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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 487 records · Page 27
All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

Research on photoelectric properties of n-GaN (0001) surface with point defects via first-principles

In order to reveal the influence of n-type doping and point defects on the electronic and optical properties of GaN (0001) surface, the pristine and defective n-GaN (0001) surface models are established. The formation energy, work function, atomic structure, electronic and optical properties of these surfaces are discussed by first-principles calculations. The results show that the defects have the strong tendency to be close to the surface position, and the N interstitial defect (N{sub i}) and the Ga vacancy (V{sub Ga}) are easily formed. The work function of the surface with V{sub Ga} drops the greatest, and the conductivity of n-type increases. In addition, the absorption coefficient of the surface with various defects is smaller than that of the pristine n-type surface. The appropriate amount of Ga vacancies can increase the probability of electron emission, but it is not conducive to the absorption of photons.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2025

Kinetics of amorphous defect phases measured through ultrafast nanocalorimetry

Recognition of the role of extended defects on local phase transitions has led to the conceptualization of the defect phase, localized thermodynamically stable interfacial states that have since been applied in a myriad of material systems to realize significant enhancements in material properties. Here, we explore the kinetics of grain boundary confined amorphous defect phases, utilizing the high temperature and scanning rates afforded by ultrafast differential scanning calorimetry to apply targeted annealing/quenching treatments at high rates capable of capturing the kinetic behavior. Four Al-based nanocrystalline alloys, including two binary systems, Al–Ni and Al–Y, and two ternary systems, Al–Mg–Y and Al–Ni–Y, are selected to probe the materials design space (enthalpy of mixing, enthalpy of segregation, chemical complexity) for amorphous defect phase formation and stability, with correlative transmission electron microscopy applied to link phase evolution and grain stability to nanocalorimetry signatures. A series of targeted isothermal annealing heat treatments is utilized to construct a Time–Temperature-Transformation curve for the Al–Ni system, from which a critical cooling rate of 2400 °C/s was determined for the grain boundary confined disordered-to-ordered transition. Finally, a thermal profile consisting of 1000 repeated annealing sequences was created to quantify the recovery of the amorphous defect phase following sequential annealing treatments, with results indicating remarkable microstructural stability after annealing at temperatures above 90% of the melting temperature. This work contributes to a deeper understanding of grain boundary localized thermodynamics and kinetics, with potential implications for the design and optimization of advanced materials with enhanced stability and performance.

Amorphous defect phases↗
DOE OSTI2020

Atomic defects, functional groups and properties in MXenes

MXenes, a new family of functional two-dimensional (2D) materials, have shown great potential for an extensive variety of applications within the last decade. Atomic defects and functional groups in MXenes are known to have a tremendous influence on the functional properties. In this review, we focus on recent progress in the characterization of atomic defects and functional group chemistry in MXenes, and how to control them to directly influence various properties ( e.g ., electron transport, Li + adsorption, hydrogen evolution reaction (HER) activity, and magnetism) of 2D MXenes materials. Dynamic structural transformations such as oxidation and growth induced by atomic defects in MXenes are also discussed. The review thus provides perspectives on property optimization through atomic defect engineering, and bottom-up synthesis methods based on defect-assisted homoepitaxial growth of MXenes.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Constructing uniform oxygen defect engineering on primary particle level for high-stability lithium-rich cathode materials

Lithium-rich layered cathode materials are considered to be research focus of cathode candidates for next generation lithium-ion batteries due to their high specific capacity and low cost. However, lattice deoxidation associated with elemental migration and internal local shrinkage usually results in deteriorated cyclic performance and notorious voltage attenuation, severely limiting its application. In this paper, we have successfully injected uniform oxygen defects into surface region of Li 1.2 Ni 0.13 Co 0.13 Mn 0.54 O 2 primary particles under the high-pressure and weak carbonate environment. Various experimental investigations indicate that the injected robust oxygen defects can not only mitigate detrimental interfacial reactions but also suppress unfavorable lattice variation and particle breakage. More importantly, theoretical calculations unravel the critical roles of oxygen defects in regulating energy band structure for strengthened anionic reversibility. Owing to stabilization effects of unique oxygen defect engineering, the modified Li 1.2 Ni 0.13 Co 0.13 Mn 0.54 O 2 cathode has harvested dramatically enhanced electrochemical performance including a high initial coulombic efficiency of 96.6%, an outstanding capacity retention of 91.96% (1C, 200 cycles) and suppressed voltage decay of only 1.62 mV per cycle. Therefore, this facile and effective defect engineering strategy could establish new guidance for promoting practical application of Li-rich Mn-based cathode material.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2019

Electron-phonon coupling induced defect recovery and strain relaxation in Ni and equiatomic NiFe alloy

In this work, we study the effects of local heating due to electron-phonon coupling in cumulative 10 MeV Ni ion irradiation on pre-damaged pure Ni and equiatomic NiFe alloy. In particular, we estimate the defect recovery in NiFe compared to pure Ni due to the chemical disorder. Furthermore, the simulations are done using the two-temperature molecular dynamics approach that includes the electronic stopping and electron-phonon interactions. A total of 50% of the damage that consists of isolated point defects and small defect clusters is recovered in NiFe, whereas no noticeable recovery is observed in elemental Ni. We have also demonstrated strain relaxation associated with defect recombination based on the results from both simulation and X-ray diffraction measurements in NiFe. The damage recovery primarily depends on pre-existing damage level, defect configuration, ion energy (stopping powers) and electron-phonon coupling parameter of the materials of interest.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Reviewing computational studies of defect formation and behaviors in carbon fiber structural units

Solid-state carbon materials, such as graphite and graphene, are at the forefront of materials research because of their unique electronic, vibrational, and mechanical properties, leading to a broad range of potential and realized applications. One key application is their role as basic structural units of carbon fiber (CF), a lightweight alternative to steel. In CF, a delicate relationship exists between ultimate material strength and the atomic-scale density of defects contained at the inter- and intra-subunit levels. Computational studies provide insight into the stability of various types of defects that can form in these systems and connect with experimental observables such as bandgap and spectroscopic measurements. Therefore, the literature contains many computational studies that focus on changes induced by defects, including vacancies and Dienes transformations (Stone-Wales and Thrower defects). However, wide-ranging methods and cell sizes have been used, and property-specific information is often lacking. Furthermore, this review summarizes key literature findings, paying particular attention to changes in the electronic, vibrational, and mechanical properties induced by defects in graphitic materials relevant to CF.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Graph-component approach to defect identification in large atomistic simulations

In this work, the graph-theoretical concept of connected components is employed to extract the evolution of defect configurations in a polycrystalline aluminum structure containing ~8.3 million atoms. This graph-component approach is applied to reveal details of defect formation, transport, and transformation in the polycrystalline Al under large shear deformation. Building upon standard nearest neighbor analysis, graph theory and associated tools are used to reduce the multi-million-atom system into discrete component subgraphs that represent distinct structural defects. This method allows the automated identification, characterization, and tracking of defective regions within large volumes of data representing atomic-scale processes. Such analysis elucidates relationships between external stimuli, such as strain, and defect distributions, which have a large influence on material properties. The Graph Analytics for Large Atomistic Simulations (GALAS) codebase that implements this analysis, together with user guidance, is openly available at https://github.com/pnnl/galas.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Point defect energetics in gallium arsenide, a comprehensive density functional theory study

In materials, point defects often control or modify functional properties. To predict the performance of materials intended for application in optoelectronic devices, it is imperative to understand the properties of those point defects. For the first time, all six intrinsic defects of GaAs, a key optoelectronics material, and their charge transition levels are calculated using density functional theory with the HSE06 functional. For comparison, both PBE and r 2 SCAN calculations are also carried out. The HSE06 results are found to be in better agreement with experimental data than previous calculations. In conclusion, the importance of using the exact electron exchange present in hybrid functionals and larger supercells to accurately determine defect levels and ground state defect configurations is demonstrated.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Towards deep computer vision for in-line defect detection in polymer electrolyte membrane fuel cell materials

Polymer Electrolyte Membrane (PEM) fuel cells are a promising source of alternative energy. However, their production is limited by a lack of well-established methods for quality control of their constituent materials like the membrane-electrode assembly during roll-to-roll manufacturing. One potential solution is the implementation of deep learning methods to detect unwanted defects through their detection in scanned images. Here we explore the detection of defects like scratches, pinholes, and scuffs in a sample dataset of PEM optical images using two deep learning algorithms: Patch Distribution Modeling (PaDiM) for unsupervised anomaly detection and Faster-RCNN for supervised object detection. Both methods achieve scores on performance metrics (ROC-AUC and PRO-AUC for PaDiM and AP for Faster-RCNN) that are comparable to their scores on benchmark datasets. These methods also have the potential to detect a wider range of defects compared to IR thermography and previous optical inspection methods. Overall, deep learning shows promise at detecting relevant defects of interest and has the potential to achieve real-time defect detection.

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2022

Electronic structure and magnetism of pristine, defected, and strained $\mathrm{Ti_2N}$ $\mathrm{MX}$ene

Here, from first principles electronic structure calculations, we unravel the evolution of structural, electronic, and magnetic properties of pristine, defected, and strained titanium nitride MXene with different functional groups (-F, -O, -H, and -OH). The formation and cohesive energies reveal their chemical stability. The dynamical stability of Ti 2 N mono-layer is also confirmed by phonon calculations. The MAX phase and defect free functionalized MXenes are metallic except for oxygen terminated (Ti 2 NO 2 ) one which is 100% spin polarized half-metallic ferromagnet. The spin–orbit coupling significantly influences the bare MXene (Ti 2 N) to exhibit Dirac topology and band inversion near the high symmetry directions. The strain effect sways the Fermi level thereby shifting it towards lower energy state under compression and towards higher energy state under tensile strain in Ti 2 NH 2 . The Ti 2 NO 2 exhibits exotic electronic structure not only in pristine but also in strained and defected structures. Its half-metallic nature changes to semi-metallic under 1% compression and it is completely destroyed under 2% compression. In single vacancy defect, its band structure remarkably transforms from half-metallic to semi-conducting with large band gap in 12.5% Ti, weakly semi-conducting in 5.5% Ti, and semi-metallic in 12.5% O. The 25% N defect changes its half-metallic characteristic to metallic. Further, the 12.5% Co substitution preserves its half-metallic character, whereas Mn substitution allows it to convert half-metallic characteristic into weak semi-metallic characteristic preserving ferromagnetism. However, Cr substitution converts half-metallic ferromagnetic state to half-metallic anti-ferromagnetic state. The understanding made here on collective structural stability, and electronic band structure, and magnetic phenomena in novel 2D Ti 2 N derived MXenes open up their possibility in designing them for synthesis.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Molecular dynamics studies of lattice defect effects on tritium diffusion in zirconium

Tritium diffusion in α-Zr containing point defects such as vacancies or self-interstitial atoms (SIAs) is simulated using molecular dynamics. Point defects rapidly aggregate to form extended defects, such as 3D nanoclusters and Frank loops. The geometry of extended defects is affected by the presence of tritium. At low temperature and in the absence of tritium, vacancies aggregate to form stacking fault pyramids. Addition of tritium at these temperatures promotes aggregation of vacancies to form 3D nanoclusters, within which the tritium concentration can be sufficiently high to suggest that these defects may serve as nucleation sites for hydride precipitation. Trapping of tritium in vacancy nanocluster reduces the calculated bulk diffusivity by an amount proportional to the vacancy concentration. At high temperature, vacancy clusters change shape to form planar basal dislocation loops, which bind tritium less strongly, leading to a sharp reduction in the fraction of trapped tritium and a corresponding increase in tritium diffusivity at high temperature. In contrast, SIAs increase tritium diffusion through α-Zr. Analysis of atomic trajectories shows that tritium does not interact directly with SIAs. In conclusion, diffusion enhancement is instead related to expansion of the lattice.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Microstructurally informed synchrotron x-ray analysis revealing helium defect transitions in ultrafine grained tungsten

The formation of insoluble gaseous defects in materials due to nuclear transmutation or ion implantation involves the diffusion of impurity atoms to form atomic defect clusters that coalesce into bubbles or cavities and ultimately degrade the material properties. Transmission electron microscopy (TEM) is limited in its ability to resolve sub-nanometer gas clusters whereas X-ray diffraction (XRD) provides information pertaining to local atomic changes. Here, in this study, helium (He) implanted ultrafine grained tungsten is explored through a multimodal defect characterization campaign combining TEM-informed Small Angle X-ray Scattering (SAXS) analysis, XRD lattice parameter measurements, and nanoscale He cluster quantification from a region of reciprocal space accessible via Wide Angle X-ray Scattering (WAXS). Moderate elevated temperature implantations are shown to produce high concentrations of sub-nanoscale He clusters and small, homogeneously distributed cavities, which collectively are linked to lattice expansion and further substantiated through complementary atomistic simulations. Increased implantation temperatures encourage the diffusion of these defects to the grain boundaries (GBs), leading to lattice relaxation and the growth of large GB cavities manifesting as bimodal size distributions in the SAXS analysis. Overall, our results demonstrate the utility of multimodal synchrotron X-ray analysis in bridging the gap between microscale He cavity quantification and atomic-scale defect analysis.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Geometrical defect detection for additive manufacturing with machine learning models

This study proposed a scheme based on Machine Learning (ML) models to detect geometric defects of additively manufactured objects. The ML models are trained with synthetic 3D point clouds with defects and then applied to detect defects in actual production. Using synthetic 3D point clouds rather than experimental data could save a huge amount of training time and costs associated with many prints for each design. Besides distance differences of individual points between source and target point clouds, this scheme uses a new concept called “patch” to capture macro-level information about nearby points for ML training and implementation. Numerical comparisons of prediction results on experimental data with different shapes showed that the proposed scheme outperformed the existing Z-difference method in the literature. Five ML methods (Bagging of Trees, Gradient Boosting, Random Forest, K-nearest Neighbors and Linear Supported Vector Machine) were compared under various conditions, such as different point cloud densities and defect sizes. Bagging and Random Forest were found the two best models regarding predictability; and the right patch size was found to be at 20. The proposed ML-based scheme is applicable to in-situ defect detection during additive manufacturing with the aid of a proper 3D data acquisition system.

Additive manufacturing↗
DOE OSTI2024

Chemical composition based machine learning model to predict defect formation in additive manufacturing

With a goal of exploiting additive manufacturing to improve the manufacturing of existing reactor materials, we developed a chemical composition-based machine learning model to predict the printability of any given alloy in laser powder bed fusion (L-PBF) using experimental data from peer-reviewed literature. We defined printability as the ability to avoid defects like cracking, balling, porosity, and lack of fusion, that are caused by thermal stresses (during solidification or liquation), molten pool disintegration into disconnected small beads or lack of heat input respectively. Our models predict the tendency of balling defect formation and porosity percentage for a given composition, under a given set of processing conditions. To predict the likelihood of balling defect, three models: a random forest classifier, a gradient boost regressor and a neural network were trained on a dataset containing both traditional alloys and high entropy alloys. The neural network model showed the highest accuracy of 92.3 % in predicting the balling defect formation. A random forest regressor, gradient boost regressor and neural network were trained and tested on a dataset of various alloys to predict porosity. The random forest regressor showed the best predictions with an R 2 score of 0.97. The models also revealed the relative importance of the input descriptors on defect-formation tendency. Of particular significance was the identification of carbon as an important element in determining the occurrence of balling and percent porosity in alloys like steel, as well as being moderately important to the percentage porosity in other alloys as well as steel. Manganese was also identified as a key descriptor for the percentage of porosity in steel and other alloys. Manganese’s low thermal conductivity and consistent presence in the dataset is the likely cause for its contribution. Carbon’s role is attributable to its relatively high specific heat and high melting temperature. In conclusion, our model serves as a swift, chemistry-based tool to design experiments and find modified compositions better suited for additive manufacturing.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Nature of Moisture-Induced fogging defects in scintillator plastic

Polyvinyl toluene (PVT) scintillator plastic may degrade in the field due to inward water diffusion at elevated temperatures, which exceeds the saturation limit at lower temperatures, and after long periods of time (e.g., many years), leads to the formation of disk-like defects that attenuate scintillation light, leading to detector degradation. In this work, using fractography and high-magnification optical and electron microscopy to characterize the water-induced defects, a model of the fogging process is hypothesized as follows: excess water present at low temperatures diffuses to spheroids to minimize contact with the hydrophobic polymer. Through the hydrophobic effect, the entropy of water increases by forming nanoclusters which minimizes the contact between the water and the PVT. As the water nanoclusters grow, they break and fold the polymer into densely-packed crystalline regions creating more space for water within the spheroids. The polymer outside the spheroid resists the shrinkage which builds up tension within the spheroid. Once the tensile stress exceeds the yield strength of the plastic, the spheroid is torn in half resulting in a defect. Excess water then drains into the cavities along the disk thereby further increasing its entropy. Slower cooling (over 1 day) leads to larger spheroids and hence, larger “permanent” defects. Freezing causes some defects to further grow due to the expansion of water to ice. These findings imply that the remaining lifetime of scintillator plastic in the field could be predicted using temperature and humidity data thereby mitigating security risks of degraded radiation portal monitors.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Universal machine learning framework for defect predictions in zinc blende semiconductors

Our article introduces a universal predictive framework for point defect formation energies and charge transition levels in a wide chemical space of zinc blende semiconductors and possible impurity atoms selected from across the periodic table. This framework was developed by leveraging high-throughput quantum mechanical simulations benchmarked using some experimental data from the literature, as well as machine learning (ML)-based regressions techniques that map unique materials descriptors to computed defect properties and yield optimized and generalizable models. Furthermore, the power and utility of these models is revealed through quick predictions for thousands of new defects and screening of low-energy impurities, which may tune the equilibrium conductivity in the semiconductor. This work presents, to our knowledge, the largest density functional theory (DFT) dataset of defect properties in semiconductors and the largest DFT+ML-based screening of point defects in semiconductors to date.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Rapid Crystallization of Zeolites with Controllable Defects: Disentangling Fluoride Concentration and pH Using NH 4 F

Zeolite synthesis is typically conducted either under basic conditions or in neutral fluoride media using hydrofluoric acid (HF). While basic (OH − ) conditions generally result in faster zeolite crystallization, they can also increase the likelihood of framework defects and crystal intergrowths. In contrast, synthesis in neutral fluoride media tends to produce fewer defects because fluoride balances positive charges from structure-directing agents. However, this method often requires significantly longer crystallization times and involves the handling of dangerous HF. In the present study, we pursue the best of both synthesis conditions, rapid syntheses with controllable defect concentrations, by disentangling of mineralizing agent and chargebalancing agent using ammonium fluoride (NH 4 F) as an alternative to HF. We have investigated the use of NH 4 F in the syntheses of siliceous and aluminum-containing zeolite A (LTA, small pore), ZSM-5 (MFI, medium pore), and siliceous Beta(*BEA, large pore). The crystallization times of all four zeolites decreased substantially with an increasing NH 4 F concentration. Crystallization times were reduced from 24 to 4 h (Si-LTA), 96 to 36 h (Al-LTA), 240 to 6 h (ZSM-5), and 24 to 3 h (Si-*BEA). Additionally, increasing the NH 4 F concentration in the synthesis mixtures decreases the defect densities of siliceous zeolites. Raman spectroscopy, along with 29 Si MAS NMR, 19 F MAS NMR, 13 C MAS NMR, and fluorine elemental analysis of Si-LTA samples confirms that the reduction in charged defects (Si−O − ) is due to the higher incorporation of F − within the double four-membered ring (D4R) present in the LTA samples. We show that the accelerated crystallization is due to the role of F − in enhancing the silica mineralization rate (formation of silicon hexafluoride species) and stabilizing D4Rs under basic conditions. As a result, combining basic and fluoride-mediated synthesis could therefore be advantageous for faster zeolite production and improved control over structural properties for a wide variety of zeolite structures.

Anions↗
DOE OSTI2025

Defect Diffusion Graph Neural Networks for Materials Discovery in High-Temperature Energy Applications

Here, the migration of crystallographic defects dictates material properties and performance for a plethora of technological applications. Density functional theory (DFT)-based nudged elastic band (NEB) calculations are a powerful computational technique for predicting defect migration activation energy barriers, yet they become prohibitively expensive for high-throughput screening of defect diffusivities. Without introducing hand-crafted (i.e., chemistry- or structure-specific) descriptors, we propose a generalized deep learning approach to train surrogate models for NEB energies of vacancy migration by hybridizing graph neural networks with transformer encoders and simply using pristine host structures as input. With sufficient training data, computationally efficient and simultaneous inference of vacancy defect thermodynamics and migration activation energies can be obtained to compute temperature-dependent vacancy diffusivities and to down-select candidates for more thorough DFT analysis or experiments. Thus, as we specifically demonstrate for potential water-splitting materials, candidates with desired defect thermodynamics, kinetics, and host stability properties can be more rapidly targeted from open-source databases of experimentally validated or hypothetical materials.

14 SOLAR ENERGY↗