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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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Storing energy and information using electrochemical ion insertion.
Abstract not provided.
UMap: Enabling Application-driven Optimizations for Memory Mapping Persistent Store
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Assessment of the Technical and Economic Potential to Produce Oil and Store CO2 by Applying CO2 Enhanced Oil Recovery to Greenfield Residual Oil Zones within the Permian Basin
Presented by NETL at Carbon Capture, Utilization and Storage Conference 2018, 03/18
UCNt: a measurement of the neutron lifetime using ultra-cold neutrons stored in an asymmetric magnetic trap
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Assessment of the Potential to Produce Oil and Store CO2 Using CO2 Enhanced Oil Recovery at Conventional Oil Fields in the US Using EIA’s Oil Field Databases
Presented by NETL at Carbon Capture, Utilization and Storage Conference 2018, 03/18
How Much Oil and CO2? At What Cost? Bounding the Quantities of Produced Oil and Stored CO2 from CO2 EOR in Conventional Oil Fields in the United States
Presented by NETL at American Institute of Chemical Engineers, 10/18
Producing Oil and Storing CO2 by Applying CO2 EOR to the Residual Oil Zone: How Much and at What Cost?
Presented by NETL at CO2 Conference Week, 12/17
A Comprehensive Web Infrastructure for Standardizing, Storing, and Launching Density Functional Calculations of Materials and Chemical Compounds
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Pylot / Co-Pylot / eCo-Pylot: Software for Collecting Storing and Analyzing Supercomputer Performance Data into Databases.
Abstract not provided.
Disposition Options for Mk-42 Target Segments Stored at ORNL
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LDMS v4: Writing Sampler and Store Plugins (updated for LDMSCON2020).
Abstract not provided.
Scatter gather using key-value store
Scatter gather operation(s) are performed by accessing a shared memory that is shared amongst nodes interconnected through network(s) and having a CNS shared amongst the nodes. Data is gathered from multiple processes at corresponding multiple nodes into location(s) in the CNS, and tuple(s) having a same tuple name are created in the CNS. The tuple(s) have information referencing the gathered data in the location(s). Alternatively, data that has been gathered using the same tuple name is scattered to multiple processes participating in the CNS. The scattering uses the tuple(s) in the CNS, and is performed from the location(s) into other location(s) at one or multiple nodes for one or multiple processes at the corresponding one or multiple nodes. Both the gathering data and the scattering data may also be performed.
Storing molecule within porous materials with a surface molecular barrier layer
In some aspects, the present disclosure provides compositions comprising a nanoporous material such as a metal organic framework and an amine containing compound. In some aspects, these compositions may be used to improve the affinity of a guest molecule to the nanoporous material relative a nanoporous material which had not been treated with the amine containing compound.
Neutrinos from Stored Muons (nuSTORM)
The 2020 Update of the European Strategy for Particle Physics (ESPP) (see https://cds.cern.ch/record/2720129 ) recommended that muon beam R&D should be considered a high-priority future initiative and that a programme of experimentation be developed to determine the neutrino cross-sections required to extract the most physics from the DUNE and Hyper-K long-baseline experiments. The ENUBET and nuSTORM collaborations have begun to work within and alongside the CERN Physics Beyond Colliders study group and the international Muon Collider collaboration to carry out a joint, five-year R&D programme to deliver a detailed plan for the implementation of an infrastructure in which: (1) ENUBET and nuSTORM deliver the neutrino cross-section measurement programme identified in the ESPP and allow sensitive searches for physics beyond the Standard Model to be carried out; and in which (2) A 6D muon ionisation cooling experiment is delivered as part of the technology development programme defined by the international Muon Collider collaboration. This document summarises the status of development of the nuSTORM and 6D cooling experiments and identifies opportunities for collaboration in the development of the initiative outlined above.
An LDMS v3 function store configuration example from the NCSA analysis pipeline.
Abstract not provided.
Nanometer scale nonvolatile memory device and method for storing binary and quantum memory states
Example implementations include an electronic memory device with a metallic layer having a first planar crystalline structure, a first encapsulating layer including an encapsulating material having a second planar crystalline structure, and disposed adjacent to a first planar surface of the metallic layer, and a second encapsulating layer including the encapsulating material, and disposed adjacent to a second planar surface of the metallic layer. Example implementations also include a method of depositing graphite crystals onto a substrate to form a gate bottom layer, depositing BN crystals onto the graphite bottom layer to form a BN bottom layer, depositing tungsten ditelluride (WTe2) crystals onto the BN bottom layer to form a metallic layer, depositing the BN crystals onto the BN bottom layer and the metallic layer to form a BN top layer, and depositing the graphite crystals onto the BN top layer to form a gate top layer.
Techniques for storing data to enhance recovery and detection of data corruption errors
Often there are errors when reading data from computer memory. To detect and correct these errors, there are multiple types of error correction codes. Disclosed is an error correction architecture that creates a codeword having a data portion and an error correction code portion. Swizzling rearranges the order of bits and distributes the bits among different codewords. Because the data is redistributed, a potential memory error of up to N contiguous bits, where N for example equals 2 times the number of codewords swizzled together, only affects up to, at most, two bits per swizzled codeword. This keeps the error within the error detecting capabilities of the error correction architecture. Furthermore, this can allow improved error correction and detection without requiring a change to error correcting code generators and checkers.