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At least 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Strategies for preparing and analyzing thin passive films with atom probe tomography

Atom probe tomography provides a unique, three-dimensional map of elemental and isotopic distributions over a wide range of materials with near-atomic scale resolution and is particularly strong at analyzing buried interfaces within materials. However, it is much more difficult to apply atom probe to the analysis of nanoscale surface films, such as those formed during alloy passivation, where unique challenges persist for sample preparation and data collection. Here, we present sample preparation strategies involving the deposition of a < 100 nm capping layer that enables reliable characterization of thin passive films approximately 2–5 nm thick formed on binary and multi-principal element alloys via atom probe tomography. Several capping layer materials (Pt, Ti, Ni/Cr bi-layer) and deposition methods are contrasted. Our results indicate a sputtered Ni/Cr bi-layer enables the characterization of the entire passive film and concentration profiles that can easily be interpreted to clearly distinguish base alloy/passive film/capping layer interfaces. Lastly, we highlight ongoing challenges and opportunities for this experimental approach.

Kautz, Elizabeth J. [University of Florida]↗
DOE OSTI2025

Enhancing Cluster Identification in Atom Probe Tomography Data Using Transfer Learning

Atom Probe Tomography (APT) is a powerful technique for visualizing the atomic-scale distribution of solutes in materials, but quantitative cluster analysis of APT datasets remains a challenge due to the need for subjective parameter selection in clustering algorithms. While distance-based and density-based methods such as HDBSCAN are widely used, their performance is highly sensitive to user-defined parameters, which undermines reproducibility and accuracy. This study proposes an image-based, deep learning-aided workflow for automating parameter selection and cluster detection in APT data analysis. By projecting 3D APT point clouds onto 2D planes, we leverage pretrained convolutional neural networks (ConvNeXt-Tiny and ResNet-50) through transfer learning to predict the number of clusters present in synthetic datasets. The output is used to guide K-means clustering and estimate HDBSCAN parameters, specifically minimum cluster size and minimum sample points. This approach reduces reliance on manual parameter tuning, improving consistency and scalability. The methodology demonstrates the feasibility of using image-based deep learning for interpreting complex spatial patterns in APT data, enabling faster and more objective analysis. The complete workflow and code are made publicly available to support reproducibility and future research.

Density-based clustering↗
DOE OSTI2025

Enhancing Cluster Identification in Atom Probe Tomography Data Using Transfer Learning

Atom probe tomography (APT) has enabled the direct visualization of solute clusters, providing valuable insights into material structures. This clustering is crucial for understanding the nanoscale composition and behavior of materials, which can significantly influence their mechanical and physical properties. However, the widely used clustering methods in the APT community face challenges such as subjective parametric selection and limited applicability, particularly in dealing with overlapping clusters, nested clusters, and artifacts across different scales, such as precipitates and dislocations. To address these challenges, we present a framework based on density-based cluster analysis that aims to be less dependent on user input, reproducible, and robust.

Density-based clustering↗
DOE OSTI2025

Scanning Transmission Electron Microscopy–Atom Probe Tomography Correlative Analysis for the Characterization of Solute-defect Interactions

Atom probe tomography (APT) and (scanning) transmission electron microscopy ((S)TEM) are complementary techniques that provide spatially resolved chemical and structural information at the atomic scale. Here, in this study, we employ two different STEM/APT correlative analysis methods to investigate Cr segregation at dislocation loops in ultra-high purity Fe–Cr alloys. APT needles for the correlative analysis were extracted either from bulk material or from thinned TEM lamellae. STEM analysis was used to determine the Burgers vectors of ion-irradiation-induced dislocation loops, while APT reconstruction of the same region revealed the Cr segregation to these loops. We extended the g•b = 0 invisibility criterion of dislocation loops from TEM mode in a lamella to STEM mode in a needle-shaped specimen. STEM and APT analysis on the same needle provide straightforward correlative analysis, although it is limited by a small observation volume. In contrast, iterative STEM analysis of TEM lamellae, followed by the selective extraction of specific regions of interest for APT analysis, expands the observation area by up to 100 times but requires additional time-consuming steps for APT needle extraction from the lamellae.

47 OTHER INSTRUMENTATION↗
DOE OSTI2025

Development of Automated Atom Probe Tomography capability to study the influence of applied voltage and laser power on the final apparent composition of the analyzed specimen

This study presents the development and implementation of an autonomous Bayesian optimization (BO) framework for controlling and optimizing experimental parameters in Atom Probe Tomography (APT). Using commercial silicon needle samples as a benchmark system, we demonstrate that BO can efficiently navigate the complex parameter space of voltage and laser power to achieve target charge state ratios (specifically Si + /(Si + +Si 2+ )) with minimal experimental evaluations. Our implementation integrates Gaussian Process modeling with the CAMECA atom probe control framework, enabling autonomous adjustment of experimental conditions in real-time. Results show that the algorithm successfully converges to target ratios under different scenarios: maintaining a reference ratio, increasing the ratio (favoring Si 1+ ), and decreasing the ratio (favoring Si 2+ ). The system adapts to specimen evolution during analysis, compensating for changes in apex geometry while maintaining optimization targets. This work establishes a proof of concept for AI-driven optimization in APT, addressing the traditional challenges of manual parameter tuning and paving the way for applications to more complex materials where compositional accuracy is critical.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Stratification of fluoride uptake among enamel crystals with age elucidated by atom probe tomography

Dental enamel is subjected to a lifetime of de- and re-mineralization cycles in the oral environment, the cumulative effects of which cause embrittlement with age. However, the understanding of atomic scale mechanisms of dental enamel aging is still at its infancy, particularly regarding where compositional differences occur in the hydroxyapatite nanocrystals and what underlying mechanisms might be responsible. Here, we use atom probe tomography to compare enamel from a young (22 years old) and a senior (56 years old) adult donor tooth. Findings reveal that the concentration of fluorine is elevated in the shells of senior nanocrystals relative to young, with less significant differences between the cores or intergranular phases. It is proposed that the embrittlement of enamel is driven, at least in part, by the infusion of fluorine into the nanocrystals and that the principal mechanism is de- and re-mineralization cycles that preferentially erode and rebuild the nanocrystals shells.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Revealing the complex chemistry of grain boundaries in K-doped BaFe 2 As 2 with atom probe tomography

Iron-based superconductors have attractive properties for high-field applications, but there is a lack of understanding of the effect of grain boundary chemistry on the in-field performance. The near atomic-scale resolution, ppm sensitivity and 3D analysis offered by atom probe tomography make it a powerful tool to investigate the nanoscale structure and chemistry of these defects in fine-grained K-doped BaFe 2 As 2 samples. A computational method to systematically extract and compare the Gibbsian interfacial excess of chemical species across grain boundaries has been explored in this work. The robustness of the method has been tested by evaluating the effects of selected variables on simulated APT datasets. The accuracy and precision of the calculated Gibbsian interfacial excess were found to be stable over a range of analysis conditions: varying grain boundary widths and detection efficiencies, spatial precisions below 1.5 nm, and bin widths between 1.2 and 1.6 nm. For the K-doped BaFe 2 As 2 samples studied, segregation of As, Ba, K and impurities of O, Na, and Sb were found at grain boundaries. The Gibbsian excess values were found to vary widely between different boundaries, showing the complexity of the grain boundary chemistry in this material. Possible links between the observed critical current density (Jc) of these samples and their nano- and micro-structure have also been investigated and discussed.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Correlated transmission electron microscopy and atom probe tomography characterization of ion irradiated Ni-based alloy Hastelloy N

Ion irradiation of Hastelloy N was conducted to better characterize the effects of irradiation on Hastelloy N using modern tools compared to studies done in the 1950s. The 2 MeV Ni + ion irradiation at 600 °C of Hastelloy N has been investigated using atom probe tomography, transmission electron microscopy and energy dispersive spectroscopy. Irradiation is found to promote formation of nanoscale M 2 C carbides over the thermodynamically favored M 6 C. Segregation of Si to dislocation loops and grain boundaries was also evident and may have assisted in formation of M 2 C. These microstructural changes result in a 20 % hardness increase caused by irradiation alone. These observations are useful in the design of new materials better suited for the harsh environment of a molten salt reactor.

Atom probe tomography↗
DOE OSTI2025

Precision Local Burnup Assessment Through Dynamic Peak Fitting in Atom Probe Tomography for Depleted, Enriched, and Irradiated Metallic and Ceramic Fuels

Abstract Burnup estimation in nuclear fuels is vital for evaluating fuel performance, transportation, and safe fuel storage. Accurate assessments of burnup from service period and spent fuels involve tracking the consumption of fissile isotopes of uranium (U) offering a direct insight into energy changes within the fuels especially for thermal spectrum reactors. In current approach, mass spectroscopic technique in atom probe tomography (APT) is utilized for accurate quantification of U isotopes. Quantification of U peaks in mass spectrum is performed on asymmetric shapes due to delayed signals, known as thermal tails, particularly for poorly conducting samples analyzed in laser mode. In this study, we introduce a novel quantification tool for isotopic analysis from APT datasets by developing a fitting algorithm based on shapes of the peaks. A MATLAB-based dynamic peak fitting toolbox is developed and designed to adapt to various peak shapes, ensuring accurate quantification of U isotopes. The effectiveness of this approach is demonstrated in standard Ni-Cr sample, depleted and enriched U samples, and U-based fuels with different burnup levels. The viability of this approach for isotopic quantification is demonstrated on both metallic and ceramic fuels.

Burnup↗
DOE OSTI2025

Machine Learning Atom Probe Tomography Tool For Automatic And Fast Clustering

The software uses a YOLO11 segmentation model trained on synthetic data to analyze APT datasets. The workflow operates as follows: 1. Data Slicing: The APT dataset is divided into multiple 2D cross-sections of a specified thickness. 2. Segmentation: The model identifies point-dense regions within each 2D slice. 3. 3D Reconstruction: Detected regions (masks) from all slices are combined and reconstructed back into the original 3D space, forming clusters. The integration with HPC resources enables the software to process large-scale APT datasets efficiently. This combination of automation and scalability reduces manual intervention, improves reproducibility, and accelerates the clustering workflow.

Tang, Yalei [Idaho National Laboratory (INL), Idah↗
DOE OSTI2025

6cr

A series of model Fe–Cr alloys containing 3–18 at.% Cr was neutron irradiated at a nominal temperature of 563 K to 1.82 dpa. Solute distributions were analyzed by atom probe tomography, which revealed α′ precipitation for alloys containing more than 9 at.% Cr. Both the Cr concentration dependence of α′ precipitation and the measured matrix compositions are in agreement with the recently published Fe–Cr phase diagrams. An irradiation-accelerated precipitation process is strongly suggested. Irradiation was carried out in the Advance Test Reactor (ATR) at Idaho National Laboratory. A series of six Fe–Cr alloys of nominal compositions 3, 6, 9, 12, 15 and 18 at.% Cr was irradiated at a neutron fluence (E > 1 MeV) of 1.1 × 1021 n cm−2 at 563 ± 15 K and to a damage level of 1.82 displacements per atom (dpa). Nominal neutron flux and dpa rate are 2.3 × 1014 n cm−2 s−1 and 3.4 × 10−7 dpa s−1, respectively. The microstructures of the Fe–Cr alloys were studied by atom probe tomography (APT) using a Cameca 4000X HR instrument. APT specimens were prepared by a standard lift-out process using a Quanta 3D 200i dual beam scanning electron microscope ensuring that the analyses were performed away from grain boundaries.

Bachhav, Mukesh↗
DOE OSTI2025

Microstructural Characterization of AGR-2 TRISO-coated Particle Buffer, IPyC, and Buffer-IPyC Interfaces

Investigating the microstructural, mechanical, and chemical behaviors of Tristructural Isotropic (TRISO) fuel particles is vital for its qualification and use in advanced reactors. Central to the study of TRISO particles is understanding the silicon carbide (SiC) layer's ability to confine fission products, with failure mechanisms linked to chemical degradation following mechanical degradation of the buffer and IPyC layers. Research has been done to quantify the micro-tensile properties of the buffer, inner pyrolytic carbon (IPyC), and buffer-IPyC interlayer regions and their interactions within both irradiated and un-irradiated TRISO particles. Techniques such as atom probe tomography (APT) and transmission electron microscopy (TEM) have also been deployed to examine microstructural defects and fission product distribution in detail. The goal is to understand layer delamination, establish connections between microstructure and mechanical attributes, and inform computational predictions of fuel performance. This work may help refine predictive models of TRISO fuel behavior and facilitating its certification for use in advanced reactors.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2026

Electric-field-assisted-sintering of rare-earth oxide dispersion strengthened Fe-Cr-Mo alloys

Oxide dispersion-strengthened (ODS) alloys are widely recognized for their exceptional high-temperature strength, creep resistance, and radiation tolerance, making them indispensable for advanced nuclear reactors, aerospace, and energy systems. Achieving a fine and stable dispersion of oxide nanoparticles is critical, as these particles act as strong barriers to dislocation motion and effective sinks for irradiation-induced defects, ensuring structural integrity under extreme conditions. Here, in this study, Fe–Cr–Mo-based ODS alloys were fabricated via mechanical alloying and consolidated using electric-field-assisted sintering (EFAS) with additions of Y 2 O 3 , La 2 O 3 , and CeO 2 . EFAS processing produced ultrafine-grained microstructures (average grain size <1 μm) with uniformly distributed oxide clusters (2–4 nm). Atom probe tomography revealed that La 2 O 3 -containing alloys exhibited the highest nanoparticle number density, resulting in superior tensile strength compared to yttria- and ceria-bearing counterparts. The combined effect of grain refinement and rare-earth oxide dispersion significantly enhanced mechanical performance, demonstrating the potential of EFAS for developing high-strength ferritic alloys for demanding environments such as nuclear systems.

36 - MATERIALS SCIENCE↗
DOE OSTI2025

Lattice expansion due to hydrogen absorption into β-rhombohedral boron

β-Rhombohedral boron (β-boron) represents one of the most popular and important allotropic forms of elemental boron. The unit cell of β-boron crystal consists of B 106.6 with a complicated and relatively open structure. We have previously reported the abrupt lattice expansion and shrinkage of β-boron crystal by thermal treatment above 700 K and photoirradiation at room temperature. Our recent studies, combined with X-ray diffraction and atom probe tomography experiments, suggest that the lattice expansion and shrinkage are related to the absorption and release of hydrogen into the structure. In conclusion, the results lead us to a new application of β-boron as a photo-switchable hydrogen storage material.

Atom probe tomography↗
DOE OSTI2025

Influence of temperature, oxygen partial pressure, and microstructure on the high-temperature oxidation behavior of the SiC Layer of TRISO particles

Tristructural isotropic (TRISO)-coated fuel particles are designed for use in high-temperature gas-cooled nuclear reactors, featuring a structural SiC layer that may be exposed to oxygen-rich environments over 1000 °C. Surrogate TRISO particles were tested in 0.2–20 kPa O 2 atmospheres to observe the differences in oxidation behavior. Oxide growth mechanisms remained consistent from 1200–1600 °C for each P O$_2$ , with activation energies of 228 ± 7 kJ/mol for 20 kPa O 2 and 188 ± 8 kJ/mol for 0.2 kPa O 2 . At 1600 °C, kinetic analysis revealed a change in oxide growth mechanisms between 0.2 and 6 kPa O2. In 0.2 kPa O 2 , oxidation produced raised oxide nodules on pockets with nanocrystalline SiC. Oxidation mechanisms were determined using Atom probe tomography. Active SiC oxidation occurred in C-rich grain boundaries with low P O$_2$ , leading to SiO 2 buildup in porous nodules. Here, this phenomenon was not observed at any temperature in 20 kPa O 2 environments.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2025

Facet-Dependent Doping and Dopant-Dependent Faceting in Si-Doped GaAsSb Nanowires

Here, we correlate the spatial distributions of Si, Sb, and rotational twins in Si-doped GaAs 1-x Sb x nanowires. GaAs 1-x Sb x nanowires were grown epitaxially on Si(111) substrates by tuning process conditions to achieve repeated nucleation of rotational twins and growth along the [111]B direction; dilute Sb and Si fluxes were chosen to create a sufficient twin density to achieve high yield while avoiding growth of the wurtzite phase. While the impact of Si and Sb on twin density and nanowire growth rate has been previously reported, the facet-dependent incorporation of these species has not been established. Scanning transmission electron microscopy was used to confirm that Sb incorporates preferentially on the (111)B facets relative to {1 ̅1 ̅0} facets prior to nucleation of a rotational twin. With periodic twinning, this facet dependence leads to alternating regions of enriched and depleted Sb concentration attributed to a growth rate-dependent Sb-As-exchange mechanism. Atom probe tomography measurements establish that while Si doping is not perturbed by twinning on (111)B facets, Si and Sb concentrations are anti-correlated for growth on non-(111)B facets. Density functional theory calculations underpin a thermodynamic model that explains the observed anisotropies in dopant incorporation.

36 MATERIALS SCIENCE↗