their facial expression. At the same time, these individuals also took four well-validated surveys of personality characteristics and moral values: the revised NEO FFI personality inventory, the Haidt moral foundations test, the Schwartz personal value system, and the domain-specific risk-taking scale (DOSPERT). We found that personality characteristics and moral values of an individual can be predicted through their emotional response to the videos as shown in their face, with an accuracy of up to 86% using gradient-boosted trees. We also found that different personality characteristics are better predicted by different videos, in other words, there is no single video that will provide accurate predictions for all personality characteristics, but it is the response to the mix of different videos that allows for accurate prediction.

Electronic charge-coupled device (CCD) cameras equipped with image intensifiers are increasingly being used for radiographic applications. These systems may be used to replace film recording for static imaging, or at other times CCDs coupled with electro-optical shutters may be used for static or dynamic (explosive) radiography. Image intensifiers provide precise shuttering and signal gain. We have developed a set of performance measures to calibrate systems, compare one system to another, and to predict experimental performance. The performance measures discussed in this paper are concerned with image quality parameters that relate to resolution and signal-to-noise ratio.

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