arXiv subjects

Stavros Nicolopoulos

Publications and source records attributed to Stavros Nicolopoulos.

4 recordsLinked to original sources

arXiv2025

In Situ Nanometer-Resolution Strain and Orientation Mapping for Gas-Solid Reactions via Precession-Assisted Four-dimensional Scanning Transmission Electron Microscopy

Chemomechanical interactions in gas or liquid environments are crucial for the functionality and longevity of various materials used in sustainable energy technologies, such as rechargeable batteries, water-splitting catalysts, and next-generation nuclear reactors. A comprehensive understanding of nanoscale strain evolution involved in these processes can advance our knowledge of underlying mechanisms and facilitate material design improvements. However, traditional microscopy workflows face challenges due to trade-offs between field of view (FOV), spatial resolution, temporal resolution, and electron beam damage, particularly in gas or liquid environments. Here, we demonstrate in situ nanometer-resolution strain and orientation mapping in a temperature-controlled gas environment with a large FOV. This is achieved by integrating a microelectromechanical system (MEMS)-based closed-cell TEM holder, precession-assisted four-dimensional scanning transmission electron microscopy (4D-STEM), and a direct electron detector (DED). Using the strain evolution during zirconium initial oxidation as a case study, we first outline critical strategies for focused ion beam gas-cell sample preparation and gas-phase TEM workflows to enhance experimental success. We then show that integrating DED with precession electron diffraction and optimizing gas pressure substantially improve the quantity and quality of the detected Bragg peaks in nano-beam electron diffraction patterns, enabling more precise strain measurements. Furthermore, we introduce a practical protocol to pause the reactions, allowing sufficient time for 4D-STEM data collection while ensuring the temporal resolution needed to resolve material dynamics. Our methodology and workflow provide a robust framework for quantitative analysis of chemomechanical evolutions in materials exposed to gas or liquid environments.

cond-mat.mtrl-sci↗

arXiv2025

Unsupervised Multi-Clustering and Decision-Making Strategies for 4D-STEM Orientation Mapping

This study presents a novel integration of unsupervised learning and decision-making strategies for the advanced analysis of 4D-STEM datasets, with a focus on non-negative matrix factorization (NMF) as the primary clustering method. Our approach introduces a systematic framework to determine the optimal number of components (k) required for robust and interpretable orientation mapping. By leveraging the K-Component Loss method and Image Quality Assessment (IQA) metrics, we effectively balance reconstruction fidelity and model complexity. Additionally, we highlight the critical role of dataset preprocessing in improving clustering stability and accuracy. Furthermore, our spatial weight matrix analysis provides insights into overlapping regions within the dataset by employing threshold-based visualization, facilitating a detailed understanding of cluster interactions. The results demonstrate the potential of combining NMF with advanced IQA metrics and preprocessing techniques for reliable orientation mapping and structural analysis in 4D-STEM datasets, paving the way for future applications in multi-dimensional material characterization.

cs.LG↗

arXiv2023

Liquid phase fast electron tomography unravels the true 3D structure of colloidal assemblies

Electron tomography has become a commonly used tool to investigate the three-dimensional (3D) structure of nanomaterials, including colloidal nanoparticle assemblies. However, electron microscopy is typically carried out under high vacuum conditions. Therefore, pre-treatment sample preparation is needed for assemblies obtained by (wet) colloid chemistry methods, including solvent evaporation and deposition on a solid TEM support. As a result of this procedure, changes are consistently imposed on the actual nanoparticle organization. Therefore, we propose herein the application of electron tomography of nanoparticle assemblies while in their original colloidal liquid environment. To address the challenges related to electron tomography in liquid, we devised a method that combines fast data acquisition in a commercial liquid-TEM cell, with a dedicated alignment and reconstruction workflow. We present the application of this method to two different systems, which exemplify the difference between conventional and liquid tomography, depending on the nature of the protecting ligands. 3D reconstructions of assemblies comprising polystyrene-capped Au nanoparticles encapsulated in polymeric shells revealed less compact and more distorted configurations for experiments performed in a liquid medium compared to their dried counterparts. On the other hand, quantitative analysis of the surface-to-surface distance of self-assembled Au nanorods in water agrees with previously reported dimensions of the ligand layers surrounding the nanorods, which are in much closer contact when in similar but dried assemblies. This study, therefore, emphasizes the importance of developing high-resolution characterization tools that preserve the native environment of colloidal nanostructures.

cond-mat.soft↗

arXiv2022

Mapping Structural Heterogeneity at the Nanoscale with Scanning Nano-structure Electron Microscopy (SNEM)

Here we explore the use of scanning electron diffraction coupled with electron atomic pair distribution function analysis (ePDF) to understand the local order as a function of position in a complex multicomponent system, a hot rolled, Ni-encapsulated, Zr$_{65}$Cu$_{17.5}$Ni$_{10}$Al$_{7.5}$ bulk metallic glass (BMG), with a spatial resolution of 3 nm. We show that it is possible to gain insight into the chemistry and chemical clustering/ordering tendency in different regions of the sample, including in the vicinity of nano-scale crystallites that are identified from virtual dark field images and in heavily deformed regions at the edge of the BMG. In addition to simpler analysis, unsupervised machine learning was used to extract partial PDFs from the material, modeled as a quasi-binary alloy, and map them in space. These maps allowed key insights not only into the local average composition, as validated by EELS, but also a unique insight into chemical short-range ordering tendencies in different regions of the sample during formation. The experiments are straightforward and rapid and, unlike spectroscopic measurements, don't require energy filters on the instrument. We spatially map different quantities of interest (QoI's), defined as scalars that can be computed directly from positions and widths of ePDF peaks or parameters refined from fits to the patterns. We developed a flexible and rapid data reduction and analysis software framework that allows experimenters to rapidly explore images of the sample on the basis of different QoI's. The power and flexibility of this approach are explored and described in detail. Because of the fact that we are getting spatially resolved images of the nanoscale structure obtained from ePDFs we call this approach scanning nano-structure electron microscopy (SNEM), and we believe that it will be powerful and useful extension of current 4D-STEM methods.

cond-mat.mtrl-sci↗