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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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Effectiveness of point-of-use and pitcher filters at removing lead phosphate nanoparticles from drinking water
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Environmental Risk of Arsenic Mobilization from Disposed Sand Filter Materials
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Fermionic Reduced Density Low-Rank Matrix Completion, Noise Filtering, and Measurement Reduction in Quantum Simulations
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A Multiple Filter Based Neural Network Approach to the Extrapolation of Adsorption Energies on Metal Surfaces for Catalysis Applications
Computational catalyst discovery involves the development of microkinetic reactor models based on estimated parameters determined from density functional theory (DFT). For complex surface chemistries, the number of reaction intermediates can be very large, and the cost of calculating the adsorption energies by DFT for all surface intermediates even for one active site model can become prohibitive. In this paper, we have identified appropriate descriptors and machine learning models that can be used to predict a significant part of these adsorption energies given data on the rest of them. Furthermore, our investigations also included the case when the species data used to train the predictive model are of different size relative to the species the model tries to predict—this is an extrapolation in the data space which is typically difficult with regular machine learning models. Due to the relative size of the available data sets, we have attempted to extrapolate from the larger species to the smaller ones in the current work. Here, we have developed a neural network based predictive model that combines an established additive atomic contribution based model with the concepts of a convolutional neural network that, when extrapolating, achieves a statistically significant improvement over the previous models.
Highly Conductive Topologically Chiral Molecular Knots as Efficient Spin Filters
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Five-second STEM dislocation tomography for 300 nm thick specimen assisted by deep-learning-based noise filtering
Scanning transmission electron microscopy (STEM) is suitable for visualizing the inside of a relatively thick specimen than the conventional transmission electron microscopy, whose resolution is limited by the chromatic aberration of image forming lenses, and thus, the STEM mode has been employed frequently for computed electron tomography based three-dimensional (3D) structural characterization and combined with analytical methods such as annular dark field imaging or spectroscopies. However, the image quality of STEM is severely suffered by noise or artifacts especially when rapid imaging, in the order of millisecond per frame or faster, is pursued. Here we demonstrate a deep-learning-assisted rapid STEM tomography, which visualizes 3D dislocation arrangement only within five-second acquisition of all the tilt-series images even in a 300 nm thick steel specimen. The developed method offers a new platform for various in situ or operando 3D microanalyses in which dealing with relatively thick specimens or covering media like liquid cells are required.
Insights into the photoprotection mechanism of the UV filter homosalate
Homosalate has been found to exhibit favourable photophysics for inclusion in sunscreens, using a combination of spectroscopic and computational approaches.
Filtering and continuously separating microplastics from water using electric field gradients formed electrochemically in the absence of buffer
Water electrolysis at a bipolar electrode in the absence of buffer forms electric field gradients in a fundamentally new way. These electric field gradients are useful for continuously separating microplastics from water.
Predictive stochastic analysis of massive filter-based electrochemical reaction networks
New methods for chemical reaction network construction and analysis enable the discovery of novel products and mechanisms in battery electrochemistry.
Defect filtering for thermal expansion induced dislocations in III–V lasers on silicon
Epitaxially integrated III–V semiconductor lasers for silicon photonics have the potential to dramatically transform information networks, but currently, dislocations limit performance and reliability even in defect-tolerant InAs quantum dot (QD)-based lasers. Despite being below the critical thickness, QD layers in these devices contain previously unexplained misfit dislocations, which facilitate non-radiative recombination. We demonstrate here that these misfit dislocations form during post-growth cooldown due to the combined effects of (1) thermal-expansion mismatch between the III–V layers and silicon and (2) mechanical hardening in the active region. By incorporating an additional sub-critical thickness, indium-alloyed “misfit dislocation trapping layer,” we leverage these mechanical hardening effects to our advantage, displacing 95% of misfit dislocations from the QD layer in model structures. Unlike conventional dislocation mitigation strategies, the trapping layer reduces neither the number of threading dislocations nor the number of misfit dislocations. It simply shifts the position of misfit dislocations away from the QD layer, reducing the defects' impact on luminescence. In full lasers, adding a misfit dislocation trapping layer both above and below the QD active region displaces misfit dislocations and substantially improves performance: we measure a twofold reduction in lasing threshold currents and a greater than threefold increase in output power. Overall, our results suggest that devices employing both traditional threading dislocation reduction techniques and optimized misfit dislocation trapping layers may finally lead to fully integrated, commercially viable silicon-based photonic integrated circuits.
Cyclically symmetric radially self-similar phononic pseudocrystal isolator for broadband, ultrasonic vibration bandstop filtering
A 2D phononic pseudocrystal isolator exhibiting cyclic symmetry and radial self-similarity is measured and demonstrated to block a wide range of ultrasonic vibration. Measurements of longitudinal and shear wave blocking effects are made and compared with computational results. The use of the bandgap edge ratio is recommended for quantifying suppression in very-wide-bandgap materials. In conclusion, the upper-to-lower suppression edge frequency ratios of 3–4 are remarkably large for shear waves and even larger for longitudinal waves upper-to-lower suppression ratio (13 at 5 dB), such that 92.5% of frequencies in that range experience ≥ 5 dB of suppression.
A compact in situ polarized 3He neutron spin filter for the diffractometer VULCAN at the Spallation Neutron Source
We have built and commissioned a compact in situ 3He polarizer based on spin-exchange optical pumping for the engineering materials diffractometer, named VULCAN, at the Spallation Neutron Source (SNS) at Oak Ridge National Laboratory. The system achieved 78% 3He polarization with a short pump-up time constant of 2.2 h. Its first deployment in an in situ half-polarized neutron diffraction experiment on a magnetic shape memory alloy, Ni–Mn–Ga, revealed changes in magnetization coupled to deformation twinning under compression. With this new capability, VULCAN has become the first diffractometer at SNS capable of performing polarized neutron experiments, opening new opportunities to study a wider range of magnetism-related material behaviors under varied thermal, mechanical, electrical, and/or magnetic fields.
Selectivity filter ion binding affinity determines inactivation in a potassium channel
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