Effect of Extended Defects on AlGaN Quantum Dots for Electron-Pumped Ultraviolet Emitters.

We study the origin of bimodal emission in AlGaN/AlN QD superlattices displaying a high internal quantum efficiency (around 50%) in the 230-300 nm spectral range. The secondary emission at longer wavelengths is linked to the presence of cone-like domains with deformed QD layers, which originate at the first AlN buffer/superlattice interface and propagate vertically. The cones originate at a 30°-faceted shallow pit in the AlN, which appears to be associated with a threading dislocation that produces strong shear strain. The cone-like structures present Ga enrichment at the boundaring facets and larger QDs within the conic domain. The bimodality of the luminescence is attributed to the differing dot size and composition within the cones and at the faceted boundaries, which is confirmed by the correlation of microscopy results and Schrödinger-Poisson calculations.

Gradient-based and wavelet-based compressed sensing approaches for highly undersampled tomographic datasets.

Electron tomography is widely employed for the 3D morphological characterization at the nanoscale. In recent years, there has been a growing interest in analytical electron tomography (AET) as it is capable of providing 3D information about the elemental composition, chemical bonding and optical/electronic properties of nanomaterials. AET requires advanced reconstruction algorithms as the datasets often consist of a very limited number of projections. Total variation (TV)-based compressed sensing approaches were shown to provide high-quality reconstructions from undersampled datasets, but staircasing artefacts can appear when the assumption about piecewise constancy does not hold. In this paper, we compare higher-order TV and wavelet-based approaches for AET applications and provide an open-source Python toolbox, Pyetomo, containing 2D and 3D implementations of both methods. A highly sampled STEM-HAADF dataset of an Er-doped porous Si sample and a heavily undersampled STEM-EELS dataset of a Ge-rich GeSbTe (GST) thin film annealed at 450°C are used to evaluate the performance of the different approaches. We show that polynomial annihilation with order 3 (HOTV3) and the Bior4.4 wavelet outperform the classical TV minimization and the related Haar wavelet.