Unraveling the Formation of Ternary AgCuSe Crystalline Nanophases and Their Potential as Antibacterial Agents.

AgCuSe nanoparticles could contribute to the growth of strongly light-absorbing thin films and solids with fast ion mobility, among other potential properties. Nevertheless, few methods have been developed so far for the synthesis of AgCuSe nanoparticles, and those reported deliver nanostructures with relatively large sizes and broad size and shape distributions. In this work, a colloidal cation exchange method is established for the easy synthesis of AgCuSe NPs with ca.

Revealing Local Grain Boundary Chemistry and Correlating it with Local Mass Transport in Mixed-Conducting Perovskite Electrodes.

Grain boundary (GB) mass transport, and chemistry exert a pronounced influence on both the performance and stability of electrodes for solid oxide electrochemical cells. Lanthanum strontium cobalt ferrite (LSCF6428) is applied as a model mixed ionic and electronic conducting (MIEC) perovskite oxide. The cation-vacancy distribution at the GBs is studied at both single and multi-grain scales using high-resolution characterization techniques and computational approaches.

Machine Learning Data Augmentation Strategy for Electron Energy Loss Spectroscopy: Generative Adversarial Networks.

Recent advances in machine learning (ML) have highlighted a novel challenge concerning the quality and quantity of data required to effectively train algorithms in supervised ML procedures. This article introduces a data augmentation (DA) strategy for electron energy loss spectroscopy (EELS) data, employing generative adversarial networks (GANs). We present an innovative approach, called the data augmentation generative adversarial network (DAG), which facilitates data generation from a very limited number of spectra, around 100.

Comparative of machine learning classification strategies for electron energy loss spectroscopy: Support vector machines and artificial neural networks.

Machine Learning (ML) strategies applied to Scanning and conventional Transmission Electron Microscopy have become a valuable tool for analyzing the large volumes of data generated by various S/TEM techniques. In this work, we focus on Electron Energy Loss Spectroscopy (EELS) and study two ML techniques for classifying spectra in detail: Support Vector Machines (SVM) and Artificial Neural Networks (ANN). Firstly, we systematically analyze the optimal configurations and architectures for ANN classifiers using random search and the tree-structured Parzen estimator methods.