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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 451 records · Page 25
All sourcesNASA NTRSDOE OSTI
DOE OSTI2015

Nanoscale building blocks in a novel lithium arsenotungsten bronze: Synthesis and characterization

We report on a novel compound Li{sub 3}AsW{sub 7}O{sub 25} obtained by solid-state reaction and characterized by diffraction and spectroscopic methods. The bronze-type compound crystallizes in the orthorhombic space group Pbca with a=724.38(3) pm, b=1008.15(4) pm, c=4906.16(17) pm and Z=8. The structure is built up by chains of WO{sub 6} octahedra interconnected by AsO{sub 4} tetrahedra and WO{sub 6} octahedra forming a polyhedral arrangement as seen in intergrowth tungsten bronzes. The X-ray single crystal structure refinement allows solving the complex arsenotungstate framework. The powder neutron diffraction data analysis locates the lithium atoms. Thermal analysis showed that Li{sub 3}AsW{sub 7}O{sub 25} is stable up to its melting at 1135(3) K followed by a decomposition at 1182(5) K. The Kubelka–Munk treatment of the UV–vis spectrum revealed a wide band gap in the range of 2.84–3.40 eV depending on the presumed electron transition type. - Graphical abstract: Crystal structure of Li{sub 3}AsW{sub 7}O{sub 25} showing different schematic components. - Highlights: • A report on a novel compound Li{sub 3}AsW{sub 7}O{sub 25} obtained by solid-state reaction. • Chains and nano-blocks of WO{sub 6} octahedra and AsO{sub 4} tetrahedra formed a structure like intergrowth tungsten bronzes. • X-ray diffraction allowed solving the complex arsenotungstate framework. • Powder neutron diffraction data analysis locates the lithium atoms. • UV–vis spectrum revealed the band-gap.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2019

Building in vitro transcriptional regulatory networks by successively integrating multiple functional circuit modules

The regulation of cellular dynamics and responses to stimuli by genetic regulatory networks suggests how in vitro chemical reaction networks might analogously direct the dynamics of synthetic materials or chemistries. A key step in developing genetic regulatory network analogues capable of this type of sophisticated regulation is the integration of multiple coordinated functions within a single network. In this study, we demonstrate how such functional integration can be achieved using in vitro transcriptional genelet circuits that emulate essential features of cellular genetic regulatory networks. By successively incorporating functional genelet modules into a bistable circuit, we construct an integrated regulatory network that dynamically changes its state in response to upstream stimuli and coordinates the timing of downstream signal expression. We use quantitative models to guide module integration and develop strategies to mitigate undesired interactions between network components that arise as the size of the network increases. This approach could enable the construction of in vitro networks capable of multifaceted chemical and material regulation.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2015

Building unbiased estimators from non-gaussian likelihoods with application to shear estimation

We develop a general framework for generating estimators of a given quantity which are unbiased to a given order in the difference between the true value of the underlying quantity and the fiducial position in theory space around which we expand the likelihood. We apply this formalism to rederive the optimal quadratic estimator and show how the replacement of the second derivative matrix with the Fisher matrix is a generic way of creating an unbiased estimator (assuming choice of the fiducial model is independent of data). Next we apply the approach to estimation of shear lensing, closely following the work of Bernstein and Armstrong (2014). Our first order estimator reduces to their estimator in the limit of zero shear, but it also naturally allows for the case of non-constant shear and the easy calculation of correlation functions or power spectra using standard methods. Both our first-order estimator and Bernstein and Armstrong’s estimator exhibit a bias which is quadratic in true shear. Our third-order estimator is, at least in the realm of the toy problem of Bernstein and Armstrong, unbiased to 0.1% in relative shear errors Δg/g for shears up to |g| = 0.2.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

DeepMerge – II. Building robust deep learning algorithms for merging galaxy identification across domains

In astronomy, neural networks are often trained on simulation data with the prospect of being used on telescope observations. Unfortunately, training a model on simulation data and then applying it to instrument data leads to a substantial and potentially even detrimental decrease in model accuracy on the new target dataset. Simulated and instrument data represent different data domains, and for an algorithm to work in both, domain-invariant learning is necessary. Here we employ domain adaptation techniques— Maximum Mean Discrepancy (MMD) as an additional transfer loss and Domain Adversarial Neural Networks (DANNs)— and demonstrate their viability to extract domain-invariant features within the astronomical context of classifying merging and non-merging galaxies. Additionally, we explore the use of Fisher loss and entropy minimization to enforce better in-domain class discriminability. We show that the addition of each domain adaptation technique improves the performance of a classifier when compared to conventional deep learning algorithms. We demonstrate this on two examples: between two Illustris-1 simulated datasets of distant merging galaxies, and between Illustris-1 simulated data of nearby merging galaxies and observed data from the Sloan Digital Sky Survey. The use of domain adaptation techniques in our experiments leads to an increase of target domain classification accuracy of up to ~20%. With further development, these techniques will allow astronomers to successfully implement neural network models trained on simulation data to efficiently detect and study astrophysical objects in current and future large-scale astronomical surveys.

galaxies: interactions↗