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At least 145 records · Page 8

Carbon, oxygen and their interaction with intrinsic point defects in solar silicon ribbon material: A speculative approach

Some background information on intrinsic point defects is provided and on carbon and oxygen in silicon in so far as it may be relevant for the efficiency of solar cells fabricated from EFG ribbon material. The co-precipitation of carbon and oxygen and especially of carbon and silicon self interstitials are discussed. A simple model for the electrical activity of carbon-self-interstitial agglomerates is presented. The self-interstitial content of these agglomerates is assumed to determine their electrical activity and that both compressive stresses (high self-interstitial content) and tensile stresses (low self-interstitial content) give rise to electrical activity of the agglomerates. The self-interstitial content of these carbon-related agglomerates may be reduced by an appropriate high temperature treatment and enhanced by a supersaturation of self-interstitials generated during formation of the p-n junction of solar cells. Oxygen present in supersaturation in carbon-rich silicon may be induced to form SiO, precipitates by self-interstitials generated during phosphorus diffusion. It is proposed that the SiO2-Si interface of the precipates gives rise to a continuum of donor stables and that these interface states are responsible for at least part of the light inhancement effects observed in oxygen containing EFG silicon after phosphorus diffusion.

Goesele, U.↗

Detection of defects in atomic-resolution images of materials using cycle analysis

The automated detection of defects in high-angle annular dark-field Z-contrast (HAADF) scanning-transmission-electron microscopy (STEM) images has been a major challenge. Here, we report an approach for the automated detection and categorization of structural defects based on changes in the material’s local atomic geometry. The approach applies geometric graph theory to the already-found positions of atomic-column centers and is capable of detecting and categorizing any defect in thin diperiodic structures (i.e., “2D materials”) and a large subset of defects in thick diperiodic structures (i.e., 3D or bulk-like materials). Despite the somewhat limited applicability of the approach in detecting and categorizing defects in thicker bulk-like materials, it provides potentially informative insights into the presence of defects. The categorization of defects can be used to screen large quantities of data and to provide statistical data about the distribution of defects within a material. This methodology is applicable to atomic column locations extracted from any type of high-resolution image, but here we demonstrate it for HAADF STEM images.

36 MATERIALS SCIENCE↗

Charged Hole‐Transporting Materials Based on Imidazolium for Defect Passivation in Inverted Perovskite Solar Cells

The hole‐transporting material (HTM) in perovskite solar cells (PSCs) provides an ideal interface with the perovskite layer for hole collection while influencing the perovskite crystalline structure and morphology. This article presents SIM and DIM, two new dopant‐free imidazolium‐based HTMs with charged units accompanied by counter ions that show the potential to function as ionic HTMs and ionic liquid additives and help improve the efficiency and stability of inverted PSCs. DIM‐based PSCs demonstrates exceptional performance with a high‐power conversion efficiency of 19.15%, attributed to improved passivation of perovskite interface defects. Furthermore, these new imidazolium salt‐based HTMs can be manufactured economically, making them a viable option for mass production and PSC commercialization.

Tingare, Yogesh S.↗

Oxygen vacancy induced site-selective Mott transition in LaNiO 3

While defects such as oxygen vacancies in correlated materials can modify their electronic properties dramatically, understanding the microscopic origin of electronic correlations in materials with defects has been elusive. Lanthanum nickelate with oxygen vacancies, LaNiO 3 - x , exhibits the metal-to-insulator transition as the oxygen vacancy level x increases from the stoichiometric LaNiO 3 . In particular, LaNiO 2.5 exhibits a paramagnetic insulating phase, also stabilizing an antiferromagnetic state below T N ≃ 152 K . Here, we study the electronic structure and energetics of LaNiO 3 - x using first principles. We find that LaNiO 2.5 exhibits a “site-selective” paramagnetic Mott insulating state at T ≃ 290 K as obtained using density functional theory plus dynamical mean field theory ( DFT + DMFT ). The Ni octahedron site develops a Mott insulating state with strong correlations as the Ni e g orbital is half-filled while the Ni square-planar site with apical oxygen vacancies becomes a band insulator. Overall, our oxygen vacancy results cannot be explained by the pure change of the Ni oxidation state alone within the rigid band-shift approximation. Our DFT + DMFT density of states explains that the peak splitting of unoccupied states in LaNiO 3 - x measured by the experimental x-ray absorption spectra originates from two nonequivalent Ni ions in the vacancy-ordered structure.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

Vacancy-Dependent Diffusion Mechanism in Oxygen-Defective SrFeO 3 Perovskite Materials: First-Principles Density Functional Theory and Experimental Approach

Understanding oxygen diffusion at the atomic scale in SrFeO 3−δ perovskites is crucial for developing oxygen storage materials with optimal performance. Such materials are required to have high stability, corrosion resistance, and acceptable oxygen storage capacity at moderate operating temperatures and pressures. Here, in this study, we used first-principles density functional theory and thermogravimetric analysis to study the vacancy-dependent oxygen diffusion in oxygen-deficient SrFeO 3−δ (δ = 0, 0.065, 0.125, 0.25, 0.5) perovskites. The electronic structures, including the partial- and spin-resolved density of states, for different SrFeO 3−δ phases were calculated and compared with available experimental and theoretical results. By mapping the migration pathways, we investigated diffusion mechanisms and calculated the energy barriers for oxygen diffusion in cubic, orthorhombic, and brownmillerite phases of SrFeO 3−δ perovskites. Using the calculated energy barriers, we deduced the diffusion time scales and diffusion coefficients within SrFeO 3−δ . A diffusion coefficient on the order of 10 –8 m 2 /s was obtained for SrFeO 2.875 . We experimentally investigated the roles of temperature and oxygen partial pressures on the redox kinetics and deduced the kinetics rate and diffusion density, which agreed well with the calculated values for the density of diffusing oxygen vacancy in the lattice. Our results showed that the energy barrier tends to reduce at higher oxygen concentrations. Our results serve as an important guideline for designing oxygen storage materials with optimal redox kinetics.

chemical looping with oxygen uncoupling (CLOU)↗

Nanoindentation mapping defects filtration for heterogeneous materials using generative adversarial networks

Advanced composite materials with multiple phases and heterogeneous microstructure necessitate spatial mapping characterization of elastic modulus to develop constitutive relations and overall mechanical response. Such modulus mapping can be obtained using the nanoindentation technique, where the indenter tip raster over the selected microstructure region. Typically, a surface preparation procedure is done in the specimens to ensure proper contact between the indenter tip and sample surface. However, a near-perfect surface finish is unachievable in heterogeneous materials, primarily with ceramic reinforcements, due to the differential material removal rate during polishing. Thus, the nanoindenter records localized erroneous measurements due to differences in surface roughness and corresponding force response. This study establishes a novel deep learning-based strategy to rectify incorrect experimental spatial measurements acquire during nanoindentation modulus mapping. Here, the integrated bicubic interpolation and generative adversarial networks (GANs) model was trained using 14 ceramic and 18 metallic data sets, each comprising 65,536 measurements. The developed algorithm was validated against experimental measurements on four unknown specimens. The standard deviation in measured elastic modulus reduces by ~50% in ceramics and ~72% in metallic samples. This computational framework proposes a novel approach to reducing uncertainty in materials’ properties using state-of-the-art computer vision techniques.

36 MATERIALS SCIENCE↗

STEM Imaging of Materials: Final Report for DE-FG02-08ER46547

This project supported research advancing the state of the art in scanning transmission electron microscopy (STEM) imaging and characterization of materials, especially electronic materials. The project began with studies of point defects in materials using high-resolution Z-contrast STEM, then evolved to including methods in four-dimensional (4D) STEM and machine learning. Highlights include: (1) the discovery of stable, p-type ZnO via Sb doping, (2) demonstration that the high performance of InGaN LEDs is not due to In composition fluctuations as previously proposed, (3) the demonstration of the first sub-pm precision high-resolution STEM images using non-rigid registration of a series of STEM images, (4) the extension of high-precision STEM to low dose imaging and spectrum imaging, (5) discovery of a synthesis method for twisted spiral growth of transition-metal dichalcogenide materials via chemical vapor deposition, and (6) development of machine-learning methods for analysis and denoising of enormous 4D STEM data sets.

36 MATERIALS SCIENCE↗

Environmental effects on defect growth in composite materials

Data for evaluating the effects of moisture and temperature on the integrity of fiber composite components was gathered. In particular, the static and cyclic performance of three composite laminates containing flaws was investigated at room temperature and at 422 K (300 F) in wet and dry conditions.

Porter, T. R.↗

Measurement and Simulation of Vacancy Formation in 2-MeV Self-irradiated Pure Fe

Positron annihilation spectroscopy is a powerful tool to quantify the amount of vacancies and vacancy clusters in materials. The technique has been utilized to study the induced defects in materials after ion beam and neutron irradiations. Here we make the case for how the technique can and should be utilized to quantify the defects created by irradiation in situ during irradiation to foster a more thorough understanding of the surviving defects after initial collision cascades. This paper outlines a future experimental approach and its meaning for the nuclear materials community, being able to benchmark commonly used rate theory models of damage evolution.

36 MATERIALS SCIENCE↗

Materials Studies of Niobium Thin Films for Quantum Circuit Applications: Progress and Challenges

Niobium (Nb) films have emerged as a crucial material in the development of superconducting qubits, which are key components in quantum computing technology. Here, this review provides a comprehensive examination of Nb films from a materials perspective, focusing on their intrinsic properties, fabrication methods/techniques, and their influence on qubit performance, particularly through surface and interface driven loss mechanisms. We discuss the key material properties that are essential for qubit operation. Various deposition techniques for Nb thin films, such as sputtering, evaporation, molecular beam epitaxy, and atomic layer deposition, are explored, alongside their impact on film quality, uniformity, and qubit performance. Additionally, the influence of surface roughness, thin-film thickness, and substrate materials on quantum coherence is analyzed. Challenges such as defects and material degradation in Nb films are reviewed, along with strategies to mitigate these issues. Finally, we present the latest advancements and future directions in Nb film research, including potential improvements to enhance qubit coherence and scalability for large-scale quantum computing systems. Ultimately, a deeper understanding of surface and interface phenomena is essential for pushing the limits of qubit performance and realizing next-generation quantum technologies.

77 NANOSCIENCE AND NANOTECHNOLOGY↗