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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 307 records · Page 17
All sourcesNASA NTRSDOE OSTI
NASA NTRS2015

Nano-ADEPT Aeroloads Wind Tunnel Test

Analysis completed since the test suggests that all test objectives were met– This claim will be verified in the coming weeks as the data is examined further– Final disposition of test objective success will be documented in a final reportsubmitted to NASA stakeholders (early August 2015)– Expect conference paper in early 2016• Data products and observations made during testing will be used to refinecomputational models of Nano-ADEPT• Carbon fabric relaxed from its pre-test state during the test– System-level tolerance for relaxation will be driven by destination-specific andmission-specific aerothermal and aerodynamic requirements• Bonus experiment of asymmetric shape demonstrates that an asymmetricdeployable blunt body can be used to generate measureable lift– With a strut actuation system and a robust GN&C algorithm, this effect could beused to steer a blunt body at hypersonic speeds to aid precision landing

atmospheric entry↗
NASA NTRS2016

Nano-ADEPT Aeroloads Wind Tunnel Test

A wind tunnel test of the Adaptable Deployable Entry and Placement Technology (ADEPT) was conducted in April 2015 at the US Army's 7 by10 Foot Wind Tunnel located at NASA Ames Research Center. Key geometric features of the fabric test article were a 0.7 meter deployed base diameter, a 70 degree half-angle forebody cone angle, eight ribs, and a nose-to-base radius ratio of 0.7. The primary objective of this wind tunnel test was to obtain static deflected shape and pressure distributions while varying pretension at dynamic pressures and angles of attack relevant to entry conditions at Earth, Mars, and Venus. Other objectives included obtaining aerodynamic force and moment data and determining the presence and magnitude of any dynamic aeroelastic behavior (buzz/flutter) in the fabric trailing edge. All instrumentation systems worked as planned and a rich data set was obtained. This paper describes the test articles, instrumentation systems, data products, and test results. Four notable conclusions are drawn. First, test data support adopting a pre-tension lower bound of 10 foot pounds per inch for Nano-ADEPT mission applications in order to minimize the impact of static deflection. Second, test results indicate that the fabric conditioning process needs to be reevaluated. Third, no flutter/buzz of the fabric was observed for any test condition and should also not occur at hypersonic speeds. Fourth, translating one of the gores caused ADEPT to generate lift without the need for a center of gravity offset. At hypersonic speeds, the lift generated by actuating ADEPT gores could be used for vehicle control.

system engineering↗
NASA NTRS2017

Polymer Nanofiber Based Reversible Nano-Switch/Sensor Diode (Nanosssd) Device

A nanostructure device is provided and performs dual functions as a nano-switching/sensing device. The nanostructure device includes a doped semiconducting substrate, an insulating layer disposed on the doped semiconducting substrate, an electrode formed on the insulating layer, and at least one polymer nanofiber deposited on the electrode. The at least one polymer nanofiber provides an electrical connection between the electrode and the substrate and is the electroactive element in the device.

Miranda, Felix A.↗
NASA NTRS2018

Polymer Nanofiber Based Reversible Nano-Switch/Sensor Schottky Diode (Nanosssd) Device

A nanostructure device is provided and performs dual functions as a nano-switching/sensing device. The nanostructure device includes a doped semiconducting substrate, an insulating layer disposed on the doped semiconducting substrate, an electrode formed on the insulating layer, and at least one polymer nanofiber deposited on the electrode. The at least one polymer nanofiber provides an electrical connection between the electrode and the substrate and is the electroactive element in the device.

Miranda, Felix A.↗
NASA NTRSDate not supplied

BRAINSTACK – A Platform for Artificial Intelligence & Machine Learning Collaborative Experiments on a Nano-Satellite

As space missions continue to become more ambitious, complex, and distant to Earth, the need for advanced on-board intelligent decision making to guide everything from mission operations to fault detection and recovery has become a major front of space research. While the prevalence of research on such Artificial Intelligence / Machine Learning (AI/ML) modules has exploded, the capacity to experimentally validate such modules in space in a rapid and inexpensive format has not. To this end, the Nano Orbital Workshop (NOW) group at NASA Ames Research Center has been at the forefront of performing initial flight evaluation tests of ‘commercially’ available AI/ML computational platforms via the TechEdSat (TES-n) flight series as part of what is programmatically referred to as the BRAINSTACK. BRAINSTACK will provide an orbital AI/ML evaluation laboratory where computational experiments are pre-loaded into memory prior to launch, and then executed as desired during the mission, with results reported back and program tweaks or new data sets uploaded as needed. Processors selected as part of the BRAINSTACK are of ideal size, packaging, and power consumption for easy integration into a cube satellite structure. These experiments have included the evaluation of small, high-performance GPUs and more recently, neuromorphic processors in LEO operations. Neuromorphic processors are of particular interest due to their superior computational power efficiency over GPUs. The first TES-n flight test of an Intel first-generation Loihi neuromorphic processor launched on January 13, 2022 and continues to operate in orbit despite almost no space environment modifications. The Intel Loihi Gen-1 is characterized by a 14nm 128-core Spiking Neural Network (SNN) able to support on-chip training. This experiment utilized a Loihi packaged in the ‘Kapoho Bay’ USB module, providing a relatively straight-forward interface to the bus avionics system. The Kapoho Bay was in turn managed by a host Intel Pentium single-board computer to handle scheduling of the AI/ML application payloads, and communications with the satellite vehicle manager. The recently released Intel Loihi Gen-2, able to support integer-valued spike payloads and produced using 7nm process, will form part of the basis of the evolving BRAINSTACK in the upcoming three TES-n/NOW flights. Additionally, it is planned to measure the radiation environment these processors experience to understand any degradation or computational artifacts caused by long term space radiation exposure on these novel architectures. This evolving flexible and collaborative environment involving various research teams across NASA and other organizations is intended to be a convenient orbital test platform from which many anticipated future space AI/ML applications may be initially tested.

Artificial Intelligence↗
NASA NTRSDate not supplied

BRAINSTACK – A Platform for Artificial Intelligence & Machine Learning Collaborative Experiments on a Nano-Satellite

As space missions continue to become more ambitious, complex, and distant to Earth, the need for advanced on-board intelligent decision making to guide everything from mission operations to fault detection and recovery has become a major front of space research. While the prevalence of research on such Artificial Intelligence / Machine Learning (AI/ML) modules has exploded, the capacity to experimentally validate such modules in space in a rapid and inexpensive format has not. To this end, the Nano Orbital Workshop (NOW) group at NASA Ames Research Center has been at the forefront of performing initial flight evaluation tests of ‘commercially’ available AI/ML computational platforms via the TechEdSat (TES-n) flight series as part of what is programmatically referred to as the BRAINSTACK. BRAINSTACK will provide an orbital AI/ML evaluation laboratory where computational experiments are pre-loaded into memory prior to launch, and then executed as desired during the mission, with results reported back and program tweaks or new data sets uploaded as needed. Processors selected as part of the BRAINSTACK are of ideal size, packaging, and power consumption for easy integration into a cube satellite structure. These experiments have included the evaluation of small, high-performance GPUs and more recently, neuromorphic processors in LEO operations. Neuromorphic processors are of particular interest due to their superior computational power efficiency over GPUs. The first TES-n flight test of an Intel first-generation Loihi neuromorphic processor launched on January 13, 2022 and continues to operate in orbit despite almost no space environment modifications. The Intel Loihi Gen-1 is characterized by a 14nm 128-core Spiking Neural Network (SNN) able to support on-chip training. This experiment utilized a Loihi packaged in the ‘Kapoho Bay’ USB module, providing a relatively straight-forward interface to the bus avionics system. The Kapoho Bay was in turn managed by a host Intel Pentium single-board computer to handle scheduling of the AI/ML application payloads, and communications with the satellite vehicle manager. The recently released Intel Loihi Gen-2, able to support integer-valued spike payloads and produced using 7nm process, will form part of the basis of the evolving BRAINSTACK in the upcoming three TES-n/NOW flights. Additionally, it is planned to measure the radiation environment these processors experience to understand any degradation or computational artifacts caused by long term space radiation exposure on these novel architectures. This evolving flexible and collaborative environment involving various research teams across NASA and other organizations is intended to be a convenient orbital test platform from which many anticipated future space AI/ML applications may be initially tested.

Artificial Intelligence↗
NASA NTRS2023

BRAINSTACK – A Platform for Artificial Intelligence & Machine Learning Collaborative Experiments on a Nano-Satellite

As the space economy continues to expand through increasingly easy access to advanced and inexpensive technology, space missions themselves have become more ambitious with exploration targets growing ever distant while simultaneously requiring larger guidance and communication budgets. These conflicting desires of distance and control drive the need for advanced on-board intelligent decision making to reduce communication and control limitations by automating as many mission functions as possible in-situ. While the amount of research on such Artificial Intelligence and Machine Learning (AI/ML) software modules has grown exponentially, the capacity to experimentally validate such software modules in space in a rapid and inexpensive format has not. To this end, the Nano Orbital Workshop (NOW) group at NASA Ames Research Center has been at the forefront of performing initial flight evaluation tests of ‘commercially’ available bleeding-edge computational platforms via what is programmatically referred to as the BrainStack on the TechEdSat (TES-n) flight series. This on-orbit computational platform provides an evaluation laboratory where advanced software experiments are pre-loaded into memory prior to launch, then executed as payloads during mission operations with results reported back and program tweaks or new training sets uploaded as needed. Processors selected as part of the BrainStack are of ideal size, packaging, and power consumption for easy integration into a cube satellite structure. These experiments have included the evaluation of small, high-performance GPUs and, more recently, neuromorphic processors, in LEO operations. Neuromorphic processors are of particular interest due to their superior power efficiency over GPUs in intelligent automation applications. The first TES-n flight test of an Intel first-generation Loihi neuromorphic processor launched on TES-13, January 13, 2022, and continues to operate in orbit despite no significant modifications to harden the processor against the space environment. The Intel Loihi Gen-1 on TES-13 is characterized by a 14nm 128-core Spiking Neural Network (SNN) able to support on-chip training. The processor is packaged in the Kapoho Bay USB module, providing a relatively straight-forward interface to the bus avionics system. The Kapoho Bay was in turn managed by an Intel Pentium single-board computer to handle scheduling of the software application payloads and communications with the satellite’s primary computer. The recently released Intel Loihi Gen-2, able to support integer-valued spike payloads and produced using 7nm process, will form part of the continually evolving BrainStack in the upcoming three TES-n/NOW flights. The Kapoho Point unit will incorporate eight Loihi-2 processors, enabling neural networks of up to one million neurons and one billion synapsis. Additionally, it is planned to measure the radiation environment these processors experience to understand any degradation or computational artifacts caused by long term space radiation exposure on these novel architectures. This evolving flexible and collaborative environment involving various research teams across NASA and other organizations is intended to be a convenient orbital test platform from which many anticipated future space automation applications may be initially tested.

Artificial Intelligence↗
DOE OSTI2020

Titanium-group nano-whiskers and method of production

Disclosed herein are structures comprising a titanium, zirconium, or hafnium powder particle with titanium carbide, zirconium carbide, or hafnium carbide (respectively) nano-whiskers grown directly from and anchored to the powder particle. Also disclosed are methods for fabrication of such structures, involving heating the powder particles and exposing the particles to an organic gas.

Seals, Roland D.↗
DOE OSTI2022

System and method for repeated metal deposition-dewetting steps to form a nano-particle etching mask producing thicker layer of engraved metasurface

A method and system is disclosed for creating an optical component having a spatially controlled refractive index and uniform anti-reflective layer. The method may involve alternately depositing and dewetting two or more thin metal material layers on the substrate to form a mask having a spatially varying nano-particle distribution, and with an increased thickness beyond what could be achieved using a single, thick layer of the same material. The substrate may then be etched, using the mask, to imprint a spatially patterned nanostructure pattern on a surface the substrate in accordance with the mask.

Feigenbaum, Eyal↗
DOE OSTI2023

Titanium-group nano-whiskers and method of production

Disclosed herein are structures comprising a titanium, zirconium, or hafnium powder particle with titanium carbide, zirconium carbide, or hafnium carbide (respectively) nano-whiskers grown directly from and anchored to the powder particle. Also disclosed are methods for fabrication of such structures, involving heating the powder particles and exposing the particles to an organic gas.

Menchhofer, Paul A.↗
DOE OSTI2025

Boron‐Doped Nano‐Crystalline Coated Carbon Fibers for Phasic Dopamine Sensing

Real time, chronic electrochemical detection of neurotransmitters will provide a positive step in the treatment and understanding of neurological disease. However, current electrodes using carbon fibers (CF) fail to perform chronically. While diamond-based coatings show promise in improving their longevity, achieving a uniform layer of such coatings on CFs is challenging, and the electrodes often lose sensitivity after coating. In this work, a complete and uniform boron-doped nanocrystalline material grown in a diamond reactor (B-NCD) was developed to coat CF microelectrodes for neurochemical sensing. The coating was characterized electrically, optically, mechanically, and chemically. The B-NCD coated CF electrodes were able to detect phasic dopamine at a sensitivity comparable to the most widely used alternatives (uncoated and PEDOT:Nafion coated CFs). During biofouling testing, the B-NCD coated CF electrodes demonstrated better stability than uncoated CFs and comparable performance to PEDOT:Nafion coated CFs. Moreover, B-NCD exhibited no signs of degradation during consecutive FSCV applications, while uncoated and PEDOT:Nafion coated CF electrodes degraded significantly over time. Furthermore, the B-NCD coating supported the survival and development of neurons and astrocytes in vitro, exhibited excellent adhesion and durability during mechanical bending testing, and enabled successful in vivo recording of phasic dopamine release in the rat brain. Overall, B-NCD coated CFs present as an ideal candidate for chronic, flexible neural implantable electrodes for long-term neurochemical monitoring.

carbon fiber↗
DOE OSTI2020

Nano-FET-enabled biosensors: Materials perspective and recent advances in North America

Field-effect transistor (FET) is a very promising platform for biosensor applications due to its magnificent properties, including label-free detection, high sensitivity, fast response, real-time measurement capability, low running power, and the feasibility to miniaturize to a portable device. 1D (e.g. carbon nanotubes, Si nanowires, conductive polymer nanowires, 1D metal oxides, and others) and 2D (e.g. graphene materials, transition metal dichalcogenides, black phosphorus, and 2D metal oxides) materials, with their unique structural and electronic properties that are unavailable in bulk materials, have helped improve the sensitivity of FET biosensors and enabled detection down to single molecule. In this article, we give insights into the rapidly evolving field of 1D and 2D materials-based FET biosensors, with an emphasis on structure and electronic properties, synthesis, and biofunctionalization approaches of these nanomaterials. In addition, the progress in the 1D/2D-FET biosensors in North America, in the last decade, is summarized in tables. Moreover, challenges and future perspectives of 1D/2D-FET biosensors are covered.

1D materials↗
DOE OSTI2020

Transparent window film with embedded nano-shades for thermoregulation

Every year, more than 20% of the energy consumption in the United States and more than 10% of the global consumption is used towards HVAC (Heating, Ventilating, Air-Conditioning) systems in buildings. Although continued efforts in advancing renewable energy, efficient appliances, and smart building systems are desired, the most critical factors that cause extensive indoor energy consumption are thermal leakage and thermal waste generation. We demonstrate a smart window film that allows natural lighting with a clear view while blocking oblique incident sunlight to make the building interior cooler without using electricity or generating heat. Building upon the concept of, often overlooked, traditional window shades and privacy window films, we emphasize and elevate the application and impact of using magnetically arranged nanoscale material for energy conservation purpose in buildings. By embedding vertically aligned Ni flakes (VANF) in a polymer matrix film, nanoscale mirror array serve as shades to block the high-angled incident sunlight, blocking approximately 73% of total solar irradiance and bringing down the indoor temperature by 1.92 °C during the daytime in a model house while providing nearly 90% transparency. Global application was simulated by applying the respective solar angle and intensity at selected latitudes, indicating their potential. This passively energy-conserving smart film is a low cost, maintenance free, and simple product for users and easy-to-manufacture product for the industry.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Facile and quantitative estimation of strain in nanobubbles with arbitrary symmetry in 2D semiconductors verified using hyperspectral nano-optical imaging

When layers of van der Waals materials are deposited via exfoliation or viscoelastic stamping, nanobubbles are sometimes created from aggregated trapped fluids. Though they can be considered a nuisance, nanobubbles have attracted scientific interest in their own right owing to their ability to generate large in-plane strain gradients that lead to rich optoelectronic phenomena, especially in the semiconducting transition metal dichalcogenides. Determination of the strain within the nanobubbles, which is crucial to understanding these effects, can be approximated using elasticity theory. However, the Föppl–von Kármán equations that describe strain in a distorted thin plate are highly nonlinear and often necessitate assuming circular symmetry to achieve an analytical solution. Here, we present an easily implemented numerical method to solve for strain tensors of nanobubbles with arbitrary symmetry in 2D crystals. The method only requires topographic information from atomic force microscopy and the Poisson ratio of the 2D material. We verify that this method reproduces the strain for circularly symmetric nanobubbles that have known analytical solutions. Finally, we use the method to reproduce the Grüneisen parameter of the E' mode for 1L-WS2 nanobubbles on template-stripped Au by comparing the derived strain with measured Raman shifts from tip-enhanced Raman spectroscopy, demonstrating the utility of our method for estimating localized strain in 2D crystals.

2D materials↗
DOE OSTI2024

Effects of electron beam irradiation on CrMnV and CrMnTiV high entropy alloys: Nano-mechanical, structural, and thermodynamic perspectives

Beam exit windows are crucial components of any particle accelerator as they provide an interface between the beamline vacuum and target material at atmospheric media. For high beam power machines, special materials and designs are required to withstand high radiation and mechanical loads, while minimizing energy loss during transition and maximizing window lifetime. This research investigates the impact of electron beam exposure to bulk CrMnV and CrMnTiV high entropy alloys (HEAs) with the primary goal of identifying suitable candidate materials for the design of robust and durable exit window settings. The selection criteria include intrinsic characteristics, power dissipation, and mechanical responses. According to the thermodynamic calculations, both equiatomic CrMnV and the addition of 7% of Ti with equiatomic CrMnV yield solid-solutions phases. The structural and mechanical properties of CrMnV and CrMnTiV samples were tested using field emission scanning electron microscopy, atomic force microscopy, scanning electron microcopy with energy dispersive x-ray spectroscopy, x-ray diffraction, and nanoindentation before and after exposure to a dose of ~66 kGy from a 10 MeV e-beam accelerator. Despite exhibiting beam transmission characteristics comparable to Cr and V, the indentation hardness of HEAs exceeded that of the Cr and V samples by five to six times. The examination of the CrMnTiV irradiated samples revealed organized deformation patterns depicting new features, which we suspect twinning and twin boundaries due to the addition of Ti to CrMnV. Ti, a hexagonal-close-packed crystal structure, is commonly known for deformation twinning behavior.

36 MATERIALS SCIENCE↗
DOE OSTI2022

In Situ Nano-Indentation of a Gold Sub-Micrometric Particle Imaged by Multi-Wavelength Bragg Coherent X-ray Diffraction

The microstructure of a sub-micrometric gold crystal during nanoindentation is visualized by in situ multi-wavelength Bragg coherent X-ray diffraction imaging. The gold crystal is indented using a custom-built atomic force microscope. A band of deformation attributed to a shear band oriented along the (221) lattice plane is nucleated at the lower left corner of the crystal and propagates towards the crystal center with increasing applied mechanical load. After complete unloading, an almost strain-free and defect-free crystal is left behind, demonstrating a pseudo-elastic behavior that can only be studied by in situ imaging while it is invisible to ex situ examinations. The recovery is probably associated with reversible dislocations nucleation/annihilation at the side surface of the particle and at the particle-substrate interface, a behavior that has been predicted by atomistic simulations. The full recovery of the particle upon unloading sheds new light on extraordinary mechanical properties of metal nanoparticles obtained by solid-state dewetting.

36 MATERIALS SCIENCE↗