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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 1,063 records · Page 59
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Screening two-dimensional materials with topological flat bands

The topological flat band (TFB) has been proposed theoretically in various lattice models, to exhibit a rich spectrum of intriguing physical behaviors. However, the experimental demonstration of flat band (FB) properties has been severely hindered by the lack of materials realization. In this study, by screening materials from a first-principles materials database, we identify a group of two-dimensional materials with TFBs near the Fermi level, covering some simple line-graph and generalized line-graph FB lattice models. These include the kagome sublattice of O in Ti O 2 yielding a spin-unpolarized TFB, and that of V in ferromagnetic V 3 F 8 yielding a spin-polarized TFB. The monolayer Nb 3 Te Cl 7 and its counterparts from element substitution are found to be breathing-kagome-lattice crystals. The family of monolayer II I 2 V I 3 compounds exhibit a TFB representing the coloring-triangle lattice model. Re F 3 , Mn F 3 , and Mn Br 3 are all predicted to be diatomic-kagome-lattice crystals, with TFB transitions induced by atomic substitution. Finally, Hg F 2 , Cd F 2 , and Zn F 2 are discovered to host dual TFBs in the diamond-octagon lattice. Our findings pave the way to further experimental exploration of eluding FB materials and properties.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Dielectric screening and vacancy formation for large neutral and charged Si n H m ( n > 1500 ) nanocrystals using real-space pseudopotentials

A commonly used procedure for computing the properties of defects in crystalline materials is to consider a large supercell that includes the defect of interest. This is a straightforward technique as standard energy band codes can be used for such computations. For neutral defects, the only impediment of such an approach is to avoid defect-defect interactions between adjoining cells. However, this procedure can be complex if the defect of interest is charged as the system at large contains Coulombic divergences. Moreover, some have recently argued that the conventional definition of formation energies for charged defects cannot be reconciled with statistical mechanics. Here, we focus on an alternative approach. We consider large nanocrystals wherein a charged defect can be placed. Since the system is confined, a charged defect within the nanocrystal does not result in a Coulombic divergence. The chief impediment is computational, i.e., while no defect-defect or Coulombic divergences are present, the nanocrystal must be sufficiently large to allow the system to properly replicate a bulklike configuration. With the development of new algorithms and hardware advances, computations for systems of sufficient size to address this issue are feasible. In particular, we solve the Kohn-Sham equation in real space using pseudopotential-density-functional theory for large silicon nanocrystals, which contain thousands of atoms. Further, we focus on (i) the screening of a point charge and (ii) the formation of a charged vacancy in hydrogen-terminated silicon nanocrystals. This approach allows us to examine the role of quantum confinement in addition to exploring the bulk limit. Comparisons to other methods confirm the viability of this approach.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Electronic structure and optical properties of halide double perovskites from a Wannier-localized optimally-tuned screened range-separated hybrid functional

Halide double perovskites are a chemically diverse and growing class of compound semiconductors that are promising for optoelectronic applications. However, the prediction of their fundamental gaps and optical properties with density functional theory (DFT) and ab initio many-body perturbation theory has been a significant challenge. Recently, a nonempirical Wannier-localized optimally tuned screened range-separated hybrid (WOT-SRSH) functional has been shown to accurately produce the fundamental band gaps of a wide set of semiconductors and insulators, including lead halide perovskites. Here, in this study, we apply the WOT-SRSH functional to five halide double perovskites and compare the results with those obtained from other known functionals and previous GW calculations. We also use the approach as a starting point for GW calculations and we compute the band structures and optical absorption spectrum for Cs 2 AgBiBr 6 , using both time-dependent DFT and the GW-Bethe-Salpeter equation approach. We show that the WOT-SRSH functional leads to accurate fundamental and optical band gaps, as well as optical absorption spectra, consistent with spectroscopic measurements, thereby establishing WOT-SRSH as a viable method for the accurate prediction of optoelectronic properties of halide double perovskites.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Accurate point defect energy levels from non-empirical screened range-separated hybrid functionals: The case of native vacancies in ZnO

We use density functional theory (DFT) with non-empirically tuned screened range-separated hybrid (SRSH) functionals to calculate the electronic properties of native zinc and oxygen vacancy point defects in ZnO, and we predict their defect levels for thermal and optical transitions in excellent agreement with available experiments and prior calculations that use empirical hybrid functionals. Furthermore, the ability of this non-empirical first-principles framework to accurately predict quantities of relevance to both bulk- and defect-level spectroscopy enables high-accuracy DFT calculations with non-empirical hybrid functionals for defect physics, at a reduced computational cost.

Defects↗
DOE OSTI2018

A convolutional neural network-based screening tool for X-ray serial crystallography

A new tool is introduced for screening macromolecular X-ray crystallography diffraction images produced at an X-ray free-electron laser light source. Based on a data-driven deep learning approach, the proposed tool executes a convolutional neural network to detect Bragg spots. Automatic image processing algorithms described can enable the classification of large data sets, acquired under realistic conditions consisting of noisy data with experimental artifacts. Outcomes are compared for different data regimes, including samples from multiple instruments and differing amounts of training data for neural network optimization.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2014

Probing conformational states of glutaryl-CoA dehydrogenase by fragment screening

Glutaric acidemia type 1 is an inherited metabolic disorder which can cause macrocephaly, muscular rigidity, spastic paralysis and other progressive movement disorders in humans. The defects in glutaryl-CoA dehydrogenase (GCDH) associated with this disease are thought to increase holoenzyme instability and reduce cofactor binding. Here, the first structural analysis of a GCDH enzyme in the absence of the cofactor flavin adenine dinucleotide (FAD) is reported. The apo structure of GCDH from Burkholderia pseudomallei reveals a loss of secondary structure and increased disorder in the FAD-binding pocket relative to the ternary complex of the highly homologous human GCDH. After conducting a fragment-based screen, four small molecules were identified which bind to GCDH from B. pseudomallei. Complex structures were determined for these fragments, which cause backbone and side-chain perturbations to key active-site residues. Structural insights from this investigation highlight differences from apo GCDH and the utility of small-molecular fragments as chemical probes for capturing alternative conformational states of preformed protein crystals.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2017

Estimating the Impacts of Direct Load Control Programs Using GridPIQ, a Web-Based Screening Tool

In direct load control (DLC) programs, utilities can curtail the demand of participating loads to contractually agreed-upon levels during periods of critical peak load, thereby reducing stress on the system, generation cost, and required transmission and generation capacity. Participating customers receive financial incentives. The impacts of implementing DLC programs extend well beyond peak shaving. There may be a shift of load proportional to the interrupted load to the times before or after a DLC event, and different load shifts have different consequences. Tools that can quantify the impacts of such programs on load curves, peak demand, emissions, and fossil fuel costs are currently lacking. The Grid Project Impact Quantification (GridPIQ) screening tool includes a Direct Load Control module, which takes into account project-specific inputs as well as the larger system context in order to quantify the impacts of a given DLC program. This allows users (utilities, researchers, etc.) to test and compare different program specifications and their impacts.

Demand Reponse↗
DOE OSTI2022

River Basin Export Reduction Optimization Support Tool; a tool to screen options for reducing nutrient loads while minimizing cost

Abstract Excess loading of nitrogen and phosphorus to river networks causes environmental harm, but reducing loads from large river basins is difficult and expensive. We developed a new tool, the River Basin Export Reduction Optimization Support Tool (RBEROST) to identify the least‐cost combinations of management practices that will reduce nutrient loading to target levels in downstream and mid‐network waterbodies. We demonstrate the utility of the tool in a case study in the Upper Connecticut River Basin in New England, USA. The total project cost of optimized lowest‐cost plans ranged from $18.0 million to $41.0 million per year over 15 years depending on user specifications. Plans include both point source and non‐point source management practices, and most costs are associated with urban stormwater practices. Adding a 2% margin of safety to loading targets improved the estimated probability of success from 37.5% to 99%. The large spatial scale of RBEROST, and the consideration of both point and non‐point source contributions of nutrients, make it well suited as an initial screening tool in watershed planning.

Chamberlin, Catherine↗
DOE OSTI2019

Screening Compounds for Fast Pyrolysis and Catalytic Biofuel Upgrading Using Artificial Neural Networks

Abstract There is significant interest among researchers in finding economically sustainable alternatives to fossil-derived drop-in fuels and fuel additives. Fast pyrolysis, a method for converting biomass into liquid hydrocarbons with the potential for use as fuels or fuel additives, is a promising technology that can be two to three times less expensive at scale when compared to alternative approaches such as gasification and fermentation. However, many bio-oils directly derived from fast pyrolysis have a high oxygen content and high acidity, indicating poor performance in diesel engines when used as fuels or fuel additives. Thus, a combination of selective fast pyrolysis and chemical catalysis could produce tuned bioblendstocks that perform optimally in diesel engines. The variance in performance for derived compounds introduces a feedback loop in researching acceptable fuels and fuel additives, as various combustion properties for these compounds must be determined after pyrolysis and catalytic upgrading occurs. The present work aims to reduce this feedback loop by utilizing artificial neural networks trained with quantitative structure-property relationship values to preemptively screen pure component compounds that will be produced from fast pyrolysis and catalytic upgrading. The quantitative structure-property relationship values selected as inputs for models are discussed, the cetane number and sooting propensity of compounds derived from the catalytic upgrading of phenol are predicted, and the viability of these compounds as fuels and fuel additives is analyzed. The model constructed to predict cetane number has a test set prediction root-mean-squared error of 9.874 cetane units, and the model constructed to predict yield sooting index has a test set prediction root-mean-squared error of 13.478 yield sooting index units (on the unified scale).

09 BIOMASS FUELS↗
DOE OSTI2015

SU-E-T-600: Patient Specific IMRT Verification Using a Phosphor-Screen Based Geometric QA System: A Preliminary Study

Purpose: Raven QA (JPLC, MD) is a unified and comprehensive quality assurance system for QA of TG-142, which use a phosphor screen, a mirror system and a camera. It is to test if this device can be used for IMRT QA dosimetry. Methods: A lung IMRT case is used deliver dose to Raven QA. Accuracy of dose distribution of 5cm slab phantom using Eclipse planning system (Varian) has been confirmed both from a Monte Carlo Simulation and from a MapCheck (SunNuclear) measurement. Geometric distortion and variation of spatial dose response are corrected after background subtraction. A pin-hole grid plate is designed and used to determine the light scatter in the Raven QA box and the spatial dose response. Optic scatter model was not applied in this preliminary study. Dose is normalized to the response of the 10×10 field and the TMR of 5cm depth was considered. Results: Time to setup the device for IMRT QA takes less than 5 minutes as other commercially available devices. It shows excellent dose linearity and dose rate independent, less than 1 %. Background signal, however, changes for different field sizes. It is believed to be due to inaccurate correction of optic scatter. Absolute gamma (5%, 5mm) passing rate was higher than 95%. Conclusion: This study proves that the Raven QA can be used for a patient specific IMRT verification. Part of this study is supported by the Maryland Industrial Partnership Grant.

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2016

SU-F-T-180: Evaluation of a Scintillating Screen Detector for Proton Beam QA and Acceptance Testing

Purpose: To test the performance of a commercial scintillating screen detector for acceptance testing and Quality Assurance of a proton pencil beam scanning system. Method: The detector (Lexitek DRD 400) has 40cm × 40cm field, uses a thin scintillator imaged onto a 16-bit scientific CCD with ∼0.5mm resolution. A grid target and LED illuminators are provided for spatial calibration and relative gain correction. The detector mounts to the nozzle with micron precision. Tools are provided for image processing and analysis of single or multiple Gaussian spots. Results: The bias and gain of the detector were studied to measure repeatability and accuracy. Gain measurements were taken with the LED illuminators to measure repeatability and variation of the lens-CCD pair as a function with f-stop. Overall system gain was measured with a passive scattering (broad) beam whose shape is calibrated with EDR film placed in front of the scintillator. To create a large uniform field, overlapping small fields were recorded with the detector translated laterally and stitched together to cover the full field. Due to the long exposures required to obtain multiple spills of the synchrotron and very high detector sensitivity, borated polyethylene shielding was added to reduce direct radiation events hitting the CCD. Measurements with a micro ion chamber were compared to the detector’s spot profile. Software was developed to process arrays of Gaussian spots and to correct for radiation events. Conclusion: The detector background has a fixed bias, a small component linear in time, and is easily corrected. The gain correction method was validated with 2% accuracy. The detector spot profile matches the micro ion chamber data over 4 orders of magnitude. The multiple spot analyses can be easily used with plan data for measuring pencil beam uniformity and for regular QA comparison.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

SU-F-T-287: A Preliminary Study On Patient Specific VMAT Verification Using a Phosphor-Screen Based Geometric QA System (Raven QA)

Purpose: The RavenQA system (LAP Laser, Germany) is a QA device with a phosphor screen detector for performing the QA tasks of TG-142. This study tested if it is feasible to use the system for the patient specific QA of the Volumetric Modulated Arc Therapy (VMAT). Methods: Water equivalent material (5cm) is attached to the front of the detector plate of the RavenQA for dosimetry purpose. Then the plate is attached to the gantry to synchronize the movement between the detector and the gantry. Since the detector moves together with gantry, The ’Reset gantry to 0’ function of the Eclipse planning system (Varian, CA) is used to simulate the measurement situation when calculating dose of the detector plate. The same gantry setup is used when delivering the treatment beam for feasibility test purposes. Cumulative dose is acquired for each arc. The optical scatter component of each captured image from the CCD camera is corrected by deconvolving the 2D spatial invariant optical scatter kernel (OSK). We assume that the OSK is a 2D isotropic point spread function with inverse-squared decrease as a function of radius from the center. Results: Three cases of VMAT plans including head & neck, whole pelvis and abdomen-pelvis are tested. Setup time for measurements was less than 5 minutes. Passing rates of absolute gamma were 99.3, 98.2, 95.9 respectively for 3%/3mm criteria and 96.2, 97.1, 86.4 for 2%/2mm criteria. The abdomen-pelvis field has long treatment fields, 37cm, which are longer than the detector plate (25cm). This plan showed relatively lower passing rate than other plans. Conclusion: An algorithm for IMRT/VMAT verification using the RavenQA has been developed and tested. The model of spatially invariant OSK works well for deconvolution purpose. It is proved that the RavenQA can be used for the patient specific verification of VMAT. This work is funded in part by a Maryland Industrial Partnership Program grant to University of Maryland and to JPLC who owns the Raven technology. John Wong is a co-founder of JPLC.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2017

Nonlinear screening of dust grains and structurization of dusty plasma: II. formation and stability of dust structures

The second part of the review on dust structures (the first part was published in Plasma Phys. Rep. 39, 515 (2013)) is devoted to experimental and theoretical studies on the stability of structures and their formation from the initially uniform dusty plasma components. The applicability limits of theoretical results and the role played by nonlinearity in the screening of dust grains are considered. The importance of nonlinearity is demonstrated by using numerous laboratory observations of planar clusters and volumetric dust structures. The simplest compact agglomerates of dust grains in the form of stable planar clusters are discussed. The universal character of instability resulting in the structurization of an initially uniform dusty plasma is shown. The fundamental correlations described in the first part of the review, supplemented with effects of dust inertia and dust friction by the neutral gas, are use to analyze structurization instability. The history of the development of theoretical ideas on the physics of the cluster formation for different types of interaction between dust grains is described.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2021

The Effect of Material Properties on Oxygen Evolution Activity and Assessing Half-Cell Screening as a Predictive Tool in Electrolysis

Iridium-based oxygen evolution catalysts are screened in this study for activity and stability with rotating disk electrode (RDE) half-cells. This study focuses on the electrochemical and materials approaches needed to characterize oxygen evolution catalysts, and include testing for activity, stability, composition, oxide content, and structure. Findings also discuss recommendations for data interpretation and detail the difficulties of comparing catalysts across materials sets with different elemental and oxide compositions, and linking RDE activity to device-level performance. The materials evaluated are a mixture of oxides and metals, and several methods are used to quantify metal content, qualitatively assess oxide content, and determine total surface area. Oxygen evolution activities and stabilities are compared, where a wide range of results are reported. In general, higher RDE performances are found for catalysts that contained larger amounts of ruthenium and metals. Higher durability, however, is found for catalysts that only contained iridium and a higher proportion of oxides. Additionally, catalysts are evaluated for performance in membrane electrode assemblies to assess RDE as a predictive tool in electrolysis. While activity trends within individual material sets generally held between ex- and in-situ testing, RDE tends to overestimate the activity of more metallic catalysts when compared to device-level performance. These results stress the need for multiple metal/oxide baselines for mixed catalysts, to better project in-situ kinetics.

36 MATERIALS SCIENCE↗
DOE OSTI2016

Communication—Improving Intermediate-Temperature Performance of a Screen-Printed LSCF Cathode with Infiltrated LSCF Nanoparticles

Here, the present study investigates the mass loading effect of an infiltrated La 0.6 Sr 0.4 Co 0.2 Fe 0.8 O 3-δ (LSCF) nanoparticles (NPs) catalyst on the area-specific polarization resistance (Rp) of a screen-printed porous LSCF cathode for solid oxide fuel cells. The results show that R p of the LSCF-NPs decorated LSCF cathode can be substantially reduced by as much as 89.3% after a single-step impregnation of 1.5 M nitrate solution containing La:Sr:Co:Fe = 0.6:0.4:0.2:0.8 with a mass loading of 3 wt%.

25 ENERGY STORAGE↗
DOE OSTI2025

A systematic decision-making methodology to formalize the selection of degree of realism in screening analysis of probabilistic risk assessment

In the nuclear power domain, Probabilistic Risk Assessment (PRA) is used to inform decision-making for Nuclear Power Plants (NPPs). Recently, there has been an increase in the utilization of modeling and simulation (M&S) to support the estimation of PRA inputs. Risk analysts should carefully select the PRA items that require M&S and their degree of realism (DoR) with consideration of the required resources. To support this selection, this article formulates a systematic decision-making approach for the DoR selection. The DoR selection is made based on two predictive decision-making attributes: the predicted differences in safety risk estimate (ΔSaRi) and the cost of analysis (ΔCAN). This research also develops and quantifies causal models to estimate ΔSaRi and ΔCAN. The causal model-based prediction of ΔSaRi and ΔCAN helps reduce the trial-and-error nature of the DoR selection in the PRA screening analysis and provides insights for DoR selection and the gradual refinements of PRA realism. This approach is demonstrated for a case study on fire PRA of NPPs, where an adequate DoR is selected from two fire models: an engineering correlation and a zone model.

Alkhatib, Sari [Department of Nuclear, Plasma, and↗
DOE OSTI2022

In silico evaluation of a targeted metaproteomics strategy for broad screening of cellulolytic enzyme capacities in anaerobic microbiome bioreactors

Microbial-driven solubilization of lignocellulosic material is a natural mechanism that is exploited in anaerobic digesters (ADs) to produce biogas and other valuable bioproducts. Glycoside hydrolases (GHs) are the main enzymes that bacterial and archaeal populations use to break down complex polysaccharides in these reactors. Methodologies for rapidly screening the physical presence and types of GHs can provide information about their functional activities as well as the taxonomical diversity within AD systems but are largely unavailable. Targeted proteomic methods could potentially be used to provide snapshots of the GHs expressed by microbial consortia in ADs, giving valuable insights into the functional lignocellulolytic degradation diversity of a community. Such observations would be essential to evaluate the hydrolytic performance of a reactor or potential issues with it.

09 BIOMASS FUELS↗
DOE OSTI2023

Rapid screening of secondary aromatic metabolites in Populus trichocarpa leaves

Abstract Background High-throughput metabolomics analytical methodology is needed for population-scale studies of bioenergy-relevant feedstocks such as poplar ( Populus sp.). Here, the authors report the relative abundance of extractable aromatic metabolites in Populus trichocarpa leaves rapidly estimated using pyrolysis-molecular beam mass spectrometry (py-MBMS). Poplar leaves were analyzed in conjunction with and validated by GC/MS analysis of extracts to determine key spectral features used to build PLS models to predict the relative composition of extractable aromatic metabolites in whole poplar leaves. Results The Pearson correlation coefficient for the relative abundance of extractable aromatic metabolites based on ranking between GC/MS analysis and py-MBMS analysis of the Boardman leaf set was 0.86 with R 2 = 0.76 using a simplified prediction approach from select ions in MBMS spectra. Metabolites most influential to py-MBMS spectral features in the Clatskanie set included the following compounds: catechol, salicortin, salicyloyl-coumaroyl-glucoside conjugates, α-salicyloylsalicin, tremulacin, as well as other salicylates, trichocarpin, salicylic acid, and various tremuloidin conjugates. Ions in py-MBMS spectra with the highest correlation to the abundance of extractable aromatic metabolites as determined by GC/MS analysis of extracts, included m/z 68, 71, 77, 91, 94, 105, 107, 108, and 122, and were used to develop the simplified prediction approach without PLS models or a priori measurements. Conclusions The simplified py-MBMS method is capable of rapidly screening leaf tissue for relative abundance of extractable aromatic secondary metabolites to enable prioritization of samples in large populations requiring comprehensive metabolomics that will ultimately inform plant systems biology models and advance the development of optimized biomass feedstocks for renewable fuels and chemicals.

09 BIOMASS FUELS↗