Optical properties of bulk semiconductors and graphene/boron nitride: The Bethe-Salpeter equation with derivative discontinuity-corrected density functional energies
Explore the source record for details and available documents.
SEARCH · Engineering Papers
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.
Explore the source record for details and available documents.
Explore the source record for details and available documents.
Explore the source record for details and available documents.
Explore the source record for details and available documents.
Explore the source record for details and available documents.
Explore the source record for details and available documents.
Explore the source record for details and available documents.
Abstract not provided.
Abstract not provided.
Explore the source record for details and available documents.
Explore the source record for details and available documents.
Abstract A dual oxidative approach using O 2 plasma followed by treatment with H 2 O 2 to impart oxygen functional groups onto the surface of a graphite felt electrode. When used as electrodes for an all‐vanadium redox flow battery (VRB) system, the energy efficiency of the cell is enhanced by 8.2 % at a current density of 150 mA cm −2 compared with one oxidized by thermal treatment in air. More importantly, by varying the oxidative techniques, the amount and type of oxygen groups was tailored and their effects were elucidated. It was found that O−C=O groups improve the cells performance whereas the C−O and C=O groups degrade it. The reason for the increased performance was found to be a reduction in the cell overpotential after functionalization of the graphite felt electrode. This work reveals a route for functionalizing carbon electrodes to improve the performance of VRB cells. This approach can lower the cost of VRB cells and pave the way for more commercially viable stationary energy storage systems that can be used for intermittent renewable energy storage.
We present a Fourier neural network (FNN) that can be mapped directly to the Fourier decomposition. The choice of activation and loss function yields results that replicate a Fourier series expansion closely while preserving a straightforward architecture with a single hidden layer. The simplicity of this network architecture facilitates the integration with any other higher-complexity networks, at a data pre- or postprocessing stage. We validate this FNN on naturally periodic smooth functions and on piecewise continuous periodic functions. We showcase the use of this FNN for modeling or solving partial differential equations with periodic boundary conditions. The main advantages of the current approach are the validity of the solution outside the training region, interpretability of the trained model, and simplicity of use.
Metal-organic frameworks (MOFs) have recently gained wide interest as candidate materials for nuclear waste immobilization. While the fundamental thermodynamic properties, such as the substitution energies determine the favorability of radionuclide sequestration by utilization of a MOF matrix, the studies of MOF electronic structure reveal the role of d-, and/or f-electrons on changes in physical properties of actinide-containing materials. We use density functional theory (DFT) calculations to investigate the electronic structures of Zr-, Th-, and U-MOFs, including their electronic band structures and, where appropriate, their magnetic properties. We employ various DFT methods including DFT+U, collinear spin-polarization, spin-orbit coupling, and different flavors of exchange-correlation functionals to assess the robustness to the specific exchange-correlation functional. Unlike the Zr-, and Th-MOFs, the U-MOF is found to be sensitive to electron localization and spin; hence we explore the magnetic structure of the U-MOF in further detail.
Explore the source record for details and available documents.
Surface coating has been identified as an effective approach for enhancing the capacity retention of layered structure cathode. However, the underlying operating mechanism of such a thin coating layer, in terms of surface chemical functionality and capacity retention, remains unclear. In this work, we use aberration-corrected scanning transmission electron microscopy and high-efficiency spectroscopy to probe the delicate functioning mechanism of an Al2O3 coating layer on a Li1.2Ni0.2Mn0.6O2 cathode. We discovered that in terms of surface chemical function, the Al2O3 coating suppresses the side reaction between the cathode and the electrolyte during battery cycling. At the same time, the Al2O3 coating layer also eliminates the chemical reduction of Mn from the cathode particle surface, therefore preventing the dissolution of the reduced Mn into the electrolyte. In terms of structural stability, we found that the Al2O3 coating layer can mitigate the layer to spinel phase transformation, which otherwise will be initiated from the particle surface and propagate toward the interior of the particle with the progression of battery cycling. The atomic to nanoscale effects of the coating layer observed here provide insight into the optimized design of a coating layer on a cathode to enhance the battery properties.