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Results for “coal-derived material”

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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57 records · Page 4
All sourcesNASA NTRSDOE OSTI
NASA NTRS1979

Advanced air transport concepts

The concepts of laminar flow control, very large all-wing aircraft, an aerial relay transportation system and alternative fuels, which would enable large improvements in fuel conservation in air transportation in the 1990's are discussed. Laminar boundary layer control through suction would greatly reduce skin friction and has been reported to reduce fuel consumption by up to 29%. Distributed load aircraft, in which all fuel and payload are carried in the wing and the fuselage is absent, permit the use of lighter construction materials and the elimination of fuselage and tail drag. Spanloader aircraft with laminar flow control could be used in an aerial relay transportation system which would employ a network of continuously flying liners supplied with fuel, cargo and crews by smaller feeder aircraft. Liquid hydrogen and methane fuels derived from coal are shown to be more weight efficient and less costly than coal-derived synthetic jet fuels.

Molloy, J. K.↗
DOE OSTI2021

Facile Synthesis of Micrometer-sized Hierarchical Porous Si@C Anodes for High-Performance Lithium-Ion Batteries

Extensive studies have been conducted to determine if replacing graphite anodes with silicon is feasible since silicon possesses a high capacity and is an abundant resource; however, Si anodes suffer significant volume changes during cycling, which pulverizes its structure and subsequently causes rapid capacity fading. We used SiO2 nanoparticles (nSiO2) and low-cost coal-derived humic acid as a feedstock to synthesize high performance Si-C anodes for Li-ion Batteries through spray drying, calcination, and magnesiothermic reductions. Our Si-C anodes features micrometer-sized porous Si coated with a graphitized carbon shell (mpSi@C). The hierarchical graphitized carbon shell and porous silicon structure relieve the mechanical stress of the Si phase upon cycling, which stabilizes the structure. This mpSi@C composite design allows for a high initial discharge capacity of 2199.9 mAh g-1 at 0.1 A g-1 and a cycling performance of 68% capacity retention after 100 cycles at 1.0 A g-1. The multipoint contact between the Si anode and C structure allows for a remarkable performance rate of 566.3 mAh g-1 at 5.0 A g-1.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2024

Process Optimization of Carbon Electrode Materials Manufacturing by Experimental Study and Machine Learning Techniques

Electrospun carbon fibers from coal have been investigated as electrodes for batteries and supercapacitors. Despite the excellent properties of coal-derived carbon fibers (CCNF) for energy storage devices, there still lacks systematic understanding on how various process parameters affect final electrode performances, which poses challenges to scale from pilot to high volume manufacturing. The goals of this project are twofold. First, we focuse on process optimization for converting a new precursor from powder river basin (PRB) coal, referred to as coal-based polyurethane (CPU) to CCNF using electrospinning. Second, different machine learning techniques will be examined using experimental data from this work and open literature. Specifically, for CPU the following process parameters need to be characterized and optimized in order to produce CCNFs with desirable mechanical integrity and physiochemical properties: precursor composition and viscosity, operating voltage and distance, oxidation and carbonization temperature and duration. Consequently, physiochemical properties of the fibers were characterized to correlate these process parameters with desirable electrochemical performance. Given the complex nature of the fiber production process, ML models are assessed for their ability to capture the nonlinear relationship between process parameters and the electrochemical properties in applications including supercapacitors. As such, we applied various machine learning techniques, to determine which technique produces a model that best predicts device function.

Cincotta, Robert E.F.↗