Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “Point defects”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 19 records

Unusual Role of Point Defects in Perovskite Nickelate Electrocatalysts

Low-cost transition-metal oxide is regarded as a promising electrocatalyst family for an oxygen evolution reaction (OER). The classic design principle for an oxide electrocatalyst believes that point defect engineering, such as oxygen vacancies (VO .. ) or heteroatom doping, offers the opportunities to manipulate the electronic structure of material toward optimal OER activity. Oppositely, in this work, we discover a counterintuitive phenomenon that both VO .. and an aliovalent dopant (i.e., proton (H + )) in perovskite nickelate (i.e., NdNiO 3 (NNO)) have a considerably detrimental effect on intrinsic OER performance. Detailed characterizations unveil that the introduction of these point defects leads to a decrease in the oxidative state of Ni and weakens Ni-O orbital hybridization, which triggers the local electron-electron correlation and a more insulating state. Evidenced by first-principles calculation using the density functional theory (DFT) method, the OER on nickelate electrocatalysts follows the lattice oxygen mechanism (LOM). The incorporation of point defect increases the energy barrier of transformation from OO*(V O ) to OH*(V O ) intermediates, which is regarded as the rate-determining step (RDS). Furthermore, this work offers a new and significant perspective of the role that lattice defects play in the OER process.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Identification and Suppression of Point Defects in Bromide Perovskite Single Crystals Enabling Gamma‐Ray Spectroscopy

Abstract Methylammonium lead tribromide (MAPbBr 3 ) stands out as the most easily grown wide‐band‐gap metal halide perovskite. It is a promising semiconductor for room‐temperature gamma‐ray ( γ ‐ray) spectroscopic detectors, but no operational devices are realized. This can be largely attributed to a lack of understanding of point defects and their influence on detector performance. Here, through a combination of crystal growth design and defect characterization, including positron annihilation and impedance spectroscopy, the presence of specific point defects are identified and correlated to detector performance. Methylammonium (MA) vacancies, MA interstitials, and Pb vacancies are identified as the dominant charge‐trapping defects in MAPbBr 3 crystals, while Br vacancies caused doping. The addition of excess MABr reduces the MA and Br defects and so enables the detection of energy‐resolved γ ‐ray spectra using a MAPbBr 3 single‐crystal device. Interestingly, the addition of formamidinium (FA) cations, which converted to methylformamidinium (MFA) cations by reaction with MA + during crystal growth further reduced MA defects. This enabled an energy resolution of 3.9% for the 662 keV 137 Cs line using a low bias of 100 V. The work provides direction toward enabling further improvements in wide‐bandgap perovskite‐based device performance by reducing detrimental defects.

Ni, Zhenyi↗

Comprehensive characterization of irradiation induced defects in ceria: Impact of point defects on vibrational and optical properties

Validation of multiscale microstructure evolution models can be improved when standard microstructure characterization tools are coupled with methods sensitive to individual point defects. We demonstrate how electronic and vibrational properties of defects revealed by optical absorption and Raman spectroscopies can be used to compliment transmission electron microscopy (TEM) and x-ray diffraction (XRD) in the characterization of microstructure evolution in ceria under non-equilibrium conditions. Experimental manifestation of non-equilibrium conditions was realized by exposing cerium dioxide (CeO2) to energetic protons at elevated temperature. Two sintered polycrystalline CeO2 samples were bombarded with protons accelerated to a few MeVs. These irradiation conditions produced a microstructure with resolvable extended defects and a significant concentration of point defects. A rate theory (RT) model was parametrized using the results of TEM, XRD, and thermal conductivity measurements to infer point defect concentrations. An abundance of cerium sublattice defects suggested by the RT model is supported by Raman spectroscopy measurements, which show peak shift and broadening of the intrinsic T2g peak and emergence of new defect peaks. Additionally, spectroscopic ellipsometry measurements performed in lieu of optical absorption reveals the presence of Ce3+ ions associated with oxygen vacancies. This work lays the foundation for a coupled approach that considers a multimodal characterization of microstructures to guide and validate complex defect evolution models.

Chauhan, Vinay S.↗

pymatgen-analysis-defects: A Python package for analyzing point defects in crystalline materials

Point defects can often determine the properties of semiconductor and optoelectronic materials. Due to the large simulation cell and the higher-cost density functionals required for defect simulations, the computational cost of defect calculations is often orders of magnitude higher than that of bulk calculations. As such, managing and curating the results of the defect calculations generated by a single user has the potential to save a significant amount of computational resources. Moreover, eventually building a high-quality, persistent defects database will significantly reduce the computational cost of defect calculations for the entire community.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

Thermokinetics of point defects in α-Fe 2 O 3

Abstract Point defect formation and migration in oxides governs a wide range of phenomena from corrosion kinetics and radiation damage evolution to electronic properties. In this study, we examine the thermodynamics and kinetics of anion and cation point defects using density functional theory in hematite ( α -Fe 2 O 3 ), an important iron oxide highly relevant in both corrosion of steels and water-splitting applications. These calculations indicate that the migration barriers for point defects can vary significantly with charge state, particularly for cation interstitials. Additionally, we find multiple possible migration pathways for many of the point defects in this material, related to the low symmetry of the corundum crystal structure. The possible percolation paths are examined, using the barriers to determine the magnitude and anisotropy of long-range diffusion. Our findings suggest highly anisotropic mass transport in hematite, favoring diffusion along the c -axis of the crystal. In addition, we have considered the point defect formation energetics using the largest Fe 2 O 3 supercell reported to date.

36 MATERIALS SCIENCE↗

Revealing the Hidden Third Dimension of Point Defects in Two-Dimensional MXenes

Point defects govern many important functional properties of two-dimensional (2D) materials. However, resolving the three-dimensional (3D) arrangement of these defects in multi-layer 2D materials remains a fundamental challenge, hindering rational defect engineering. Here, we overcome this limitation using an artificial intelligence-guided electron microscopy workflow to map the 3D topology and clustering of atomic vacancies in Ti3C2TX MXene. Our approach reconstructs the 3D coordinates of vacancies across hundreds of thousands of lattice sites, generating robust statistical insight into their distribution that can be correlated with specific synthesis pathways. This large-scale data enables us to classify a hierarchy of defect structures-from isolated vacancies to nanopores-revealing their preferred formation and interaction mechanisms, as corroborated by molecular dynamics simulations. This work provides a generalizable framework for understanding and ultimately controlling point defects across large volumes, paving the way for the rational design of defect-engineered functional 2D materials.

2D materials↗

Point defect evolution under irradiation: Finite size effects and spatio-temporal correlations

The evolution of point defect concentrations under irradiation is controlled by their diffusion properties, and by their formation and elimination mechanisms. The latter includes the mutual recombination of vacancies and interstitials, and the elimination of point defects at sinks. Two models are traditionally used to predict the defect concentration evolution, the standard rate theory (SRT) and the atomistic kinetic Monte Carlo (AKMC). We show in this work a large discrepancy in the defect concentrations between both methods when the average number of defects in the AKMC simulation box is close or lower than one. The reason is that AKMC naturally captures strong space and time correlations between vacancies and interstitials generated by the finite size of the periodic simulation box. These correlations strongly affect the recombination rate and the point defect concentrations. SRT fails to predict such correlations, and the corresponding solution deviates from the more accurate solution given by the AKMC simulation under similar conditions. These finite size correlation effects are strong when the elimination of point defects occurs by recombination only, but can still be significant in the presence of sinks. In order to account for the spacio-temporal correlation in a continuum framework, we introduce a Correlated Pair Theory (CPT). This theory fully takes into account the correlations between vacancy and interstitial pairs and predicts point defect concentrations in good agreement with AKMC simulations. Inversely, if the goal is for the AKMC to reproduce defect concentrations in bulk using small simulation boxes, the naturally occurring correlations need to be corrected. We show here that the CPT can be used to modify the elimination rates in the AKMC simulations, so as to yield point defect concentrations in agreement with SRT.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

Using lifetime of point defects for dislocation bias in bcc Fe

The interaction between dislocations and point defects is key to deformation processes and microstructural evolution of structural materials. In this work, we compute the lifetime of point defects to describe their interaction with dislocations. This approach can accurately account for the effects of the dislocation core and anisotropic defect dynamics to accumulatively determine the capture efficiency, sink strength, and dislocation bias at different temperatures and dislocation densities. Particularly, the absorption of point defects by straight screw and edge dislocations in a model bcc iron system is studied. The maximum swelling rates based on the obtained bias factors are in close agreement with a variety of experimental measurements, including both neutron and ion-irradiation data, especially when considering the survival fraction for point defects from displacement cascades. Further, this approach applies to many other processes and sinks, such as dislocation loops and interfaces, providing a powerful means to develop fundamental insights critical for improving radiation resistance and mechanical properties of structural materials through controlling defect interaction and evolution.

36 MATERIALS SCIENCE↗

Incorporating point defect generation due to jog formation into the vector density-based continuum dislocation dynamics approach

During plastic deformation of crystalline materials, point defects such as vacancies and interstitials are generated by jogs on moving dislocations. A detailed model for jog formation and transport during plastic deformation was developed within the vector density-based continuum dislocation dynamics framework. Here as a part of this model, point defect generation associated with jog transport was formulated in terms of the volume change due to the non-conservative motion of jogs. Balance equations for the vacancies and interstitials including their rate of generation due to jog transport were also formulated. A two-way coupling between point defects and dislocation dynamics was then completed by including the stress contributed by the eigen-strain of point defects. A jog drag stress was further introduced into the mobility law of dislocations to account for the energy dissipation during point defects generation. A number of test problems and a fully coupled simulation of dislocation dynamics and point defects generation and diffusion were performed. The results show that there is an asymmetry of vacancy and interstitial generation due to the different formation energies of the two types of defects. The results also show that a higher hardening rate and a higher dislocation density are obtained when the point defect generation mechanism is coupled to dislocation dynamics.

36 MATERIALS SCIENCE↗

First-principles determination of the phonon-point defect scattering and thermal transport due to fission products in ThO 2

This work presents the first-principles calculations of the lattice thermal conductivity degradation due to point defects in thorium dioxide using an iterative solution of the Pierels-Boltzmann transport equation. We have used the non perturbative Green’s function methodology to compute the phonon- point defect scattering rates that consider the local distortion around the point defect, including the mass difference changes, interatomic force constants, and structural relaxation near the point defects. The point defects considered in this work include the vacancy of thorium ($V$ $TH$ ) and oxygen ($V$ $O$ ), substitutions of helium ($He$ $Th$ ), krypton ($Kr$ $Th$ ), zirconium ($Zr$ $Th$ ), iodine ($I$ $Th$ ) and xenon ($Xe$ $Th$ ) in the thorium site, and the three different configurations of the Schottky defects. The results of the phonon-defect scattering rate reveal that, among all the considered intrinsic defects, the thorium vacancy and helium substitution in the thorium site scatter the phonon most due to the substantial changes in the force constant and structural distortions. The scattering of phonons due to the substitutional defects unveils that the zirconium atom scatters phonons the least, followed by xenon, iodine, krypton, and helium. This is contrary to the intuition that the scattering strength follows $He$ $Th$ > $Kr$ $Th$ > $Zr$ $Th$ > $I$ $Th$ > $Xe$ $Th$ based on the mass difference. This striking difference in the zirconium phonon scattering is due to the local chemical environment changes. Zirconium is an electropositive element with valency similar to thorium and, therefore, can bond with the oxygen atoms, thus creating less force constant variance compared to iodine, an electronegative element noble gas helium, xenon, and krypton. In conclusion, these results can serve as the benchmark for the analytical models and help the engineering-scale modeling effort for nuclear design.

36 MATERIALS SCIENCE↗

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↗

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↗

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↗

Tuning Point Defects by Elastic Strain Modulates Nanoparticle Exsolution on Perovskite Oxides

Exsolution generates stable and catalytically active metal nanoparticles via phase precipitation out of a host oxide. An ability to control the size and dispersion of the exsolution particles is desirable for design of nanostructured (electro)catalysts. In this work, we demonstrate that tuning point defects by lattice strain affects both the thermodynamics and the kinetics of iron (Fe 0 ) exsolution on La 0.6 Sr 0.4 FeO 3 (LSF) thin film model. By combining in situ surface characterization and ab initio defect modeling, we show oxygen vacancy and Schottky defects to be the primary point defects formed upon Fe0 exsolution. Lattice strain tunes the formation energy, and thus the abundance of these defects, and alters the amount and size of the resulting exsolution particles. In addition, we find that the density of exsolved nanoparticles matches the concentration of oxygen vacancy pairs, thus pointing to the surface oxygen vacancy pairs as preferential nucleation sites for exsolution. The tensile-strained LSF with a facile formation of these critical point defects results in a higher Fe 0 metal concentration, a larger density of nanoparticles, and a reduced particle size at its surfaces. These results provide important mechanistic insights and highlight the role of point-defect engineering in designing nanostructured catalysts in energy and fuel conversion technologies.

36 MATERIALS SCIENCE↗

Identifying native point defect configurations in α-alumina

Intimately intertwined atomic and electronic structures of point defects govern diffusion-limited corrosion and underpin the operation of optoelectronic devices. For some materials, complex energy landscapes containing metastable defect configurations challenge first-principles modeling efforts. Here, we thoroughly reevaluate native point defect geometries for the illustrative case of α-Al 2 O 3 by comparing three methods for sampling candidate geometries in density functional theory calculations: displacing atoms near a naively placed defect, initializing interstitials at high-symmetry points of a Voronoi decomposition, and Bayesian optimization. We find symmetry-breaking distortions for oxygen vacancies in some charge states, and we identify several distinct oxygen split-interstitial geometries that help explain literature discrepancies involving this defect. We also report a surprising and, to our knowledge, previously unknown trigonal geometry favored by aluminum interstitials in some charge states. Importantly, these new configurations may have transformative impacts on our understanding of defect migration pathways in aluminum-oxide scales protecting metal alloys from corrosion. Overall, the Voronoi scheme appears most effective for sampling candidate interstitial sites because it always succeeded in finding the lowest-energy geometry identified in this study, although no approach found every metastable configuration. Finally, we show that the position of defect levels within the band gap can depend strongly on the defect geometry, underscoring the need to conduct careful searches for ground-state geometries in defect calculations.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

Revisiting point defect thermodynamics in group IVB and VB transition metal carbides

We present a comprehensive re-examination of point defect thermodynamics in group IVB and VB transition metal carbides (TMCs) with the rocksalt structure using a combination of density functional theory (DFT) calculations and a statistical mechanical Wagner-Schottky model within the canonical ensemble. The most stable configurations of point defects were discovered using basin-hopping global optimization, driven by either a machine learning interatomic potential (MLIP) or DFT. A key finding is the identification of previously unreported dicarbon antisites—a C–C dimer occupying a metal site—as the structural (constitutional) defects on the carbon-rich side of stoichiometry in all group IVB and VB TMCs except TaC. Furthermore, dicarbon antisite-containing thermal defect complexes, such as quadruple and interbranch defects, can dominate in TMCs under specific stoichiometric and temperature conditions. In conclusion, by incorporating dicarbon antisites into the defect landscape, this work provides a revised understanding of the thermodynamics of point defects in TMCs.

Carbides↗

Density functional theory calculations of the thermodynamic and kinetic properties of point defects in β -U

Density functional theory (DFT) calculations of the thermodynamic and kinetic properties of point defects in the β phase of uranium are reported. Defect energies and entropies were calculated using 2 x 2 x 2 supercells and the Generalized Gradient Approximation (GGA) of Perdew-Burke-Ernzerhof (PBE) for the exchange-correlation potential. Due to computational cost, calculations of the vibrational properties governing entropies were performed by only displacing atoms within (roughly) the 3rd or 4th nearest neighbor shell of the defect, which implicitly assumes that atoms beyond this distance are unaffected by the defect. Migration barriers were estimated by nudged elastic band (NEB) calculations. The low symmetry of the β-U phase (the unit cell is tetragonal and contains 30 atoms) results in many point defect configurations and even more migration pathways. A connectivity map, starting from the most stable point defects, was developed in order to identify the rate-limiting step controlling the net diffusion rate in each crystallographic direction. The uranium self-diffusivity tensor was calculated by combining the defect formation energies, entropies, migration barriers and attempt frequencies. The fastest diffusion rate was determined to be a vacancy mechanism in the z crystallographic direction. The predicted uranium self-diffusivity for this mechanism agrees well with available experimental data. Finally, the diffusion mechanisms and rates identified in this study will inform fuel performance models of swelling and gas evolution.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

Native point defects in HgCdTe infrared detector material: Identifying deep centers from first principles

We investigate the native point defects in the long-wavelength infrared (LWIR) detector material Hg0.75Cd0.25Te using a dielectric-dependent hybrid density functional combined with spin–orbit coupling. Characterizing these point defects is essential as they are responsible for intrinsic doping and nonradiative recombination centers in the detector material. The dielectric-dependent hybrid functional allows for an accurate description of the bandgap (Eg) for Hg1−xCdxTe (MCT) over the entire compositional range, a level of accuracy challenging with standard hybrid functionals. Our comprehensive examination of the native point defects confirms that cation vacancies VHg(Cd) are the primary sources of p-type conductivity in the LWIR material given their low defect formation energies and the presence of a shallow acceptor level (−/0) near the valence-band maximum. In addition to the shallow acceptor level, the cation vacancies exhibit a deep charge transition level (2−/−) situated near the midgap, characteristic of nonradiative recombination centers. Our results indicate that Hg interstitial could also be a deep center in the LWIR MCT through a metastable configuration under the Hg-rich growth conditions. While an isolated Te antisite does not show deep levels, the formation of VHg–TeHg defect complex introduces a deep acceptor level within the bandgap.

Physics↗