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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 559 records · Page 31
All sourcesNASA NTRSDOE OSTI
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 OSTI2017

Photocatalytic activity of ZnO nanoparticles with optimization of defects

Graphical abstract: Systematic scheme for photocatalytic activity. Pristine and air quenched ZnO nanoparticles were effectively synthesized by simple combustion method and effectively used as photocatalyst for photodegradtion of methyl orange dye. It was observed that air quenched ZnO nanoparticles shows the better properties as compared to pristine ZnO. The complete degradation of dye was observed in 150 min. - Highlights: • Pristine and air quenched ZnO nanoparticles were synthesized by simple combustion method. • Synthesized ZnO for air quenched ZnO has better optoelectronic properties. • ZnO nanoparticles for air quenched ZnO exhibiting complete photodegradation of Methyl Orange dye under UV irradiation. • Photocatalytic activities for all the samples were observed in 150 min. - Abstract: Tuning of defect levels is the major concern for the application of ZnO nanoparticles. These nanoparticles with different defect concentration were prepared by simple combustion method at 700 °C and subsequent quenching in air. The catalyst was characterized by X-Ray Diffraction (XRD), Field Emission Scanning Electron Microscope (FESEM), UV–vis spectroscopy measurements. XRD reveals that the cell volume increased with large temperature difference between 700 °C and quenched media. FTIR confirmed that the vibrational frequencies shifted from 519 cm{sup −1} to 535 cm{sup −1} point out the role of quenching defects. FESEM shows the size is of the order of 20–45 nm and found irregular distribution of grain boundaries in the quenched induced defects. Different morphology plays a major role in the enhancement of photocatalytic efficiency of synthesized nanoparticles. PL intensity indicates more oxygen vacancies for air quenched ZnO whereas; DTA study demonstrates that the synthesized ZnO is thermally stable. Kinetics study confirmed the photodegradation of methyl orange (MO) dye using this photocatalyst follows first order rate constant. It exhibits the complete degradation of dye only in 150 min under UV irradiation. Kinetic study revealed the rate constant of nanoparticles is 0.0165 min{sup −1}. Air quenched sample showed the higher intensity of defect band as compared to pristine ZnO.

36 MATERIALS SCIENCE↗
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↗
DOE OSTI2026

Shifting Defect Self-Regulation via Disordered Vacancies in Hollow Tin Perovskites

Tin(II)-based hybrid halide perovskites typically suffer from severe self-doping behavior as a result of facile oxidation of Sn(II) to Sn(IV), leading to high carrier densities (holes) and metallic-like conductivities that limit their applications. In this contribution, we describe how substituting the large ethylenediammonium cation for methylammonium in the intentionally defective “hollow” perovskite family, MA 1−x en x Sn 1−0.7x I 3−0.4x (MA = methylammonium, en = ethylenediammonium), where 0 ≤ x ≤ 0.38, effectively minimizes the intrinsic self-doping behavior. The use of a solvent-free, mechanochemical synthesis route further circumvents oxidative side reactions typical in solution processing, enabling more precise control and understanding of both composition and defect chemistry. Dark and time-resolved microwave conductivity measurements of these materials as a function of “x” reveal two regimes of conductivity suppression: at low x incorporation (x ≤ 0.15), the carrier density decreases by an order of magnitude via defect-mediated charge compensation, while higher substitution (0.15 < x ≤ 0.38) greatly reduces the carrier mobility. At these lower substitution levels, the observations suggest that intrinsic equilibrium tin vacancies are compensated instead by ionic defects in lieu of mobile holes. For the higher substitution levels, the less mobile carriers exhibit long recombination lifetimes, consistent with polaron-mediated transport. These findings establish a strategy for relatively low iodine chemical potential synthesis and defect-driven control of the carrier concentration in tin halide perovskites, advancing the rational discovery of dopable hybrid semiconductors.

36 MATERIALS SCIENCE↗
DOE OSTI2017

Influence of Structural Defects on Biomineralized ZnS Nanoparticle Dissolution: An In-Situ Electron Microscopy Study

The dissolution of metal sulfides, such as ZnS, plays an important role in the fate of metal contaminants in the environment. Here we have examined the dissolution behavior of ZnS nanoparticles synthesized via several abiotic and biological pathways. Specifically, the biogenic ZnS nanoparticles were produced by an anaerobic, metal-reducing bacterium Thermoanaerobacter sp. X513 in a Zn-amended, thiosulfate-containing growth medium, whereas the abiogenic ZnS nanoparticles were produced by mixing an aqueous Zn solution with either H 2 S-rich gas or Na 2 S solution. For biogenic synthesis, we prepared two types of samples, in the presence or absence of trace silver (Ag). The size distribution, crystal structure, aggregation behavior, and internal defects of the synthesized ZnS nanoparticles were primarily examined using high-resolution transmission electron microscopy coupled with X-ray energy dispersive spectroscopy. The characterization results show that both the biogenic and abiogenic samples were dominantly composed of sphalerite. In the absence of Ag, the biogenic ZnS nanoparticles were significantly larger (i.e., ~10 nm) than the abiogenic ones (i.e., ~3–5 nm) and contained structural defects (e.g., twins and stacking faults). The presence of trace Ag showed a restraining effect on the particle size of the biogenic ZnS, resulting in quantum-dot-sized nanoparticles (i.e., ~3 nm). In situ dissolution experiments for the synthesized ZnS were conducted with a liquid-cell coupled to a transmission electron microscope (LCTEM), and the primary factors (i.e., the presence or absence structural defects) were evaluated for their effects on the dissolution behavior using the biogenic and abiogenic ZnS nanoparticle samples with the largest average particle size. Analysis of the dissolution results (i.e., change in particle radius with time) using the Kelvin equation shows that the defect-bearing biogenic ZnS nanoparticles (γ = 0.799 J/m 2 ) have a significantly higher surface energy than the abiogenic ZnS nanoparticles (γ = 0.277 J/m 2 ), suggesting that larger defect-bearing ZnS nanoparticles may be more reactive than the smaller quantum-dot-sized ZnS nanoparticles. These findings provide new insight into the factors that govern the dissolution of metal sulfide nanoparticles in relevant natural and engineered scenarios, and have implication for tracking the fate of zinc at contaminated sites. Moreover, our study exemplified the use of an in situ method (i.e., LCTEM) to investigate nanoparticle behavior (e.g., dissolution) in aqueous solutions.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Characterizing Defect Dynamics in Silicon Carbide Using Symmetry-Adapted Collective Variables and Machine Learning Interatomic Potentials

Silicon carbide (SiC) divacancies are attractive candidates for spin-defect qubits possessing long coherence times and optical addressability. The high activation barriers associated with SiC defect formation and motion pose challenges for their study by first-principles molecular dynamics. In this work, we develop and deploy machine learning interatomic potentials (MLIPs) to accelerate defect dynamics simulations while retaining ab initio accuracy. We employ an active learning strategy comprising symmetry-adapted collective variable discovery and enhanced sampling to compile configurationally diverse training data, calculation of energies and forces using density functional theory (DFT), and training of an E(3)-equivariant MLIP based on the Allegro model. Here, the trained MLIP reproduces DFT-level accuracy in defect transition activation free energy barriers, enables the efficient and stable simulation of multidefect 216-atom supercells, and permits an analysis of the temperature dependence of defect thermodynamic stability and formation/annihilation kinetics to propose an optimal annealing temperature to maximally stabilize VV divacancies.

Computer simulations↗
DOE OSTI2020

Locally Engineering and Interrogating the Photoelectrochemical Behavior of Defects in Transition Metal Dichalcogenides

Transition metal dichalcogenides (TMDs) are attractive materials for a variety of applications in solar energy conversion and electrocatalysis, due to their favorable optical and electrical properties and their unique two-dimensional structures which facilitate the fabrication of wide-area, ultrathin layers. Unfortunately, the basal planes which make up the majority of these materials are relatively inert, and thus a great deal of effort has been directed to engineering favorable, catalytically active defects into these materials. In this paper, we demonstrate how probe-based electrochemical techniques can be employed as multifunctional tools for locally modifying TMD materials and probing the electrochemical behavior of the resulting defects. Scanning Electrochemical Cell Microscopy (SECCM) was employed to locally anodize exfoliated p-type WSe 2 nanosheets, creating hole-like defects within individual basal planes in a highly controllable fashion. Photoelectrochemical SECCM imaging was then employed to characterize the chemical behavior of these engineered defects, revealing significantly enhanced activity toward the Hydrogen Evolution Reaction (HER). Atomic force microscopy studies are presented which suggest these enhancements result from an increased density of monolayer-high step features within the anodized defects. Analysis of the SECCM data in the context of finite element simulations revealed that these enhancements increased with increasing anodization time, with local kinetic rates over 2 orders of magnitude higher than unaltered basal planes.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗