Results for “defect detection” — Page 5

DOE OSTI2025

Describing Point Defect Topology in 2D Energy Materials through Computer Vision

Point defects such as vacancies and impurity atoms strongly impact the performance of 2D materials. Traditional efforts often rely on manual detection, a process that is time-intensive, prone to human error, and challenging to scale. Here we leverage machine learning (ML) methods to identify and quantify vacancies within 2D transition metal carbides (Ti3C2, MXenes), aiming to expedite detection while improving accuracy. MXenes exhibit valuable defect-defined electrochemical properties, but we currently lack statistical understanding of defect topology needed to fully harness these materials. Here we employ a convolutional neural network for semantic segmentation of experimental MXene images, opening an opportunity to conduct a rigorous statistical study on defect hierarchy while investigating local relaxation in the lattice. We show how the integration of ML can yield fundamental insight into point defects, providing a powerful tool that will play an increasingly crucial role in the future of materials science.

2d materials↗

DOE OSTI2025

Describing Point Defect Topology in 2D Energy Materials Through Computer Vision

Point defects such as vacancies and impurity atoms strongly impact the performance of 2D materials. Traditional efforts often rely on manual detection, a process that is time-intensive, prone to human error, and challenging to scale. Here we leverage machine learning (ML) methods to identify and quantify vacancies within 2D transition metal carbides (Ti3C2, MXenes), aiming to expedite detection while improving accuracy. MXenes exhibit valuable defect-defined electrochemical properties, but we currently lack statistical understanding of defect topology needed to fully harness these materials. We employ a convolutional neural network for semantic segmentation of experimental MXene images, opening an opportunity to conduct a rigorous statistical study on defect hierarchy while investigating local relaxation in the lattice. We show how the integration of ML can yield fundamental insight into point defects, providing a powerful tool that will play an increasingly crucial role in the future of materials science.

2D materials↗

DOE OSTI2026

Describing Point Defect Topology in 2D Energy Materials Through Computer Vision

Point defects such as vacancies and impurity atoms strongly impact the performance of 2D materials. Traditional efforts often rely on manual detection, a process that is time-intensive, prone to human error, and challenging to scale. Here we leverage machine learning (ML) methods to identify and quantify vacancies within 2D transition metal carbides (Ti3C2, MXenes), aiming to expedite detection while improving accuracy. MXenes exhibit valuable defect-defined electrochemical properties, but we currently lack statistical understanding of defect topology needed to fully harness these materials. Here we employ a convolutional neural network for semantic segmentation of experimental MXene images, opening an opportunity to conduct a rigorous statistical study on defect hierarchy while investigating local relaxation in the lattice. We show how the integration of ML can yield fundamental insight into point defects, providing a powerful tool that will play an increasingly crucial role in the future of materials science. ML is often not just a matter of straightforward application, and pretrained models proved ineffective in this case. Instead, we trained our own neural network (NN) and applied data augmentation techniques and fine-tuning to the training dataset. Since labeled microscopy data is often scarce, we developed training data from a previously published wide-frame MXene image, using customized Gaussian fitting to locate atomic positions. Our trained model was then applied to a large dataset of experimental images, enabling a statistical study of defect configurations across three samples prepared with different HF etchant concentrations (5%, 9.1%, and 12.5%), as shown in Fig. 1. This also allowed us to investigate local strain around vacancies, though we find that we are limited by the precision of measurements using high-angle annular dark field (HAADF) images, as shown in Fig. 2. This study demonstrates how ML enables large-scale, quantitative analysis of atomic defects - an otherwise infeasible task with traditional methods. While our NN was specialized for Ti3C2 MXenes, the pipeline we developed provides a foundation for future ML models tailored to other materials. Ultimately, we envision embedding the NN onto the microscope to give real-time feedback to the user. To make this a reality, continued work is necessary to fully understand the NN's capabilities and limitations. This study gets one step closer to our goals of automated experimentation moving away from traditional methods of manual labeling. As ML capabilities advance, we hope to continue adapting and applying these techniques in microscopy.

2D materials↗

DOE OSTI2026

Subterahertz Spin Relaxation Dynamics of Boron-Vacancy Centers in Hexagonal Boron Nitride

Quantum sensors based on spin defects have become powerful tools for detecting faint magnetic signals, yet their operation remains confined to low magnetic fields and gigahertz frequencies. Extending such sensors into high-field (>0.3 T) and subterahertz regimes would enable quantum metrology across a wide range of electromagnetic phenomena and scientific applications, but has proven challenging. Here, we demonstrate that negatively charged boron vacancies ($V^−_B$) in hexagonal boron nitride can function as relaxation-based quantum sensors operating up to 0.2 terahertz and 7 T fields. Their uniform spin-orientation and persistent spin-contrast at high fields enable measurement of intrinsic spin relaxation across unexplored field regimes. We reveal a crossover in relaxation behavior, initially decreasing at low fields before rising at higher fields, consistent with the emergence of single-phonon-induced resonant noise at subterahertz frequencies. These results establish $V^−_B$ centers as a versatile platform for quantum sensing in the subterahertz, high-field regime.

boron-vacancies↗

DOE OSTI2025

Metallicity and anomalous Hall effect in epitaxially strained, atomically thin RuO 2 films

The anomalous Hall effect (AHE), a hallmark of time-reversal symmetry breaking, has been reported in rutile RuO 2 , a debated metallic altermagnetic candidate. Previously, AHE in RuO 2 was observed only in strain-relaxed thick films under extremely high magnetic fields (~50 T). Yet, in ultrathin strained films with distinctive anisotropic electronic structures, there are no reports, likely due to disorder and defects suppressing metallicity thus hindering its detection. Here, we demonstrate that ultrathin, fully strained 2 nm TiO 2 /t nm RuO 2 /TiO 2 (110) heterostructures, grown by hybrid molecular beam epitaxy, retain metallicity and exhibit a sizeable AHE at a significantly lower magnetic field (< 9 T). Density functional theory calculations reveal that epitaxial strain stabilizes a noncompensated magnetic ground state and reconfigures magnetic ordering in RuO 2 (110) thin films. These findings establish ultrathin RuO 2 as a platform for strain-engineered magnetism and underscore the transformative potential of epitaxial design in advancing spintronic technologies.

MBE↗

DOE OSTI2024

Deep learning with mixup augmentation for improved pore detection during additive manufacturing

In additive manufacturing (AM), process defects such as keyhole pores are difficult to anticipate, affecting the quality and integrity of the AM-produced materials. Hence, considerable efforts have aimed to predict these process defects by training machine learning (ML) models using passive measurements such as acoustic emissions. This work considered a dataset in which keyhole pores of a laser powder bed fusion (LPBF) experiment were identified using X-ray radiography and then registered both in space and time to acoustic measurements recorded during the LPBF experiment. Due to AM’s intrinsic process controls, where a pore-forming event is relatively rare, the acoustic datasets collected during monitoring include more non-pores than pores. In other words, the dataset for ML model development is imbalanced. Moreover, this imbalanced and sparse data phenomenon remains ubiquitous across many AM monitoring schemes since training data is nontrivial to collect. Hence, we propose a machine learning approach to improve this dataset imbalance and enhance the prediction accuracy of pore-labeled data. Specifically, we investigate how data augmentation helps predict pores and non-pores better. This imbalance is improved using recent advances in data augmentation called Mixup, a weak-supervised learning method. Convolutional neural networks (CNNs) are trained on original and augmented datasets, and an appreciable increase in performance is reported when testing on five different experimental trials. When ML models are trained on original and augmented datasets, they achieve an accuracy of 95% and 99% on test datasets, respectively. We also provide information on how dataset size affects model performance. Lastly, we investigate the optimal Mixup parameters for augmentation in the context of CNN performance.

36 MATERIALS SCIENCE↗

DOE OSTI2024

Deep learning with mixup augmentation for improved pore detection during additive manufacturing

In additive manufacturing (AM), process defects such as keyhole pores are difficult to anticipate, affecting the quality and integrity of the AM-produced materials. Hence, considerable efforts have aimed to predict these process defects by training machine learning (ML) models using passive measurements such as acoustic emissions. This work considered a dataset in which keyhole pores of a laser powder bed fusion (LPBF) experiment were identified using X-ray radiography and then registered both in space and time to acoustic measurements recorded during the LPBF experiment. Due to AM’s intrinsic process controls, where a pore-forming event is relatively rare, the acoustic datasets collected during monitoring include more non-pores than pores. In other words, the dataset for ML model development is imbalanced. Moreover, this imbalanced and sparse data phenomenon remains ubiquitous across many AM monitoring schemes since training data is nontrivial to collect. Hence, we propose a machine learning approach to improve this dataset imbalance and enhance the prediction accuracy of pore-labeled data. Specifically, we investigate how data augmentation helps predict pores and non-pores better. This imbalance is improved using recent advances in data augmentation called Mixup, a weak-supervised learning method. Convolutional neural networks (CNNs) are trained on original and augmented datasets, and an appreciable increase in performance is reported when testing on five different experimental trials. When ML models are trained on original and augmented datasets, they achieve an accuracy of 95% and 99% on test datasets, respectively. We also provide information on how dataset size affects model performance. Lastly, we investigate the optimal Mixup parameters for augmentation in the context of CNN performance.

42 ENGINEERING↗

DOE OSTI2025

Defect Engineering in Large‐Scale CVD‐Grown Hexagonal Boron Nitride: Formation, Spectroscopy, and Spin Relaxation Dynamics

Recently, numerous techniques have been reported for generating optically active defects in exfoliated hexagonal boron nitride (hBN), which hold transformative potential for quantum photonic devices. However, achieving on-demand generation of desirable defect types in scalable hBN films remains a significant challenge. Here, it is demonstrated that formation of negative boron vacancy defects, V B − , in suspended, large-area CVD-grown hBN is strongly dependent on the type of bombarding particles (ions, neutrons, and electrons) and irradiation conditions. In contrast to suspended hBN, defect formation in substrate-supported hBN is more complex due to the uncontrollable generation of secondary particles from the substrate, and the outcome strongly depends on the thickness of the hBN. Different defect types are identified by correlating spectroscopic and optically detected magnetic resonance features, distinguishing boron vacancies (formed by light ions and neutrons and emitting at 800 nm) from other optically active defects emitting at 650 nm assigned to anti-site nitrogen vacancy (N B V N ) and reveal the presence of additional “dark” paramagnetic defects that influence spin-lattice relaxation time (T 1 ) and zero-field splitting parameters, all of which strongly depend on the defect density. These results underscore the potential for precisely engineered defect formation in large-scale CVD-grown hBN, paving the way for the scalable fabrication of quantum photonic devices.

CVD↗

DOE OSTI2025

Laser Powder Bed Fusion Manufactured Alloys 625 and 282 and Properties Relevant to Advanced Nuclear Components

Under the Advanced Materials and Manufacturing Technologies program, two Ni-based alloys fabricated by laser powder bed fusion (LPBF) have been evaluated: γ′-strengthened Haynes 282 and solution-strengthened Inconel 625. Large printing defects were observed in the LPBF 282 alloy fabricated using a Renishaw AM250 machine, likely due to particle spattering during printing. Annealing at 1,180°C for 1 h followed by 4 h at 800°C in a high density of 50 nm γ′-strengthening precipitates as well as partial recrystallization resulted in a bimodal grain distribution. Creep testing performed at 750°C revealed lower creep life and ductility for the LPBF 282 compared with wrought 282. X-ray computed tomography combined with optical and scanning electron microscopy microstructural characterization revealed crack formation during creep testing, initiated either from printing defects or from creep cavitation at grain boundaries. Printing defects were the likely reason for the lower creep performance of LPBF 282 and could be suppressed by optimizing the build configuration. Printing 625 on an EOS M290 machine using the recommended EOS parameters resulted in a very low defect density. Superior creep strength at 725°C was observed for the as-printed LPBF 625 along the build direction when compared with wrought 625. No defect larger than ~50 µm could be detected by X-ray computed tomography in the as-printed conditions and after creep testing for 1,000 h at 725°C and 150 MPa. Small, needle-like δ-phase precipitates were observed after creep testing for 100 h at 725°C and 200 MPa. The rapid formation of the γ phase is directly related to the initial Nb and Mo segregation in the LPBF 625 cell walls, and its effect on the alloy’s high-temperature performance will be evaluated. Solution annealing was carried out at 1,150°C for 1 h and led to full recrystallization of the alloy. A decrease in strength and increase in ductility were observed after solution annealing, and the alloy recrystallization resulted in isotropic tensile properties at room temperature, contrary to what was observed for the as-printed LPBF 625.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2024

Unveiling long-lived dual emission in a tetraphenylethylene-based metal–organic framework

Incorporating photoactive linkers into metal–organic frameworks (MOFs) has proved useful in improving photophysical properties of organic chromophores. This is achieved by controlling the local packing of linkers or defect engineering within the MOF. Using these ideas, we demonstrate that a tetraphenylethylene-based MOF exhibits long-lived linker-based emission out to 1 μs—substantially longer than previously reported. The emission contains two independent components whose dynamics branch from early timescales. Furthermore these findings suggest that charge recombination and distinct defect sites exist and contribute a weak yet detectable emission, and demonstrate how high-sensitivity transient photoluminescence spectroscopy can reveal unexpected populations in nominally crystalline materials.

36 MATERIALS SCIENCE↗

DOE OSTI2025

Detecting rare earth elements via optically detected magnetic resonance (ODMR) and spin-relaxometry using nitrogen vacancy centers in nanodiamonds

Nanodiamonds embedded with nitrogen-vacancy (NV) centers are emerging as powerful tools in quantum sensing due to their remarkable sensitivity to local variations in electromagnetic fields, temperature, and pressure. These point defects in the diamond lattice can be probed using optically detected magnetic resonance (ODMR) and spin relaxometry techniques, allowing for the detection and characterization of magnetic nanoparticles and ions. The sensing mechanism relies on laser-induced excitation of the NV center's non-degenerate triplet ground state, which promotes electrons to an excited state. Subsequent photoluminescence (PL) emission, which is spin-state dependent, is then measured. These spin states are highly responsive to magnetic perturbations in the surrounding environment. By analyzing changes in the ODMR spectra and spin relaxation times, we can effectively monitor how magnetic species influence the NV center's spin dynamics, thus enabling sensitive and selective characterization of nanoscale magnetic materials. These techniques have been applied for the characterization of transition metals and rare earth elements which shows natural ferromagnetic properties.

magnetic nanopartgicles and ions↗

DOE OSTI2024

Advanced Instrumentation for Metal Additive Manufacturing

Laser powder bed fusion (LPBF) is the most widely used process for metal additive manufacturing (AM), particularly where complex geometries provide performance advantages unattainable with traditional manufacturing techniques. However, LPBF is highly sensitive to innate variability in both the powder spreading and fusion steps, often leading to defects such as pores that are difficult to detect yet significantly impair component mechanical properties and fatigue life. This thesis presents a range of novel instruments enabling both precise assessment of powder layer characteristics and in-situ thermal metrology of metal AM to advance the quality control of LPBF. First, leveraging a custom X-ray microscope and a radiation-transport model developed through this work, transmission X-ray imaging is used to study spreading of thin metal powder layers. Effective layer depth is directly mapped at a process-relevant size scale, surpassing optical techniques that can only estimate local deposition from layer surface topography. Layer packing density and quality are shown to be influenced by powder flowability and particle size relative to nominal powder layer thickness. Layer quality is additionally connected to the geometry of the spreading implement and its velocity. This technique and its presented findings enable pairing feedstocks with spreading strategies that create layers with consistent packing density and uniformity. Second, a twofold approach is employed to optically interrogate the laser fusion step of LPBF for observing signatures of defect formation. Aperture division multiplexing is conceptualized, providing for simultaneous laser delivery and high-fidelity infrared (IR) process monitoring through a common optic. In-situ microscopy at 50 μm spatial resolution and at mid-wave IR wavelengths is proven readily achievable with the first purpose-built optic of this type. Next, a bespoke imaging spectrometer, along with a temperature-emissivity separation technique, is used to retrieve accurate process temperatures over a 1000 K range. Data from these instruments are correlated to porosity as fine as 4.3 μm in two LPBF test artifacts, as verified using computed tomography (CT), establishing the viability of robust optically-based component qualification.

Penny, Ryan↗

DOE OSTI2026

Towards dislocation-driven quantum interconnects

A central problem in the deployment of quantum technologies is the realization of robust architectures for quantum interconnects. We propose to engineer interconnects in semiconductors and insulators by patterning spin qubits at dislocations, thus forming quasi one-dimensional lines of entangled point defects. To gain insight into the feasibility and control of dislocation-driven interconnects, we investigate the optical cycle and coherence properties of nitrogen-vacancy (NV) centers in diamond, in proximity of dislocations, using a combination of advanced first-principles calculations. We show that one can engineer spin defects with properties similar to those of their bulk counterparts, including charge stability and a favorable optical cycle, and that NV centers close to dislocations have much improved coherence properties. Finally, we predict optically detected magnetic resonance spectra that may facilitate the experimental identification of specific defect configurations. Our results provide a theoretical foundation for the engineering of one-dimensional arrays of spin defects in the solid state.

Materials science↗

DOE OSTI2026

Sensor Reduction for Diversion Detection in a Realistic Heat Pipe Microreactor Using Supervised Machine Learning

Microreactors are designed as a smaller, cheaper, and safer alternative to traditional nuclear power plants. Their non-traditional characteristics and prospect of mass production and deployment will likely require new approaches to nuclear safeguards. The primary proliferation concern with microreactors is the diversion of fuel material. Such diversion may produce measurable defects in key physical attributes like neutron flux, which may in turn be detectable using machine learning models. Preliminary work has demonstrated this ability for modeled nominal and diversion scenarios using large quantities of energy integrated neutron flux data. In practice, the number of available sensors for such measurements will be limited and energy integrated flux information will not be available. This work explores the ability of tree-based gradient boosted ensemble models to classify a given microreactor core is nominal or diversion, and determine the number of fuel pins diverted in the case of diversion with reduced numbers of sensors and more realistic detector responses. Classification accuracy of greater than 98% and regression errors as low as 5% of the total number of fuel pins were achieved with as few as 15 sensors, compared to 99% and 4.1% with a maximum of 240 sensors.

22 - GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2024

Gallium oxide semiconductor-based large volume ultrafast radiation hard spectroscopic scintillators

We report on the development of the first-ever inorganic radiation-hard moisture-insensitive large volume spectroscopic semiconductor-based scintillator with less than 2 ns decay time and light yields as high as 8000 ph/MeV. Despite extensive research into scintillator materials, the quest for an ideal scintillator combining ultrafast decay times (akin to BaF 2 and Yb-doped scintillators such as Lu 2 O 3 :Yb), high light yields (exceeding 2000 photons per MeV), spectroscopic capabilities, and exceptional radiation hardness remain unfulfilled. In this study, we demonstrate and report for the first time the viability of large-volume (up to 20 mm thickness) gallium oxide (β-Ga 2 O 3 ) semiconductor-based scintillators for applications requiring these properties. These β-Ga 2 O 3 scintillators were grown using the fast turnaround (~2 days) crucible-free optical float zone (FZ) technique. The high light yield and ultrafast decay time of these high-purity n-type semiconductors with free carrier concentration of 6 × 10 17 cm –3 are attributed to native defects, specifically oxygen vacancies (V O ) and gallium–oxygen vacancy pairs (V Ga –V O ), generated during optimized FZ growth. The ultrafast decay, along with high light yield, enables excellent timing resolution and high count rate detection for applications like time-of-flight positron emission tomography, physics experiments, and nuclear safety. The radiation hardness of these devices has been documented in a separate publication.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2024

Monitoring the corrosion resistance of cold-spray coatings with membrane-based sensors

In this presentation, the MBES shows the ability to detect the change in exposed environment even with cold-spray coatings. Corrosion rates from the cold-spray coating were comparable to a 316 stainless steel sample. Defects in the coating did not impact sensor performance in terms of corrosion rate or water content detection. Energy dispersive X-ray spectroscopy was used to document ion migration into the membrane.

Rendon, Arnaldo↗

DOE OSTI2025

Enhanced Design of Radiation Tolerant High-Temperature Structural Health Monitoring Sensors

Acoustic emission sensors are vital in the nuclear industry for real-time structural health monitoring and early detection of material degradation. By capturing high-frequency stress waves emitted from defects like cracks, corrosion, or fatigue, acoustic emission sensors enable non-invasive monitoring of critical components such as reactor vessels, piping, and containment structures. This technology supports predictive maintenance, enhances safety, and ensures regulatory compliance by providing early warnings of potential failures. It is also instrumental in research, particularly in material testing reactors, where it is used to monitor the behavior of fuels and materials under irradiation, by allowing the detection of cracking or other acoustic signals in real time. This enables the evaluation of performance and accident behavior of advanced fuel concepts.

46 - INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AN↗

DOE OSTI2024

High-field/high-frequency electron spin resonances of Fe-doped β − Ga 2 O 3 by terahertz generalized ellipsometry: Monoclinic symmetry effects

We demonstrate detection and measurement of electron paramagnetic spin resonances (EPR) of iron defects in β − Ga 2 O 3 utilizing generalized ellipsometry at frequencies between 110 and 170 GHz. The experiments are performed on an Fe-doped single crystal in a free-beam configuration in reflection at 45 ∘ and magnetic fields between 3 and 7 T. In contrast with low-field, low-frequency EPR measurements, we observe all five transitions of the s = 5 / 2 high-spin state Fe 3 + simultaneously. We confirm that ferric Fe 3 + is predominantly found at octahedrally coordinated Ga sites. We obtain the full set of fourth-order monoclinic zero-field splitting parameters for both octahedrally and tetrahedrally coordinated sites by employing measurements at multiple sample azimuth rotations. The capability of high-field EPR allows us to demonstrate that simplified second-order orthorhombic spin Hamiltonians are insufficient, and fourth-order terms as well as consideration of the monoclinic symmetry are needed. These findings are supported by computational approaches based on density-functional theory for second-order and on ligand-field theory for fourth-order parameters of the spin Hamiltonian. Terahertz ellipsometry is a way to measure spin resonances in a cavity-free setup. Its possibility of varying the probe frequency arbitrarily without otherwise changing the experimental setup offers unique means of truly disentangling different components of highly anisotropic spin Hamiltonians. Published by the American Physical Society 2024

Materials Science↗