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At least 505 records · Page 28

Time-Dependent Corrosion Behavior of Aluminum Using Advanced Electrochemical and Characterization Techniques

The time-dependent corrosion behavior of pure aluminum (Al) in a chloride-containing environment was investigated using various electrochemical and characterization techniques for up to 336 h. Transmission electron microscopic and secondary ion mass spectroscopic analysis revealed the continuous dissolution of the surface film over the immersion time. In the meantime, the increasing passive oxide thickness resulted in the surface film resistance enhancement over the immersion time, as indicated by the electrochemical impedance spectroscopic analysis. The electrochemical noise measurements showed an increase in the corrosion kinetics with immersion time until 60 h because of the accelerated localized corrosion in the early stage of immersion. However, an inhibition in corrosion kinetics occurred after longer immersion times due to corrosion product deposition inside the pit.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Conditional Latent Diffusion for High-Resolution Prediction of Electrochemical Surface Morphology

A conditionally guided generative latent diffusion process that is trained on a set of experimental processing parameters and their associated resulting electron microscope images of the electrodeposition process is able to interpolate between processing parameters in a physically consistent way. Electrodeposition of rhenium with pulse and pulse-reverse waveforms is used as a model system, and the process is adaptable to other electrodeposition, electropolishing, or corrosion processes. The method is able to extrapolate, predicting estimates of material morphologies for experimental setups unseen in the training data. The results are demonstrated with experimental data.

36 MATERIALS SCIENCE↗

Role of Oxygen on Chemical Segregation in Uncapped Ge 2 Sb 2 Te 5 Thin Films on Silicon Nitride

Germanium antimony telluride has been the most used and studied phase-change material for electronic memory due to its suitable crystallization temperature, amorphous to crystalline resistance contrast, and stability of the amorphous phase. In this paper, the segregation of Ge in a Ge 2 Sb 2 Te 5 film of 30 nm thickness during heating inside the transmission electron microscope was observed and characterized. Furthermore, Ge 2 Sb 2 Te 5 film was deposited using sputtering on a Protochips Fusion holder and left uncapped in atmosphere for about four months. Oxygen incorporated within the film played a significant role in the chemical segregation observed which resulted in amorphous Ge-O island boundaries and Sb and Te rich crystalline domains. Such composition changes can occur when the phase-change material interfaces insulating oxide layers in an integrated device and can significantly impact its electrical and thermal properties.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Microstructural Study of Hydration of C 3 S in the Presence of Calcium Nitrate Using Scanning Transmission X-Ray Microscopy (STXM)

Calcium nitrate (CN) is used widely as an effectively inorganic setting accelerator and antifreeze admixture in concrete structures. In this paper, the multiscale investigation of CN on the hydration of C 3 S was studied by scanning transmission X-ray microscopy (STXM) combined with near-edge X-ray absorption fine structure (NEXAFS), 29 Si MAS NMR, calorimetry, scanning electron microscope, and N 2 absorption. It was concluded that the calcium silicate hydrate (C-S-H) surrounds the unhydrated C 3 S at 1-day hydrated C 3 S in the presence of calcium nitrate, while portlandite is partly in transformation and is formed partly. Based on Ca L 3 , 2 -edge NEXAFS spectra for 1-day hydrated C 3 S particle, calcium nitrate does not change the structure of the asymmetrically 7-fold coordination of calcium in the C-S-H. Calcium nitrate can accelerate the hydration of C 3 S to some extent and polymerization of the silicate chains within C-S-H considerably at early age, resulting in the increasing specific surface area.

Li, Qinfei↗

EDS Montage

Applies machine learning to analyze individual or sets of X-ray energy dispersive spectroscopy spectrum images acquired in the electron microscope.

Parish, ChadM↗

Microscopy Parser

Reads proprietary electron microscope vendor files and exports them in a standardized open source HDF5 format.

Simpson, Joseph↗

pnnl/Multimodal-Few-Shot

This project combines few-shot machine learning with a multimodal framework for electron microscope data quantification.

Doty, Christina↗

WizEM

The software provides a flexible pipeline for artificial intelligence (AI) assisted analysis of data collected from an electron microscope. This tool distinctly provides near real-time image quantification results during data collection, incorporating feedback from a user to tune performance.

Akers, Sarah [Pacific Northwest National Laborator↗

2D Polyhedral Template Matching for Atomic Resolution Microscopy

SAND2024-13879O The 2D Polyhedral Template Matching for Atomic Resolution Microscopy is a suite of functions for analyzing atomic resolution electron microscopy images using the 2D polyhedral template matching (2D-PTM) method. This software analyzes atomic resolution microscopy data from electron microscopic imaging or scanning probe microscopies. The primary application is for identifying different crystal phases, crystal orientations, and defect structures obtained in such atomic resolution images. Written in MATLAB and starting from an atomic resolution image, the code identifies the positions of atomic intensity peaks. It then matches predefined structural templates to the local atomic environments. Outputs include the local structural identification, the template scaling factor and rotation angle, root-mean-squared deviation (RMSD), and centrosymmetry parameter.

Medlin, Douglas↗

pyTriBeam

SAND2025-01899O pyTriBeam is a software tool that creates automated processes for a scanning electron microscope including workflows for 3D serial sectioning dataset collection, high-res image montaging, and support for custom script use. This includes integration for 3D chemical mapping (EDS) and crystallographic (EBSD) data collection with select supported detectors. The application allows end users to setup and run customizable data collection workflows without requiring expertise in programming. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

Hovey, Chad↗

Multiscale characterization and representation of variability in ceramic matrix composites

Low density, high strength, and high creep and oxidation resistance properties of ceramic matrix composites (CMCs) make them an ideal choice for use in extreme environments in space and military applications. This paper presents a detailed characterization study of structural and manufacturing flaws in Carbon fiber Silicon-Carbide-Nitride matrix (C/SiNC) CMCs at different length-scales. Energy-dispersive spectroscopy (EDS) is used for the chemical characterization of the material’s elemental constituents. High-resolution multiscale graphs obtained from scanning electron microscope (SEM) and confocal laser scanning microscope (LSM) are used to characterize the distribution and morphology of defects at different length scales. This is followed by the classification and quantification of the common manufacturing defects. An image processing algorithm based on the image segmentation process is developed to quantify the variability of various scale-dependent architectural parameters. Finally, a three-dimensional stochastic representative volume element (SRVE) generation algorithm is developed to provide precise representations of material textures at multiple length scales. The developed algorithm accurately accounts for material features and flaws based on a range of multiscale structural and defects characterization results.

36 MATERIALS SCIENCE↗

Epoxy resin reinforced with carbonized chicken feathers: An innovative composite material with sustainable potentials

Here, this article explores a novel method for enhancing the mechanical properties of epoxy resin composites by incorporating carbonized chicken feathers as a filler material. The fabrication process involves carbonizing chicken feathers at 600°C and incorporating 5-10 wt% of the fillers into an epoxy matrix. The composites showed enhanced mechanical properties and samples containing 10 wt% filler exhibit the best properties. The performance corresponds to 49% increase in tensile strength, 16% rise in Young’s modulus, 40% improvement in flexural modulus, and 57% in flexural strength. X-ray diffraction and scanning electron microscopy with energy dispersive spectroscopy were employed to characterize the filler. This characterization provides valuable insights into the structure and chemical composition of the pulverized carbonized chicken feathers that contributed to the attained improvement in composites’ properties. Microstructural examination of the developed composite under scanning electron microscope also provides insights into matrix-filler interface and dispersion of the fillers within the composite matrix. The study not only highlights the unique combination of carbonized feathers’ inherent strength and compatibility with the epoxy matrix but also underscores the eco-friendly nature of utilizing agricultural waste. The findings suggest promising applications in industries demanding lightweight, high-strength materials, which can contribute to sustainable engineering solutions.

36 MATERIALS SCIENCE↗

Highly Catalytically Active High-spin Single-atom Iron Catalyst Supported by Catechol-containing Microporous 2D Polymer

Traditionally, Fe-SACs are prepared through energy-intense processes, which often lead to the loss of precision in structural features from the starting substrates and impeding rational design. In this work, we have described the synthesis of a unique catechol-containing porous polymer with designed features in the substrates maintained, affording atomically dispersed iron catalyst (Fe-SAC) through treatment of ferrous chloride (FeCl 2 ). An aberration-corrected scanning transmission electron microscope (AC-STEM) and synchrotron X-ray absorption spectroscopy (XAS) were employed to shed light on the local coordination geometry of the atomically dispersed iron catalyst. The resulting Fe-SAC exhibits excellent catalytic performance in reduction of nitroaromatics with highest molar K app among all Fe based catalysts.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Additive Manufacturing of Controlled Porous Elastomeric Nanocomposites for Enhanced Sensing Function

Sensors manufactured using piezoresistive elastomeric nanocomposites have a wide range of applications in many fields such as structural health monitoring, robotics, and biomedical industries. Additive manufacturing of these sensors, achieved using Direct Ink Writing (DIW), has provided numerous advantages including increased sensitivity and the fabrication of complex geometry. However, solid sensors manufactured either additively or using conventional methods, display a greater level of hysteresis, especially at strains higher than 10%, which limits their sensitivity. The solid sensors also display a greater level of piezoresistive relaxation where the resistance change with time either in a static or dynamic loading environment. Porosity can be introduced in the form of varying infill patterns and densities to reduce bulk material effects. In this paper, a lattice structure with four infill densities are investigated, and the samples are printed using a grid infill pattern. The fabricated samples are characterized using a scanning electron microscope (SEM) to validate the microstructural features and layer bonding. The pressure-sensing capability of each sensor is investigated using cyclic compression loading at various strain levels. Sensing results show that an increase in sensitivity, as well as a decrease in hysteresis with an increase in porosity, compared to solid samples (100% in-fill). The highest porosity was measured to be 62.3% with a corresponding 112% increase in the gauge factor compared to the solid bulk. These results indicate that introducing porosity using DIW is a sensible strategy to improve the piezoresistive performance of nanocomposites and to allow for the tunability of sensing capacity in pressure sensors

Sukati, Musa↗

Deep Neural Network Algorithm for CMC Microstructure Characterization and Variability Quantification

Microstructure characterization and variability quantification are crucial for understanding ceramic matrix composites (CMCs) mechanical behavior and deformation mechanisms across length scales. Traditionally, analyses of the micrographs obtained from microscopy are labor-intensive. However, with the vast improvement in computer vision (CV) and deep learning (DL), an automated algorithm can be designed to extract essential microstructure variability from micrographs which can then be used to construct a statistically representative volume element (SRVE). The DL-based algorithm spans the taxonomy of microstructure analyses, including semantic segmentation of microstructure constituents, secondary phases, matrix/fiber interface, and defects, and quantifying the microstructure variability in terms of probability distributions. In this work, C/SiNC and SiC/SiNC CMCs microstructures are semantically segmented through a deep convolutional neural network, followed by variability quantification through the implementation of a fully connected regression layer, hence forming a deep regression network. The deep regression network operates in a feedforward regime, in which the neuron output signal traverses through the network in a unidirectional manner. The weight tensor associated with each layer is updated through a backpropagation stochastic gradient descent approach. The input gray-scale image obtained through in-house scanning electron microscope and confocal microscope micrographs is augmented through affine transformations to increase the training set size, which is then processed through four strided convolutional layers. This compresses the image resolution by half at each layer while increasing the image depth by applying different filters (image encoding). The class activation maps (CAMs) corresponding to the applied filters highlight the key architectural features and assist with the semantic segmentation of the microstructure.

Hamza, Mohamed H.↗

Wafer-scale waveguide sidewall roughness scattering loss characterization by image processing

Photonic integrated circuits (PICs) are vital for developing affordable, high-performance optoelectronic devices that can be manufactured at an industrial scale, driving innovation and efficiency in various applications. Optical loss of modes in thin film waveguides and devices is a critical measure of their performance. Thin film growth, lithography, masking, and etching processes are imperfect processes that introduce significant sidewall and top-surface roughness and cause dominating optical losses in waveguides and photonic structures. This roughness, as perturbations couple light from guided to far-field radiation modes, leads to scattering losses that can be estimated from theoretical models. Typically, with UV-based lithography, sidewall roughness is significantly larger than wafer-top surface roughness. Atomic force microscopy (AFM) imaging measurement gives a 3D and high-resolution roughness profile, but the measurement is inconvenient, costly, and unscalable for large-scale PICs and at wafer-scale. Here, we evaluate the sidewall roughness profile based on 2D high-resolution scanning electron microscope (SEM) imaging. We characterized the loss on two homemade nitride and oxide films on 3-inch silicon wafers with 12 waveguide devices on each and correlated the scattering loss estimated from a 2D image-based sidewall profile and theoretical Payne model. The lowest loss of guided fundamental transverse electric (TE 0 ) mode is found at 0.075 dB/cm at 633 nm across 24 devices, a record at visible wavelength. Our work shows 100% success (edge continuity span exceeding 95% of image width/height) in edge detection in image processing of all images to estimate autocorrelation function and optical mode loss. These demonstrations offer valuable insights into waveguide sidewall roughness and a comparison of experimental and 2D SEM image processing based loss estimations with applications in loss characterization at wafer-scale PICs.

Khurana, Mohit (ORCID:0000000207898672)↗

Assessment of Different Approaches for Measuring Shear Fracture Appearance in Charpy Tests

In this work, different approaches for establishing shear fracture appearance (SFA) on tested Charpy specimens were assessed and compared, namely (in order of increasing expected accuracy) estimates based on characteristic forces from instrumented Charpy tests, optical measurements using image analysis software, and measurements performed with the aid of a scanning electron microscope. Comparisons between different SFA values for 129 instrumented Charpy tests currently included in the National Institute of Standards and Technology database allowed us to establish the relationships between the different available approaches and formulate recommendations for the revision of the standards ASTM E2298-18, Standard Test Method for Instrumented Impact Testing of Metallic Materials, and ISO 14556:2015, Metallic Materials — Charpy V-Notch Pendulum Impact Test — Instrumented Test Method, which currently provide four empirical correlations that can be used to estimate SFA.

36 MATERIALS SCIENCE↗

Understanding plasticity in irradiated alloys through TEM in situ compression pillar tests

Depth-sensing transmission electron microscopic (TEM) in situ mechanical testing has become widely utilized for understanding deformation in irradiated materials. Until now, compression pillars have primarily been used to study the elastic properties and yield of irradiated materials. In this study, we utilize TEM in situ compression pillars to investigate plastic deformation in two ion-irradiated alloys: Fe–9% Cr oxide dispersion strengthened (ODS) alloy and nanocrystalline Cu–24% Ta. We develop an algorithm to automate the extraction of instantaneous pillar dimensions from TEM videos, which we use to calculate true stress–strain curves and strain hardening exponents. True stress–strain curves reveal intermitted plastic flow in all specimen conditions. In the Fe–9% Cr ODS, intermitted plastic flow is linked to strain bursts observed in TEM videos. Low strain hardening or strain softening is observed in all specimen conditions. TEM videos link the strain softening in irradiated Fe–9% Cr ODS to dislocation cross-slip, and in Cu–24% Ta to grain boundary sliding.

36 MATERIALS SCIENCE↗