Gulnaz Ganeeva | Searcharxiv

arXiv2026

Cathodoluminescence Analysis of Defects and Grain Boundaries in Zn3P2 Thin Films Grown on Graphene by MOVPE and MBE

Zn3P2 is a promising earth-abundant absorber for thin-film photovoltaics, yet its development is hindered by the lack of lattice-matched substrates, its incompatible thermal expansion coefficient, and a complex defect landscape. Here, we demonstrate the quasi-van der Waals epitaxy of Zn3P2 on graphene by metal-organic vapour phase epitaxy (MOVPE) and directly link the density of antiphase boundaries to optical emission modulation using correlative electron microscopy and cathodoluminescence (CL). Moreover, it is observed through CL that grain boundaries act as non-radiative sinks for excited charge carriers. The effect extends several micrometres into the grains, making grain boundaries detrimental to the applicability of Zn3P2 in read devices. Further comparison with molecular beam epitaxy grown films reveals the suppression of strain-related sub-bandgap emission in MOVPE-grown material. Overall, quasi-van der Waals epitaxy of Zn3P2 by MOVPE resulted in larger grains and improved material quality. In addition, these results directly link extended defects to recombination pathways in Zn3P2 and highlight grain-size control as a key strategy for improving earth-abundant photovoltaic absorbers.

cond-mat.mtrl-sci↗

arXiv2021

3D Reconstruction of Curvilinear Structures with Stereo Matching DeepConvolutional Neural Networks

Curvilinear structures frequently appear in microscopy imaging as the object of interest. Crystallographic defects, i.e., dislocations, are one of the curvilinear structures that have been repeatedly investigated under transmission electron microscopy (TEM) and their 3D structural information is of great importance for understanding the properties of materials. 3D information of dislocations is often obtained by tomography which is a cumbersome process since it is required to acquire many images with different tilt angles and similar imaging conditions. Although, alternative stereoscopy methods lower the number of required images to two, they still require human intervention and shape priors for accurate 3D estimation. We propose a fully automated pipeline for both detection and matching of curvilinear structures in stereo pairs by utilizing deep convolutional neural networks (CNNs) without making any prior assumption on 3D shapes. In this work, we mainly focus on 3D reconstruction of dislocations from stereo pairs of TEM images.

eess.IV↗