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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 307 records · Page 17
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Multiscale modeling of hydrogenolysis of ethane and propane on Ru(0001): Implications for plastics recycling

Plastic waste presents an environmental threat. Chemical recycling via hydrogenolysis can convert plastic waste into waxes, lubricants, and fuels. Among catalysts, Ru stands out for its superior activity and selectivity. The chemistry of light alkane hydrogenolysis can help understanding plastics deconstruction. Here, we perform first-principles calculations, develop descriptor-based relations, and conduct microkinetic modeling and analysis on ethane and propane. Predictions are in excellent agreement with experimental data. We identify a similar cracking pattern for both hydrocarbons entailing a deeply dehydrogenated species with the removal of four hydrogen atoms: CHCH*+* → 2CH* for ethane and CH 3 CCH*+2* → CH 3 C* + CH* for propane. We find that the rate-determining step is the C-C cracking for ethane and the first dehydrogenation from the terminal carbon (CH 3 CH 2 CH 3 *+* → CH 3 CH 2 CH 2 *+H*) for propane. The vinyl species CH 2 CH* produced from propane cracking is responsible for whether a single or multiple cracking events occur and effectively controls the selectivity. Specifically, ethane formation in propane hydrogenolysis is suppressed at elevated temperatures due to over-cracking via multiple (two here) cracking events being preferred over hydrogenation and desorption of ethane from the catalyst. Our workflow and models provide a baseline for future studies on heavier hydrocarbons. Insights into recent experimental studies of polyethylene over Ru-based catalysts are discussed.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

A general strategy and a consolidated mechanism for low-methane hydrogenolysis of polyethylene over ruthenium

Polyethylene (PE) is one of the most environment-threatening plastic waste. Its Ru-catalyzed hydrogenolysis is rapid but produces too much methane. Here, in this study, low-density PE hydrogenolysis is performed at mild conditions and short times over Ru-doped zirconia catalysts (Ru-XZr, X = Ti, Nb, Ce, W, V, Mo, Fe) to determine low-methane catalysts. Methane is produced via direct terminal C-C scission and surface cascade of consecutive C-C scissions, with the latter sensitive to hydrogen availability and dominant in hydrogen-lean conditions. Reactivity studies, characterization and theory reveal that the most effective dopant oxides (W, V, and Mo) are intermediately reducible, as they store and supply extra hydrogen to Ru via reverse hydrogen spillover. This hydrogen readily hydrogenates and desorbs long alkyl surface intermediates that would otherwise produce cascade methane. Our proposed mechanism reconciles the low methane production achieved by increasing the hydrogen pressure or reducing the particle size and exposes the key selectivity descriptors.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Machine learning informed rational design of high entropy double perovskite oxide universal air/steam electrodes for solid oxide electrochemical cells

Due to their high efficiency and versatility, solid oxide electrochemical cells (SOCs) are poised to play a significant role in future energy conversion and storage applications. In recent years, SOCs have bifurcated into two distinct categories: traditional oxygen-ion conducting SOCs that typically operate from ∼650—850 °C and the more recent proton-conducting ceramic (PCC) SOCs that typically operate from ∼400—650 °C. Current performance and lifetime of both oxygen-ion conducting SOCs and PCCs is primarily limited by the air/steam electrode, which facilitates the oxygen reduction reaction (ORR) during fuel cell operation and must also facilitate the oxygen evolution reaction (OER) during electrolysis operation. Here, we present a newly designed high-entropy double perovskite oxide suitable as a universal ORR/OER electrode for both oxygen-ion conducting SOCs and PCCs. Machine learning methods are applied to identify chemical descriptors for highly catalytic high-entropy double perovskite oxides (AA’B 2 O 6 ) across a large compositional space. Based on the machine-learning guidance, we ultimately converge on Ba 0.9 Cs 0.1 (Ca 0.2 Gd 0.2 La 0.2 Pr 0.2 Sr 0.2 )Co 1.5 Fe 0.5 O 6 (CsBaHEO) as a universal air/steam electrode. Structure stabilization is accomplished by an equimolar five-cation high-entropy composition on the A’-site, while cesium substitution on the A-site enhances the electrical conductivity and leads to a higher oxygen vacancy concentration. This material exhibits versatility and high performance in reversible oxygen-ion SOCs, reversible PCCs, and also large-scale tubular PCCs. For example, the CsBaHEO-based PCC reaches 1018 mW∙cm −2 at 600°C, while a large-scale tubular PCC using CsBaHEO for electrolysis achieves a hydrogen production rate of 21.314 ML∙min −1 at 600 °C.

Cell↗
DOE OSTI2022

Modification of LiMn 2 O 4 surfaces by controlling the Acid–Base surface chemistry of atomic layer deposition

In this combined theoretical and experimental study, we report the effects of Al precursor selection on the growth chemistry, interfacial structure, and electrochemistry of Al 2 O 3 coatings on spinel LiMn 2 O 4 (LMO) surfaces by atomic layer deposition (ALD). Five Al precursors, exhibiting a range of Lewis acid-base properties, were used to establish trends in the ALD Al 2 O 3 growth chemistry on LMO powders in comparison to redox-inactive planar silicon substrates. We show that the Lewis acid-base properties of the Al precursor ligands can be used to tune both the Mn oxidation states and the Al 2 O 3 coverage on the LMO surface. Density functional theory calculations are used to examine the reaction mechanism of each precursor on LMO surfaces, elucidating a correlation between the Lewis acidity of the ligands and the decomposition thermochemistry. In-depth X-ray photoelectron spectroscopy and in situ Fourier transform infrared spectroscopy measurements support these theoretical predictions and further reveal how different Al precursors modify the atomic and electronic structure near the LMO surface during ALD. While the Mn oxidation state is strongly influenced by the Lewis acidity of the precursor ligand, the surface coverage and thickness of the Al 2 O 3 coating are a more representative descriptor of the electrochemical performance measured in coin cell experiments. Finally, we show how the ligand acid-base properties of ALD precursors can be used to rationally tailor atomic layer growth mechanisms, which may enable atomic level control over the structure of functionalized interfaces for various applications related to catalysis, semiconductors, and energy storage.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Spatiotemporal 4D Whole-cell Modeling of a Minimal Autotroph Reveals Central Carbon Metabolism Regulated Locally by Protein Megacomplexes via Post-translational Modifications under Light Disturbance

Photosynthetic microorganisms rely on multiple pathways in central carbon metabolism to adapt to fluctuating light and energy availability across diel cycles. Mechanistic insight into the regulatory dynamics of this adaptation requires integrating processes spanning disparate timescales, from rapid redox-dependent post-translational modifications (PTMs) to slower changes in protein expression and metabolic pathway usage. To address this complexity beyond genome-based inference and traditional modeling, we develop a whole-cell four-dimensional (3D + time) model of the marine cyanobacterium Prochlorococcus marinus MED4 that explicitly represents the spatial organization of enzymatic and molecular processes in central carbon metabolism under light perturbation. We employ a perturbation-based research design to experimentally generate time-series, multi-omics measurements that provide molecular descriptors and cryo-ET derived 3D segmented volumes as constraints for this dynamic 4D framework. The integration of experiments and modeling across defined light regimes enables quantitative validation of system-level responses and forecasting under distinct light disturbances. We test the hypothesis that light-dependent redox PTMs regulating the structural assembly of a protein megacomplex, the “dark complex,” modulate metabolic flux at a conserved regulatory node of the Calvin–Benson cycle (CBC) in cyanobacteria. Our model shows that subcellular spatial organization buffers rapid light-induced changes in thylakoid reaction rates, which are followed by redox-PTM-mediated sequestration or release of CBC enzymes in the dark complex, ultimately impacting carbon fixation dynamics within carboxysomes. Comparison with an equivalently parameterized well-mixed stochastic model demonstrates that post-translational regulation not only buffers transcriptional noise and diffusion-driven fluctuations but also stabilizes phenotypic outcomes, underscoring the importance of spatial heterogeneity in phenotypic robustness. This ability to probe adaptive, spatiotemporally resolved mechanisms in photosynthetic machinery and central carbon metabolism addresses a critical gap in genotype-to-phenotype inference and expands modeling and design capabilities for understudied or genetically intractable autotrophs such as P. marinus MED4.

Johnson, Connah G.↗
DOE OSTI2023

A review of in situ/operando studies of heterogeneous catalytic hydrogenation of CO 2 to methanol

Repurposing CO 2 into chemicals, one of the Carbon Dioxide Removal (CDR) strategies, still faces significant challenges in conversion and energy efficiency due to the lack of effective catalysts and processes. Fundamental understanding through in situ/operando investigations of the reaction mechanisms and catalyst structures is pivotal for developing the efficient catalysts. This paper reviews the past and recent in situ/operando studies of methanol synthesis from heterogeneous CO 2 hydrogenation over a few typical catalysts including Cu-based, oxide-based, and noble-metal-based catalysts. With the development of high-pressure reactors, in situ/operando IR, X-ray absorption spectroscopy (XAS), X-ray diffraction (XRD), neutron diffraction and imaging have been used to reveal the surface intermediates and structures of working catalysts under CO 2 hydrogenation conditions. On the one hand, the combined operando techniques shed light on working mechanisms for some catalytic systems. On the other hand, due to the complexity of selective CO 2 hydrogenation reaction and limited sensitivity of current accessible operando techniques to the surface structure of catalysts, it is still murky about the exact nature of the active sites at the surface/interface and how different sites promote different reaction paths. Furthermore, it is concluded that new methodologies for differentiating signals from different sites, and the development of surface-sensitive techniques for high pressure reactions are needed for providing structural descriptors of highly active and selective CO 2 conversion catalysts.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Toward Rational Design of Supported Vanadia Catalysts of Lignin Conversion to Phenol

In sustainable chemical engineering, catalytic upgrading of lignocellulosic biomass has recently gained attention for producing renewable platform chemicals. To achieve maximal biomass utilization, upgrading the underutilized lignin components is essential. Among various catalysts for lignin upgrading, supported vanadia (V2O5) catalysts are promising because of their cost-effectiveness and tunability of either dopant metals or catalyst supports. Here, computational studies are conducted to derive rational design guidelines of supported V2O5 catalysts for accomplishing the high catalytic activity of lignin upgrading to phenol, a key compound for producing bioplastics and biofuel blendstocks. Guaiacol was used as the model compound since it comprises the highest portion of depolymerized lignin. Computational mechanistic studies for the catalytic guaiacol conversion to phenol were performed for the V2O5 catalysts on Titania (TiO2) and silica (SiO2) to explain higher experimental phenol yields on V2O5/SiO2 than V2O5/TiO2. The hydrogen migration from the methoxy group to the aryl ring was identified as a rate-determining step, and the overall activation energies on the two catalysts were compared. A structural analysis was carried out for the catalysts and rate-determining transition states to gain further insights from mechanistic studies. It was concluded that the tilt angle of the aryl group in the hydrogen migration transition state is a key descriptor determining the catalytic activity of phenol formation. These features correlate well with activation energies and experimental phenol yields, indicating that they provide design guidelines for supported metal catalysts for lignin upgrading before experiments.

BIOMASS FUELS,INORGANIC, ORGANIC, PHYSICAL, AND AN↗
DOE OSTI2022

Prediction of local concentration fields in porous media with chemical reaction using a multi scale convolutional neural network

The study of solute transport in porous media is of interest in many chemical engineering systems. Some example applications include packed bed catalytic reactors, filtration devices, and batteries. The pore scale modeling of these systems is time consuming and may require large computing resources, for this reason computational fluid dynamics (CFD) simulations are not practical if a large number of simulations is required, like in multiscale modeling, where a model at a large scale calls for pore scale simulations. It has been shown that neural networks can be trained with a dataset of flow simulations and then predict fields orders of magnitude faster, and with less computational resources, in new domains. However, it is crucial to provide the neural network with an effective description of the domain and the undergoing operating conditions to be able to train models that generalize accurately in unseen samples. Therefore, research is needed to employ neural networks in new complex systems. The appropriate training of a network for predicting coupled flow and solute transport processes is an outstanding problem due to the complex interplay between geometry and operating conditions. In this work, we train a multi scale convolutional neural network (MSNet) with a diverse dataset of simulations of transport and chemical reaction in porous media to predict the local concentration fields in images of porous media. Our dataset contains a wide diversity of sphere pack arrangements under different operating conditions (Péclet and Reynolds numbers). Further, we train a robust model by employing different input descriptors that represent the medium and the different operating conditions of each system. Our trained model is able to provide nearly instantaneous predictions, compared to around twenty hours of the CFD workflow, with less than 3.5% error on new geometries and transport conditions. Thus the model could be easily integrated in a multiscale workflow where fast response is needed.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

High-throughput measurements of CO 2 permeance and solubility in ionic liquid reveal a synergistic role of ionic interactions and void fractions

The factors that govern CO 2 solubility in ionic liquids (ILs) are of great interest for the development of new materials for CO 2 capture and utilization. The cationic functional group (i.e., imidazolium, pyrrolidinium, pyridinium, etc.), alkyl chain length of cation, degree of fluorination of anion, anion size, and the void fraction in IL are known to influence CO 2 solubility. However, a comprehensive explanation of how these factors collectively affect CO 2 solubility has not been developed yet. This knowledge gap is largely attributed to the lack of CO 2 solubility data for IL structures other than imidazolium based ILs. We report here an automated high-throughput (HT) setup for the measurement of CO 2 solubility in room-temperature ILs (RTILs) combining six different anions and nine different cations for a total of 19 different specific ranges of RTILs. The HT setup first dispenses up to 200 µL of RTILs in a 96-well microtiter plate and then utilizes a robotic arm to measure cyclic voltammogram (CV) in each well using maneuverable Ag electrodes. The Cottrell analysis of the CO 2 reduction CV peak provides a direct measurement of CO 2 permeance in RTILs, which yields Henry’s constant from the estimated diffusion coefficient of CO 2 . Henry’s constants thus obtained are in very good agreement with those reported earlier. The measured CO 2 permeance and Henry’s constant of all RTILs seem to follow a first-order dependence on void fraction and a second-order dependence on electrostatic interaction between anion and cation of IL, with some synergistic dependence on the product of a void fraction and electrostatic interaction, making them two important descriptors for the design of novel ILs.

CO2 Solubility↗
DOE OSTI2022

Quantifying the anion effect of gas solubility within ionic liquids using the solvation affinity index

The solubility of gas molecules within ionic liquids (ILs) is strongly dictated by the anion effect and the free volume effect, and of these two influences, the anion effect can be challenging to predict. We evaluate a wide range of different gas solutes (O 2 , NH 3 , CH 4 , H 2 S, SO 2 , Ar, H 2 , N 2 , CO 2 ) in different ILs, and we find that the solubility is strongly correlated to one of three different IL properties, which can be directly calculated from the anion electrostatic potential (ESP) surfaces. Thus, the solutes can be grouped into three fundamental types, leading to a general descriptor, the solvation affinity index (SAI), which can identify the affinity between a wide range of gas solutes and IL solvents. The SAI of nine common industrial gases with 47 common anions are generated. Furthermore, this can provide a fast approach for pre-screening IL solvents or even for ILs embedded within porous supports.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Medium-independent hydrogen atom binding isotherms of nickel oxide electrodes

Adsorption and transfer of hydrogen atoms at solid/solution interfaces are fundamental to heterogeneous catalysis for chemical energy transformations and other processes. Reported here are electrochemical and spectroelectrochemical measurements of the thermodynamics of H-atom binding to nickel oxide electrodes, both the average and the distribution of NiO–H bond dissociation free energies (BDFEs). These are perhaps the first measurements of binding isotherms at non-metal electrodes. Remarkably, both the BDFEs and the non-Langmuirian isotherms are the same in water, acetonitrile, and dimethylformamide, and with different buffers and proton activities. Such medium independence of the BDFEs and isotherms has not been previously reported for any binary material. The medium independence supports the common use of computed hydrogen binding energies as intrinsic descriptors of surface reactivity, while the broadened isotherms add a level of complexity to such analyses. Furthermore, this work demonstrates the capability to derive key thermodynamic parameters at chemically reactive solid-liquid interfaces.

13 HYDRO ENERGY↗
DOE OSTI2024

Anion-derived contact ion pairing as a unifying principle for electrolyte design

Enabling new electrochemical technologies requires systems that can operate under ever-more demanding conditions, and progress in energy storage applications reveals tantalizing opportunities to reimagine electrolyte design for performance at extreme potentials. Here, a common thread among these innovations is the formation of significant populations of contact ion pairs (CIPs) in the electrolyte, regardless of the specific cation chemistry or solvent system. The examples summarized in this review suggest that a set of general electrolyte design rules likely exists, where the purposeful selection of anion chemistry can yield CIP structures with tunable control over reaction thermodynamics, kinetics, and interphase chemistry. Identifying the relevant descriptors for high-performance, anion-derived CIP structures can be achieved utilizing a combined experimental and computational approach, aided by machine learning and artificial intelligence, to more rapidly survey the vast combinatorial space available and to enable a new generation of electrolytes for decarbonized electrochemical processes at scale.

electrochemistry↗
DOE OSTI2023

Hierarchical reconstruction of 3D well-connected porous media from 2D exemplars using statistics-informed neural network

The relationships between porous microstructures and transport properties are of fundamental importance in various scientific and engineering applications. Due to the intricacy, stochasticity and heterogeneity of porous media, reliable characterization and modeling of transport properties often require a complete dataset of internal microstructure samples. However, it is often an unbearable cost to acquire sufficient 3D digital microstructures by purely using microscopic imaging systems. Herein this paper presents a machine learning-based technique to hierarchically reconstruct 3D well-connected porous microstructures from one isotropic or several anisotropic low-cost 2D exemplar(s). To compactly characterize the large-scale microstructural features, a Gaussian image pyramid is built for each 2D exemplar. Local morphology patterns are collected from the Gaussian image pyramids, and then they serve as the training data to embed the 2D morphological statistics into feed-forward neural networks at multiple length levels. By using a specially-developed morphology integration scheme, the 3D morphological statistics at different levels can be inferred from the statistics-informed neural networks. Gibbs sampling is adopted to hierarchically reconstruct 3D microstructures by using multi-level 3D morphological statistics, where the large-scale, regional and local morphological patterns are statistically generated and successively added to the same 3D random field. The proposed method is tested on a series of porous media with distinct morphologies, and the statistical equivalence between the reconstructed and the real microstructures is systematically evaluated by comparing morphological descriptors and transport properties. The results demonstrate that the proposed 2D-to-3D microstructure reconstruction method is a universal and efficient approach to generating morphologically and physically realistic samples of porous media.

42 ENGINEERING↗
DOE OSTI2023

Operando methods: A new era of electrochemistry

One of the grand challenges facing electrochemistry is to directly resolve the complex nature of (electro)catalyst active sites and capture real-time “movies” of reaction dynamics, i.e. “watching chemistry in action”. The need for such fundamental understanding has stimulated the development of operando/in situ methods, which have greatly enhanced our ability to identify activity descriptors of electrocatalysts and establish structure–property relationships of energy materials. Here, this review summarizes the frontiers of operando electrochemical liquid-cell scanning transmission electron microscopy and correlative synchrotron X-ray methods, which are complementary tools to comprehensively investigate reaction dynamics across multiple spatiotemporal scales. In an effort to encourage greater adoption of advanced operando methods by the general electrochemistry community, this review points out the need to benchmark electrochemistry in confined and heterogenous liquid environment with minimal beam-induced damage. We anticipate that multimodal operando methods will become indispensable for understanding interfacial reaction mechanisms for the broad chemistry and energy materials communities.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

A machine learning approach to predict thermal expansion of complex oxides

Although it is of scientific and practical importance, the state-of-the-art of predicting the thermal expansion of oxides over broad temperature and composition ranges by physics-based atomistic simulations is currently limited to qualitative agreements. We present an emerging machine learning (ML) approach to accurately predict the thermal expansion of cubic oxides with a dataset consisting of experimentally measured lattice parameters while using the metal cation polyhedron and temperature as descriptors. High-fidelity ML models that can accurately predict temperature- and composition-dependent lattice parameters of cubic oxides with isotropic thermal expansions have been successfully trained. The ML-predicted thermal expansions of oxides not included in the training dataset have shown good agreement with available experiments. The limitations of the current approach and challenges to go beyond cubic oxides with isotropic thermal expansion are also briefly discussed.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Simulation and analysis of small angle scattering (SAS) patterns of Ni-based superalloy microstructures generated by a phase-field model

This paper investigates the relationship between microstructural features and small-angle scattering (SAS) patterns in Ni-based superalloys using a combined phase-field and SAS simulation approach coupled with microstructure analyses. The simulated SAS patterns accurately capture key experimental observations previously reported in the literature, including the time-dependent transition from circular to square-shaped precipitates and the development of anisotropic SAS patterns. Importantly, our analysis reveals the correlations between characteristic length scales extracted from SAS profiles and microstructural descriptors, such as precipitate size and inter-precipitate distance. These findings provide a comprehensive understanding of the link between SAS profiles and microstructure evolution in Ni-based superalloys, offering valuable insights for materials characterization and design.

Microstructure↗
DOE OSTI2025

Understanding and design of interstitial oxygen conductors

Highly efficient oxygen-active materials that react with, absorb, and transport oxygen is essential for fuel cells, electrolyzers and related applications. While vacancy-mediated oxygen-ion conductors have long been the focus of research, they are limited by high migration barriers at intermediate temperatures (400–600 °C), which hinder their practical applications. In contrast, interstitial oxygen conductors exhibit significantly lower migration barriers enabling higher ionic conductivity at lower temperatures. This review systematically examines both well-established and recently identified families of interstitial oxygen-ion conductors, focusing on how their unique structural motifs such as corner-sharing polyhedral frameworks, isolated polyhedral, and cage-like architectures, facilitate low migration barriers through interstitial and/or interstitialcy diffusion mechanisms. A central discussion of this review focuses on the evolution of design strategies, from targeted donor doping, element screening, to physical-intuition descriptor material screening and machine learning approach, which leverage computational tools to explore vast chemical spaces in search for new interstitial conductors. The success of these strategies demonstrates that a significant, largely unexplored space remains for discovering high-performing interstitial oxygen conductors. Crucial features enabling high-performance interstitial oxygen diffusion include the availability of electrons for oxygen reduction and sufficient structural flexibility with accessible volume for interstitial accommodation and migration. This review concludes with a forward-looking perspective, proposing a knowledge-driven methodology that integrates current understanding with data-centric approaches to identify promising interstitial oxygen conductors outside traditional search paradigms. These approaches are expected to significantly accelerate the development of high-performance interstitial oxygen conductors for a variety of oxygen-active applications, ultimately paving the way for more efficient and sustainable energy technologies.

Interstitial oxygen conductors↗
DOE OSTI2026

NanoPSD: A software for automatic detection of Nano-Particle Shape Distribution in electron microscopy images

Accurate quantification of the size and morphology of nanoparticles from electron microscopy (EM) images is essential to understand growth mechanisms, surface reactivity, and functional behavior in nanoscale materials. Manual analysis remains slow, subjective, and difficult to reproduce in large datasets. We introduce NanoPSD (Nano-Particle Shape Distribution), an open-source and fully automated framework for quantitative particle detection and morphology analysis from EM images. NanoPSD integrates adaptive contrast enhancement, polarity-agnostic scale-bar detection, Optical Character Recognition (OCR)-based calibration, and classical segmentation via Otsu thresholding with morphological refinement. Particle contours are used to extract geometric descriptors, including equivalent circular diameter, aspect ratio, circularity, and solidity, enabling automated classification into spherical, rod-like, and aggregate morphologies. The framework supports both single-image and batch processing, generating publication-quality visualizations, LaTeX-ready tables, and structured comma-separated values (CSV) datasets. As a demonstration, we applied NanoPSD to plasma-synthesized nanoparticle samples diagnosed via transmission electron microscopy (TEM). The code produced statistically robust size and morphology distributions spanning a few to tens of nanometers with minimal user supervision. The pipeline demonstrates high reproducibility and scalability, processing large image collections with consistent calibration and output formatting. Its modular design enables seamless integration of future deep-learning-based segmentation models, providing a pathway toward intelligent, data-driven electron microscopy analysis.

36 MATERIALS SCIENCE↗