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.
Not Available
Not Available
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.
Operando pair distribution function (PDF) analysis and ex situ Na-23 magic-angle spinning solid-state nuclear magnetic resonance (MAS ssNMR) spectroscopy are used to gain insight into the alloying mechanism of high-capacity antimony anodes for sodium-ion batteries. Subtraction of the PDF of crystalline NaxSb phases from the total PDF, an approach constrained by chemical phase information gained from Na-23 ssNMR in reference to relevant model compounds, identifies two previously uncharacterized intermediate species formed electro-chemically; a-Na3-xSb (x approximate to 0.4-0.5), a structure locally similar to crystalline Na3Sb (c-Na3Sb) but with significant numbers of sodium vacancies and a limited correlation length, and a-Na1.7Sb, a highly amorphous structure featuring some Sb-Sb bonding. The first sodiation breaks down the crystalline antimony to form first a-Na3-xSb and, finally, crystalline Na3Sb. Desodiation results in the formation of an electrode formed of a composite of crystalline and amorphous antimony networks. We link the different reactivity of these networks to a series of sequential sodiation reactions manifesting as a cascade of processes observed in the electrochemical profile of subsequent cycles. The amorphofis network reacts at higher voltages reforming a-Na1.7Sb, then a-Na3-xSb, whereas lower potentials are required for the sodiation of crystalline antimony, which reacts to form a-Na3-xSb without the formation of a-Na3-xSb. a-Na3-xSb is converted to crystalline Na3Sb at the end of the second discharge. We find no evidence of formation of NaSb. Variable temperature Na-23 NMR experiments reveal significant sodium mobility within c-Na3Sb; this is a possible contributing factor to the excellent rate performance of Sb anodes.
Explore the source record for details and available documents.
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.
Renewable transportation fuels from biomass have the potential to substantially reduce greenhouse gas emissions and diversify global fuel supplies. Thermal conversion by fast pyrolysis converts up to 75% of the starting plant material (and its energy content) to a bio-oil intermediate suitable for upgrading to motor fuel. Woody biomass, by far the most widely-used and researched material, is generally preferred in thermochemical processes due to its low ash content and high quality bio-oil produced. However, the availability and cost of biomass resources, e.g. forest residues, agricultural residues, or dedicated energy crops, vary greatly by region and will be key determinates in the overall economic feasibility of a pyrolysis-to-fuel process. Formulation or blending of various feedstocks, combined with thermal and/or chemical pretreatment, could facilitate a consistent, high-volume, lower-cost biomass supply to an emerging biofuels industry. However, the impact of biomass type and pretreatment conditions on bio-oil yield and quality, and the potential process implications, are not well understood. This literature review summarizes the current state of knowledge regarding the effect of feedstock and pretreatments on the yield, product distribution, and upgradability of bio-oil.
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.
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.
Explore the source record for details and available documents.
Energy transfer to damped modes in gyrokinetic ion temperature gradient driven turbulence is studied to understand the transfer dynamics and find scaling representations for the heavily populated mode space. Proper orthogonal and linear eigenmode decompositions are introduced and examined to assess whether modes are well-resolved and what scales they encompass. It is observed that damped modes across a range of inhomogeneous scales receive energy simultaneously and directly from the unstable mode, constituting a form of parallel transfer, distinct from the serial mode-to-mode transfer of the wavenumber cascade of hydrodynamic turbulence. Controlling for modes that are well resolved and labeling the modes of the linear decomposition in order of damping rate, energy transfer in the mode space satisfies an equipartition of the energy dissipation rate, leading to a simple rule for the distribution of energy in the space of damped modes. Energy dissipation rate equipartition is the form that the canonical nonlinear invariance of energy transfer assumes in a dissipation range with parallel rather than serial transfer.
Abstract Not Provided
Explore the source record for details and available documents.