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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 325 records · Page 18
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Long-term Accumulation, Depth Distribution, and Speciation of Silver Nanoparticles in Biosolids-Amended Soils

Biosolids, when applied to soil, can be a source of metals and metal nanoparticles. One of the metals that forms nanoparticles is silver (Ag), which is commonly used in elemental (Ag(0)) form as an engineered nanomaterial in industrial or consumer products. The objective of this study was to quantify and characterize the accumulation and transport of Ag in a natural soil that has received agronomically-recommended rates of biosolids as fertilizer for the past 23 years (1994{2017). Total Ag concentrations were measured in biosolids and soil samples collected from 0 to 10 cm between 1996 and 2017. In addition, the depth distribution of Ag in the soil down to 60-cm depth was measured in 2017. Electron microscopy, in combination with X-ray spectroscopy, and X-ray absorption spectroscopy were used to identify the elemental association and oxidation state of the Ag in the samples. The Ag concentrations in the biosolids-amended soil increased steadily from 1996 until 2007, after which the concentrations leveled off at about 1.25 mg Ag kg-1 soil. This corresponded with a decrease of Ag concentrations in the biosolids over time. The majority of the Ag (82%) was confined to the top 10 cm of the soil, small amounts (14%) were detected in 10 to 20-cm depth, and trace amounts (4%) in 30 to 40-cm depth. The Ag in the biosolids and soil was identified as Ag-containing nanoparticles with a diameter of 10 to 12 nm. Ag was associated with S suggest that these 19 nanoparticles are Ag2S. This could be corroborated in biosolids with X-ray absorption spectroscopy (XANES); however, the Ag concentrations in the soil samples were too low to allow identification with XANES. Biosolids, when applied at agronomic rates in dryland cropping systems, represent an economically viable source of crop nutrients. In our study, long-term application of biosolids did not increase the concentration of total Ag in soil above a maximum of 1.5 mg Ag kg-1, and the Ag is present in the sulfide form. This concentration is below ecotoxicity limits for Ag2S in soil.

Taylor, Stephen E.↗
DOE OSTI2018

Improved Models for Metallic Nanoparticle Cores from Atomic Pair Distribution Function (PDF) Analysis

X-ray atomic pair distribution functions (PDFs) were collected from a range of canonical metallic nanomaterials, both elemental and alloyed, prepared using different synthesis methods and exhibiting drastically different morphological properties. Widely applied shape-tuned attenuated crystal (AC) fcc models proved inadequate, yielding structured, coherent, and correlated fit residuals. However, equally simple discrete cluster models could account for the largest amplitude features in these difference signals. A hypothesis testing based approach to nanoparticle structure modeling systematically ruled out effects from crystallite size, composition, shape, and surface faceting as primary factors contributing to the AC misfit. On the other hand, decahedrally twinned cluster cores were found to be the origin of the AC structure misfits for a majority of the nanomaterials reported here. It is further motivated that the PDF can readily differentiate between the arrangement of domains in these multiply twinned motifs. Most of the nanomaterials surveyed also fall within the sub-5 nm size regime where traditional electron microscopy cannot easily detect and quantify domain structures, with sampling representative of the average nanocrystal synthesized. Here, the results demonstrate that PDF analysis is a powerful method for understanding internal atomic interfaces in small noble metallic nanomaterials. Such core cluster models, easily built algorithmically, should serve as starting structures for more advanced models able to capture atomic positional disorder, ligand induced or otherwise, near nanocrystal surfaces.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2021

Predicting distribution of malaria vector larval habitats in Ethiopia by integrating distributed hydrologic modeling with remotely sensed data

Abstract Larval source management has gained renewed interest as a malaria control strategy in Africa but the widespread and transient nature of larval breeding sites poses a challenge to its implementation. To address this problem, we propose combining an integrated high resolution (50 m) distributed hydrological model and remotely sensed data to simulate potential malaria vector aquatic habitats. The novelty of our approach lies in its consideration of irrigation practices and its ability to resolve complex ponding processes that contribute to potential larval habitats. The simulation was performed for the year of 2018 using ParFlow-Common Land Model (CLM) in a sugarcane plantation in the Oromia region, Ethiopia to examine the effects of rainfall and irrigation. The model was calibrated using field observations of larval habitats to successfully predict ponding at all surveyed locations from the validation dataset. Results show that without irrigation, at least half of the area inside the farms had a 40% probability of potential larval habitat occurrence. With irrigation, the probability increased to 56%. Irrigation dampened the seasonality of the potential larval habitats such that the peak larval habitat occurrence window during the rainy season was extended into the dry season. Furthermore, the stability of the habitats was prolonged, with a significant shift from semi-permanent to permanent habitats. Our study provides a hydrological perspective on the impact of environmental modification on malaria vector ecology, which can potentially inform malaria control strategies through better water management.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2019

Control of formation of viscoelastic droplets and distribution of nano-inclusions in functional deposition for lithium–sulfur batteries

The electrospray process produces micro/nanodroplets for various applications such as thin and uniform coatings, drug carriers and mass spectrometry. In this paper, we study the spray processes of viscoelastic jets using simulations and experiments. In discretized modeling, the jet is perturbed with axisymmetric instability and the growth of this instability causes the jet to break into droplets. For the experiments, a solution of polyvinyl alcohol in water is sprayed and is visualized using a high-speed camera. The droplet size distribution is studied from simulations with experiments for three spray cases: electrospray, air spray, and air-controlled electrospray. Our simulations and experiments reveal that the electric field is effective in reducing the droplet size, while air flow offers more jet break-ups and thus a larger number of droplets. As a result, air-controlled electrospray where these two driving forces are synergistically combined leads to a larger number of smaller droplets than electrospray or air spray. As a result, we applied three spray processes to obtain a deposition of sulfur/mesoporous carbon/graphene/polymer binder composites as a lithium sulfur battery cathode and demonstrated that air-controlled electrospray leads to a higher capacity and rate capability than other processes, exhibiting 800 mA h g –1 at 0.5C and 600 mA h g –1 at 2C.

25 ENERGY STORAGE↗
DOE OSTI2019

Deep‐learning‐based power distribution network switch action identification leveraging dynamic features of distributed energy resources

This study proposes a data‐driven approach for identifying switch actions in power distribution networks. Simulated micro‐phasor measurement unit data is utilised to train a convolutional neural network (CNN) model. The trained CNN model can identify multi‐phase multi‐switch actions. Instead of working as a blackbox, the proposed approach extracts the features from the hidden layers of the trained CNN for engineering interpretation and error check. In addition, a random‐forest‐based feature ranking algorithm is proposed to identify the most important features. The proposed approach is validated on the IEEE 123‐node feeder modelled in GridLAB‐D. The CNN model is built and trained using TensorFlow. The proposed approach achieves 96.57% identification accuracy.

42 ENGINEERING↗
DOE OSTI2018

Search for new phenomena using the invariant mass distribution of same-flavour opposite-sign dilepton pairs in events with missing transverse momentum in $\sqrt{s}=13$ TeV pp collisions with the ATLAS detector

A search for new phenomena in final states containing an e + e - or μ + μ - pair, jets, and large missing transverse momentum is presented. This analysis makes use of proton–proton collision data with an integrated luminosity of 36.1 fb -1 , collected during 2015 and 2016 at a centre-of-mass energy s√=13 TeV with the ATLAS detector at the Large Hadron Collider. The search targets the pair production of supersymmetric coloured particles (squarks or gluinos) and their decays into final states containing an e + e - or μ + μ - pair and the lightest neutralino ($\sim\atop{χ}$ $0\atop{1}$) via one of two next-to-lightest neutralino ($\sim\atop{χ}$ $0\atop{2}$) decay mechanisms: $\sim\atop{χ}$ $0\atop{2}$→Z$\sim\atop{χ}$ $0\atop{1}$, where the Z boson decays leptonically leading to a peak in the dilepton invariant mass distribution around the Z boson mass; and $\sim\atop{χ}$ $0\atop{2}$→ℓ + ℓ - $\sim\atop{χ}$ $0\atop{1}$ with no intermediate ℓ + ℓ - resonance, yielding a kinematic endpoint in the dilepton invariant mass spectrum. The data are found to be consistent with the Standard Model expectation. Finally, results are interpreted using simplified models, and exclude gluinos and squarks with masses as large as 1.85 and 1.3 TeV at 95% confidence level, respectively.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2019

(DG)2 (Distributed Generation for Distribution Grids) [SWR-18-63]

(DG)2 is an impact analysis tool for electrical utilities to perform spatio-temporal analysis with solar and battery energy storage systems. The purpose of the tool is to help distribution utilities with identifying impacts and costs associated with locating a MW-scale solar farm and energy storage system at a certain location over other choices. This tool also assists utilities to assess the amount of reverse power flow at the transmission lines.

Latif, Aadil↗
DOE OSTI2024

Snow distribution patterns revisited: A physics-based and machine learning hybrid approach to snow distribution mapping in the sub-Arctic Supporting Data

Snow in the Arctic and sub-Arctic is highly variable at fine scales, with deep drifts and shallow scoured areas creating a complex pattern of snow on the landscape. This fine-scale variation in snow is driven primarily by landscape and vegetation properties. Some landscape features, such as river beds, will rapidly fill in with snow during the wintertime due to high winds, while shrubs will trap blowing snow, resulting in drifts. Meanwhile, snow will blow off of exposed areas, resulting in abnormally shallow snow. These complex interactions between wind, vegetation, and terrain are difficult to represent well with physically-based models, but machine learning techniques have shown promise in the past. Here, we propose a hybrid modeling approach, where we use machine learning derived snow pattern maps to inform SnowModel, a physically-based snow process model. We develop and test this technique at the Teller 27 Seward Peninsula NGEE-Arctic study site. This dataset includes 5 *.nc files of model inputs and outputs plus one user guide (*.pdf). We present the data we use to drive SnowModel (vegetation type, digital elevation model), the snow pattern maps used to inform SnowModel (Standardized Depth Values maps, Machine Learning Snow Distribution Pattern), and our machine learning, SnowModel, and Hybrid snow depth and snow water equivalent results. The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research. The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska. Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

54 ENVIRONMENTAL SCIENCES↗