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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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Output-based adaptive aerodynamic simulations using convolutional neural networks
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Subgraph centrality: A complex network metric to characterize the contact network between sheared sand particles
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Many-body quantum electrodynamics networks: Non-equilibrium condensed matter physics with light
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The resistance perturbation distance: A metric for the analysis of dynamic networks
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Three-dimensional conductive network formed by carbon nanotubes in aqueous processed NMC electrode [Three-dimensional conductive network formed by carbon nanotubes in aqueous processed NMC532 electrode]
Aqueous processing of lithium nickel manganese cobalt oxide (LiNi 0.5 Mn 0.3 Co 0.2 O 2 , NMC 532) cathodes was investigated by incorporating carbon nanotubes (CNTs) as the conductive additive. Morphology observation showed CNTs evenly disperse across the electrode, uniformly covering each primary particle, and form three-dimensional electronic pathways. A resistance measurement indicated the CNTs can improve the electronic conductivity of the composite electrode by an order of magnitude compared to carbon black. CNTs based electrodes showed higher rate performance, lower hysteresis, and better cycling performance with 99.4% capacity retention after 200 cycles in full pouch cells compared to 94.6% for carbon black based electrode. Meanwhile, the content of active materials in the electrode was increased from 90 wt% to 96 wt% and the energy density was increased by 11.7%. Furthermore, this research demonstrates an effective combined approach for achieving aqueous processed cathodes with enhanced durability while simultaneously achieving higher energy density by reducing the content of inactive components.
Avoidance of vertical displacement events in DIII-D using a neural network growth rate estimator
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Influence of Mg2+ on CaCO3 precipitation during subsurface reactive transport in a homogeneous silicon-etched pore network
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Learning constitutive relations from indirect observations using deep neural networks
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RotEqNet: Rotation-equivariant network for fluid systems with symmetric high-order tensors
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Nonlocal kernel network (NKN): A stable and resolution-independent deep neural network
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Energetic design of grain boundary networks for toughening of nanocrystalline oxides
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