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At least 127 records · Page 7

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DOE OSTI2024

DOC-DICAM: Domain Aware One Class Defect Identification in Composite Aerostructure Material

Fiber-reinforced composites are a common material used in the design of aircraft structures due to their good tensile strength and resistance to compression. During the manufacturing process, these structures are thoroughly inspected for flaws and defects to ensure structural integrity during commercial use. Non-destructive testing (NDT) is a collection of inspection methods that allow inspectors to evaluate material without altering it. Due to the high safety standards in aerospace manufacturing, the NDT process is done manually and can be a significant bottleneck in the development workflow. In this paper, we develop an AI-based assistance tool to drastically reduce inspection time. Typical AI workflows require large amounts of annotated data, but defects rarely occur resulting in strong class imbalance. To overcome this, we formulate the problem of defect identification as an anomaly detection task in which our primary focus is learning non-defect characteristics. To do this, we develop a multi-task self-supervised learning framework that embeds problem specific domain knowledge into the deep learning model. We verify our method using fuselage data generated in a production environment. As a result, we show that our method can effectively identify defects and requires minimal training and inference time.

anomaly detection↗

DOE OSTI2024

Development of a MOOSE-based crystal plasticity model with irradiation defect evolution for irradiation creep in 316

Irradiation creep and irradiation swelling are two of the lifetime limiting factors for structural materials in nuclear reactors. These mechanical effects are driven by irradiation defect evolution and the interaction of those defects with dislocations in the microstructure. We present here a coupled cluster dynamics – crystal plasticity approach to model irradiation swelling and creep behavior in 316 SS. The time-dependent evolution of irradiation defects is calculated with a cluster dynamics approach and passed to the crystal plasticity model to compute the dislocation evolution. We show the impact of the irradiation defect evolution on the stress state in the material, which drives inelastic deformation through dislocation motion. The inelastic deformation in the 316 SS is dependent on the dose rate, where the inelastic deformation driven by the early-stage irradiation defect evolution determines the mechanical behavior of the 316 SS.

316 Stainless Steels↗

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

The Role of Defect Geometry in Localized Emission from Monolayer Tungsten Dichalcogenides

In two-dimensional transition metal dichalcogenides such as tungsten diselenide (WSe 2 ), single photon emission has been broadly attributed to exciton localization from atomic point defects, yet the precise microscopic origins are unclear. This work introduces an empirically grounded computational framework that explains the origins of facile single photon emission in WSe 2 . High-resolution microscopy identifies native defect geometries in monolayer WSe 2 lattices from which the model is built. Here, the qualitative effects of chalcogen type, defect geometry, and mechanical strain on the electronic structure are individually assessed using density functional theory, and a specific divacancy configuration emerges as the candidate for localized single-electron transitions that match observed spectral energies. Spectroscopy and photon correlation measurements further validate this model, establishing a self-consistent link between defect geometry, electronic structure, and quantum emission.

defect emission↗

DOE OSTI2026

Controlling Nb(IV) Defects in SrNbO 2 N Oxygen Evolution Photocatalyst by Ammonolysis With Dinitrogen–Ammonia Mixtures

Strontium niobium oxynitride (SrNbO 2 N) is a promising, corrosion resistant semiconductor for the visible light-driven water splitting reaction, a non-photovoltaic pathway to green hydrogen fuel. However, SrNbO 2 N materials made by ammonolysis usually contain Nb 4+ defect states that cause electron–hole recombination. Here, in this work, we demonstrate that such defects can be minimized by synthesizing SrNbO 2 N from metal oxides in a mixed 13%:87% (vol) NH 3 /N 2 atmosphere. According to electron paramagnetic resonance (EPR), SrNbO 2 N made in pure NH 3 contains paramagnetic impurities with g = 2.002 and 2.195, which can be assigned to lattice and surface Nb 4+ defects. These states also cause broad optical absorptions centered at 800 and 1020 nm, respectively, and the lattice defect produces a 1.55–1.63 eV signal in surface photovoltage spectra. The improved SrNbO 2 N contains five times fewer lattice Nb 4+ defects (8.95 × 10 15 cm −3 ), based on the integrated EPR signal intensity, and supports a water oxidation photocurrent of 1.07 mA cm −2 at 1.23 V versus RHE under simulated sunlight and an apparent quantum efficiency of 5.1% at 400 nm during photocatalytic oxygen evolution. Based on earlier results with LaTiO 2 N and BaTaO 2 N, dilution of NH 3 during synthesis appears generally beneficial to transition metal oxynitrides.

electron paramagnetic resonance↗

DOE OSTI2024

Role of Selenium in CdZnTeSe as a Defect Engineering Agent

Cadmium zinc telluride (CdZnTe) with 10 atomic % Zn has been the material of choice for room-temperature semiconductor compact X- and gamma-ray detector applications for over three decades. Despite its commercial success as the most desirable room-temperature semiconductor radiation detection material, CdZnTe (CZT) suffers from a lack of compositional homogeneity on both the micro- and macroscales and the presence of high concentrations of performance-limiting defects such as subgrain boundary networks (dislocation walls) and secondary phases (Te-rich inclusions). Random distributions of these defects in the CZT matrix result in the spatial inhomogeneity of the material’s charge-transport properties. The recently discovered quaternary Cd x Zn 1-x Te 1-y Se y (CZTS) has experienced remarkable advances in its material properties, with highly reduced defects and higher compositional homogeneity. This book chapter focuses on the presence of performance-limiting defects in CZT that have thus far hindered the yield and cost of high-quality detectors and restricted their widespread deployment for a variety of potential applications, particularly for their use as large-volume gamma detectors, where the demands on material perfection are significantly greater. This chapter also provides an overview of the recent developments in the quaternary material CZTS, particularly the effects of selenium (Se) in the CZTS matrix on the defect engineering of the quaternary alloy material and the advancement of CZTS as a potential next-generation detector operable at room temperature.

Roy, Utpal N.↗

DOE OSTI2026

Kinetic control of phase evolution and defect-mediated recombination in AACVD-grown copper antimony sulphide thin films

Copper antimony sulphide (CAS) is a multinary chalcogenide semiconductor in which small deviations from stoichiometry can drive phase competition and strong defect-mediated modulation of optoelectronic properties. However, the systematic roles of copper precursor fraction and deposition time in governing phase evolution, off-stoichiometry, and recombination dynamics in aerosol-assisted chemical vapour deposition (AACVD)-grown undoped CAS thin films remain insufficiently understood. In this work, undoped CAS thin films were deposited by AACVD using Cu(dedtc)2 and Sb(dedtc)3 single-source precursors at 550 °C and a carrier gas flow rate of 150 sccm, while the Cu(dedtc)2 mole fraction (x = 0.15 – 0.55) and deposition time (1 – 2 hours) were systematically varied to probe how growth kinetics influence phase composition, microstructure, and defect-mediated optical properties without post-deposition annealing or extrinsic doping. Increasing copper precursor content drives phase evolution toward tetrahedrite-dominant CAS films at intermediate Cu(dedtc)2 mole fractions, with the film deposited at x = 0.35 exhibiting the strongest tetrahedrite character within the parameter space examined. The films are also copper-rich, antimony-poor, and sulphur-deficient, consistent with off-stoichiometric growth and intrinsic defect formation, plausibly including copper interstitials, Cu-on-Sb antisites, and sulphur vacancies. These growth-dependent compositional deviations are accompanied by tunable indirect optical bandgaps of approximately 1.60 – 2.18 eV and weak visible photoluminescence governed by defect-mediated recombination. Time-resolved photoluminescence reveals bi-exponential decay behaviour with lifetimes of approximately 0.1 – 3.6 ns, with emission dominated by slower donor–acceptor pair recombination and a smaller contribution from faster trap-assisted pathways. Collectively, these results establish an explicit kinetic process–structure–defect–property relationship for AACVD-grown CAS thin films and provide a growth–structure–property framework relevant to future optimization of CAS-based optoelectronic and energy materials.

36 MATERIALS SCIENCE↗

DOE OSTI2025

Simulation-driven design optimization of reaction injection molding (RIM) process for polydicyclopentadiene (pDCPD): Minimizing cycle time, defects, and warpage

Replacing metal components in trucks, trailers, and buses with lightweight polymer composites is challenging due to high temperatures and complex manufacturing. The Reaction Injection Molding (RIM) process using Dicyclopentadiene (DCPD) resin offers a solution by producing robust parts with excellent stiffness, impact strength, and resistance properties. Simulations are essential for optimizing this process, predicting defects, and improving quality. However, most commercial software is tailored for thermoplastics, requiring thermoset users to generate their own datasets. In this study, a material data card for DCPD was developed to perform RIM simulations. Design of Experiments (DOE) was used to identify key factors affecting filling, curing, and warpage, aiming to minimize cycle time and defects. The simulations explored varying injection gate parameters (size, location, number) and process conditions (mold/resin temperature, injection/curing pressure). Results showed that gate design significantly impacts filling behavior and defects. A single central gate provided balanced flow with fewer defects, while two corner gates led to more defects. Additionally, lower injection pressure increased filling time, while higher mold temperature accelerated curing but led to more warpage. In conclusion, this optimization framework aims to enhance DCPD part performance and promote sustainable manufacturing by reducing waste and energy consumption.

42 ENGINEERING↗

DOE OSTI2025

Implications of point defect accumulation on UO 2 thermal conductivity and fission gas release under accelerated fuel irradiation

Evaluation of thermal properties is a crucial factor for nuclear fuel performance. During reactor operation, the accumulation of fission products and irradiation-induced lattice defects are responsible for degradation in thermal conductivity. Consequently, it affects fuel temperature and fission gas release (FGR) among other Multiphysics processes important for economics and safety analysis. We analyze the implications of point defects (PD) accumulation described using a rate theory (RT) Model on lattice thermal conductivity of UO 2 . Here, we demonstrate that fission rate-dependent point defect concentrations have the largest impact on in-pile thermal conductivity in the periphery of light water reactor fuels below a temperature threshold governed by the migration barrier of defects. Our analysis provides a mechanistic description of this phenomena which current fuel performance codes treat empirically. The reduction of thermal conductivity in the low -temperature rim region acts as additional thermal resistance and leads to a temperature notably larger than suggested by Lucuta thermal conductivity correlation. These effects are anticipated to have notable impacts when fuels are exposed to accelerated radiation. The impact of such point defect-informed treatment of thermal conductivity on fuel performance is evaluated by a detailed analysis of fission gas behavior and its release. We consider several models capturing different stages of fission gas bubble evolution and fission gas release (FGR). Finally, a new fission rate-dependent correction to the Lucuta correlation is proposed. The results show a significant reduction in thermal conductivity at the fuels’ periphery and an increase in fuel centerline temperature specifically at low burnups. Ultimately a modified LC shows a higher FGR compared to the original LC, while the acceleration process results in a reduction in overall FGR.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2025

Improved guided-wave acoustic defect detection and localization in pipes under varying temperature conditions using deep learning

Early defect detection in pipelines is critical across industries, particularly in the oil and gas sector, where failures result in significant maintenance costs and operational disruptions. Acoustic guided-wave techniques are widely used for nondestructive evaluation of pipeline defects due to their long-distance propagation capability. However, environmental variations, sensitivity limitations, and complex signal interpretation challenges limit the effectiveness of traditional signal processing approaches with guided-wave signals. Recent advances in deep learning methods have demonstrated remarkable success in solving complex real-world problems in many fields. In particular, deep-learning-based signal processing holds substantial promise to overcome limitations and challenges of conventional signal processing. This study presents a deep learning framework for pipeline inspection using acoustic guided-wave signals under temperature varying environments. The proposed framework employs a dual-path one-dimensional convolutional autoencoder that combines defect detection, localization, and temperature prediction functions. The proposed system utilizes multi-mode and broadband acoustic waves with an optimized number of sensors that provide high accuracy while retaining practical simplicity. Experimental validation is performed on a carbon steel pipe. The results indicate exceptional defect detection accuracy and precise defect localization with a mean absolute error of 66 mm. The proposed technique also predicts the effective average temperature of the pipe with a mean absolute error of 0.2°C. Comparative analysis shows superior performance of the proposed method over a traditional method previously developed by the authors' team. These results highlight the potential of integrating deep learning methods into guided-wave pipeline inspection systems to improve reliability under varying environmental conditions.

42 ENGINEERING↗

DOE OSTI2026

Radiative Electronic Bound States in the Continuum from Defects in Semiconductors

Continuum-buried defect states in semiconductors are generally expected to be optically inactive because of their strong coupling to continuum bands. Here, we show that such defects can instead host radiative electronic bound states in the continuum (BICs) using the silicon G center as a prototypical example. Hybrid functional first-principles calculations with a Hubbard U correction reveal that a localized defect state, initially buried below the valence band maximum (VBM) in the ground state, undergoes exchange-driven energy-level reordering under optical excitation and shifts above the VBM. This exchange-induced transition suppresses nonradiative decay and enables a robust radiative emission. By computing temperature-dependent nonradiative lifetimes and comparing them to experimental photoluminescence (PL) lifetimes, we quantitatively reproduce the observed temperature dependence of the emission. These results uncover a stabilization mechanism for continuum-embedded defect states and establish electronic BICs as a general paradigm for designing defect-based optical systems, including quantum emitters and qubits.

Hong, SeongYun↗

DOE OSTI2025

Low-energy pathways lead to self-healing defects in CsPbBr 3

Self-regulation of free charge carriers in perovskites via Schottky defect formation has been posited as the origin of the well-known defect-tolerance of metal halide perovskite materials. Understanding the mechanisms of self-regulation promises to lead to the fabrication of better performing solar cell materials with higher efficiencies. We investigate many such mechanisms here for CsPbBr 3 , a popular representative of a more commercially viable all-inorganic metal halide perovskite. We investigate different atomic-level mechanisms and pathways of the diffusion and recombination of neutral and charged interstitials and vacancies (Schottky pairs) in CsPbBr 3 . We use nudged elastic band calculations and ab initio-derived pseudopotentials within quantum ESPRESSO to determine energies of formation and migration and hence the activation energies for these defects. While halide vacancies are known to exhibit low formation energies, the migration of interstitials is less studied. Our calculations uncover interstitial defect pathways capable of producing an activation energy at, or below, the single experimental value of 0.53 eV observed for the slow, temperature-dependent recovery of light-induced conductivity in bulk CsPbBr 3 . Our work reveals the existence of a low-energy diffusion pathway involving a concerted “domino effect” of interstitials, with the net result that interstitials can diffuse more readily over longer distances than expected. This observation suggests that defect self-healing can be promoted if the “domino effect” strategy can be engaged.

14 SOLAR ENERGY↗

DOE OSTI2025

Mg and native defects in cubic silicon carbide from first principles

The diffusion of Mg defects in 3C-SiC is studied using the density functional theory. Mg has the highest burn-in rate as a transmutant in 3C-SiC when it is placed in high-energy neutron irradiation environment of a fusion reactor. The presence and evolution of transmutant defects impact thermal and mechanical properties of this important structural material. This study is focused on understanding the structure, stability, and evolution of Mg defects and the interaction of Mg with native defects in 3C-SiC. Our calculations of diffusion coefficients for different Mg defects suggest that Mg is likely to diffuse faster in pristine 3C-SiC than in the damaged one, in agreement with earlier experimental observations.

36 MATERIALS SCIENCE↗

DOE OSTI2025

Spin qubit properties of the boron-vacancy/carbon defect in the two-dimensional hexagonal boron nitride

Spin qubit defects in two-dimensional materials have a number of advantages over those in three-dimensional hosts including simpler technologies for defect creation and control, as well as qubit accessibility. In this work, we select the V B C B defect in the hexagonal boron nitride (hBN) as a possible optically controllable spin qubit and explain its triplet ground state and neutrality. In this defect a boron vacancy is combined with a carbon dopant substituting the closest boron atom to the vacancy. Our density-functional-theory calculations confirmed that the system has dynamically stable spin triplet and singlet ground states. As revealed from our linear response GW calculations, the spin-sensitive electronic states are localized around the three undercoordinated N atoms and make local peaks in the density of electronic states within the bandgap. Using the triplet and singlet ground state energies, as well as the energies of the optically excited states, obtained from solution to the Bethe–Salpeter equation, we construct the spin-polarization cycle, which is found to be favorable for the spin qubit initialization. The calculated zero-field splitting parameters ensure that the splitting energy between the spin projections in the triplet ground state is comparable to that of the known spin qubits. We thus propose the V B C B defect in hBN as a promising spin qubit.

2D BN↗

DOE OSTI2026

Point defects and impurities in fluorite PuO 2

The native surface oxide of plutonium plays a critical role in ensuring the stability and safe storage of the underlying metal; consequently, understanding the role of defects and impurities in determining the properties of the oxide layer is critical. Here, in this study, we use hybrid density-functional theory calculations to evaluate the electronic structure and defect chemistry of PuO 2 , the most stable of the native oxide phases, including both native and extrinsic defects. We find that oxygen vacancies (𝑉 O ) form readily in PuO 2 , as do polarons. Electron polarons (𝜂 − ) are the lowest-energy acceptor species in PuO 2 , while the charge compensating donor species will shift from 𝑉 O under O-poor conditions to hole polarons (𝜂 + ) under O-rich conditions. Nitrogen and fluorine can substitute readily for oxygen atoms under O-poor conditions, while fluorine can also incorporate in an interstitial configuration (F$^−_i$) under more O-rich conditions. Carbon and chlorine incorporation in PuO 2 will be very limited. We also evaluate the kinetic barriers for oxygen-related defects, which we find to diffuse readily when present. Our results provide valuable insights into the critical role and variable chemistry of point defects and impurities in PuO 2 , which in turn have important implications for the safe storage of the underlying metal layer. In short, exposure of freshly prepared plutonium to reactive nitrogen- and fluorine-containing contaminants should be avoided, while carbon- or chlorine-containing contaminants are less likely to incorporate readily into the oxide.

Materials science↗

DOE OSTI2024

Assessing Carrier Mobility, Dopability, and Defect Tolerance in the Chalcogenide Perovskite Ba Zr S 3

The chalcogenide perovskite Ba Zr S 3 has attracted much attention as a promising solar absorber for thin-film photovoltaics. Here we use first-principles calculations to evaluate its carrier transport and defect properties. We find that Ba Zr S 3 has a phonon-limited electron mobility of 37 cm 2 / V s, which is comparable to that in halide perovskites, but lower hole mobility of 11 cm 2 / V s. The defect computations indicate that Ba Zr S 3 is intrinsically n -type due to shallow sulfur vacancies, but that strong compensation by sulfur vacancies will prevent attempts to make it p -type. We also establish that Ba Zr S 3 shows some degree of defect tolerance, presenting only few low formation energy, deep intrinsic defects. Among the deep defects, sulfur interstitials are the dominant nonradiative recombination centers but exhibit a moderate capture coefficient. Our work highlights the material’s intrinsic limitations in carrier mobility and p -type doping, and suggests focusing on suppressing the formation of sulfur interstitials to achieve longer carrier lifetime. Published by the American Physical Society 2024

Yuan, Zhenkun (ORCID:0000000272005973)↗

DOE OSTI2026

Infrared thermography NDT for in-situ defect detection in sandwich composite panel manufacturing

Composite manufacturing presents numerous challenges, as defects can arise from various sources throughout the process. In sandwich composite structures, the integration of a foam core introduces additional complexity and increases the likelihood of defect formation like delamination. To mitigate these issues and reduce the risk of future structural failures, in-situ monitoring during manufacturing is essential. This study investigates infrared (IR) thermography as a non-destructive technique for detecting manufacturing defects in foam-core sandwich composite panels under thermally excited conditions representative of in-situ processing. A stationary FLIR A8590 IR camera (640 × 512 pixels, 30Hz, 17mm lens, 9 ft stand-off distance) was used to monitor prefabricated panels subjected to controlled external heating simulating compression molding and resin cure exotherm. Interlaminar delamination defects with characteristic sizes ranging from 0.25 × 0.25in² to 5 × 5in² produced measurable surface temperature depressions of approximately 4–10°C during transient cooling, exceeding the effective noise floor of the camera by more than two standard deviations. Thicker laminates exhibited prolonged defect detectability windows due to increased thermal diffusion time. In contrast, embedded Teflon inclusions generated weak thermal contrasts of ≤ 3°C, approaching the measurement noise floor, due to limited thermal property contrast with the surrounding glass fiber composite. These results establish quantitative detectability limits for stationary thermographic inspection of sandwich composite panels under manufacturing-representative thermal cycles.

Barakat, Abdallah [ORNL] (ORCID:0000000296141398)↗

DOE OSTI2026

Product Defect Detection System: SYSM- 5620 Final Project

Retail sales is a growing market estimated to up to seven percent year over year. With this growing market there is also a trend in growing rate of retail returns, estimated just last year at $\$$850 billion. Retail stores must ensure that products available for purchase remain safe, undamaged, and acceptable to customers throughout their time in the store. This job exists regardless of the specific solution used because stores are always responsible for preventing damaged or defective products from reaching customers and when they fail to this is categorized under operation inefficiencies which accounts for an estimated $\$$12 billion in returns. When defective items remain on the sales floor, stores may experience increased returns, reduced customer satisfaction, loss of customer trust, and potential safety concerns depending on the product type. As a result, the core job to be done is to identify defective products quickly, remove them from the sales floor before they are purchased, and preserve useful information about the defect so that the store can improve its handling, stocking, and supplier coordination over time. The need for a more reliable process is especially important in high volume retail environments where employees manage large numbers of products across many aisles, shelves, and storage areas. In these settings, manual inspection alone can be inconsistent and difficult to sustain at the individual item level. At the same time, broader retail trends continue to emphasize operational efficiency, product visibility, and improved customer experience, creating an opportunity for more automated and data driven defect detection methods.

42 ENGINEERING↗