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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 433 records · Page 24
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Elastic-instability–enabled locomotion

Locomotion of an organism interacting with an environment is the consequence of a symmetry-breaking action in space-time. Here we show a minimal instantiation of this principle using a thin circular sheet, actuated symmetrically by a pneumatic source, using pressure to change shape nonlinearly via a spontaneous buckling instability. This leads to a polarized, bilaterally symmetric cone that can walk on land and swim in water. In either mode of locomotion, the emergence of shape asymmetry in the sheet leads to an asymmetric interaction with the environment that generates movement––via anisotropic friction on land, and via directed inertial forces in water. Scaling laws for the speed of the sheet of the actuator as a function of its size, shape, and the frequency of actuation are consistent with our observations. The presence of easily controllable reversible modes of buckling deformation further allows for a change in the direction of locomotion in open arenas and the ability to squeeze through confined environments––both of which we demonstrate using simple experiments. Our simple approach of harnessing elastic instabilities in soft structures to drive locomotion enables the design of novel shape-changing robots and other bioinspired machines at multiple scales.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Deep representation learning improves prediction of LacI-mediated transcriptional repression

Significance The understanding of protein function increases with new experimental and evolutionary datasets. A major challenge is to apply machine learning to these datasets to capture essential features of protein function. Here, we analyze the experimentally determined repression function for tens of thousands of mutants of the LacI protein. This study provides a continuous, noncategorical repression value across a majority of all single mutations and for thousands of higher-order mutations. To develop a top-performing model for the prediction of repression by LacI, we compare several leading variant effect prediction algorithms. A deep representation learning paradigm, first trained across millions of proteins from all known protein families and then fine-tuned using LacI experimental data, offers the highest predictive performance of repression function.

42 ENGINEERING↗
DOE OSTI2021

AutoAtlas: Neural Network for 3D Unsupervised Partitioning and Representation Learning

Here we present a novel neural network architecture called AutoAtlas for fully unsupervised partitioning and representation learning of 3D brain Magnetic Resonance Imaging (MRI) volumes. AutoAtlas consists of two neural network components: one neural network to perform multi-label partitioning based on local texture in the volume, and a second neural network to compress the information contained within each partition. We train both of these components simultaneously by optimizing a loss function that is designed to promote accurate reconstruction of each partition, while encouraging spatially smooth and contiguous partitioning, and discouraging relatively small partitions. We show that the partitions adapt to the subject specific structural variations of brain tissue while consistently appearing at similar spatial locations across subjects. AutoAtlas also produces very low dimensional features that represent local texture of each partition. We demonstrate prediction of metadata associated with each subject using the derived feature representations and compare the results to prediction using features derived from FreeSurfer anatomical parcellation. Since our features are intrinsically linked to distinct partitions, we can then map values of interest, such as partition-specific feature importance scores onto the brain for visualization.

42 ENGINEERING↗
DOE OSTI2024

Structural insights into the formation of repulsive netrin guidance complexes

Netrins dictate attractive and repulsive responses during axon growth and cell migration, where the presence of the receptor Uncoordinated-5 (UNC-5) on target cells results in repulsion. Here, we showed that UNC-5 is a heparin-binding protein, determined its structure bound to a heparin fragment, and could modulate UNC-5–heparin affinity using a directed evolution platform or structure-based rational design. We demonstrated that UNC-5 and UNC-6/netrin form a large, stable, and rigid complex in the presence of heparin, and heparin and UNC-5 exclude the attractive UNC-40/DCC receptor from binding to UNC-6/netrin to a large extent. Caenorhabditis elegans with a heparin-binding–deficient UNC-5 fail to establish proper gonad morphology due to abrogated cell migration, which relies on repulsive UNC-5 signaling in response to UNC-6. Combining UNC-5 mutations targeting heparin and UNC-6/netrin contacts results in complete cell migration and axon guidance defects. Our findings establish repulsive netrin responses to be mediated through a glycosaminoglycan-regulated macromolecular complex.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Topological supramolecular network enabled high-conductivity, stretchable organic bioelectronics

Intrinsically stretchable bioelectronic devices based on soft and conducting organic materials have been regarded as the ideal interface for seamless and biocompatible integration with the human body. A remaining challenge is to combine high mechanical robustness with good electrical conduction, especially when patterned at small feature sizes. We develop a molecular engineering strategy based on a topological supramolecular network, which allows for the decoupling of competing effects from multiple molecular building blocks to meet complex requirements. We obtained simultaneously high conductivity and crack-onset strain in a physiological environment, with direct photopatternability down to the cellular scale. We further collected stable electromyography signals on soft and malleable octopus and performed localized neuromodulation down to single-nucleus precision for controlling organ-specific activities through the delicate brainstem.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Constructing Neural Network Based Models for Simulating Dynamical Systems

Dynamical systems see widespread use in natural sciences like physics, biology, and chemistry, as well as engineering disciplines such as circuit analysis, computational fluid dynamics, and control. For simple systems, the differential equations governing the dynamics can be derived by applying fundamental physical laws. However, for more complex systems, this approach becomes exceedingly difficult. Data-driven modeling is an alternative paradigm that seeks to learn an approximation of the dynamics of a system using observations of the true system. In recent years, there has been an increased interest in applying data-driven modeling techniques to solve a wide range of problems in physics and engineering. Here this article provides a survey of the different ways to construct models of dynamical systems using neural networks. In addition to the basic overview, we review the related literature and outline the most significant challenges from numerical simulations that this modeling paradigm must overcome. Based on the reviewed literature and identified challenges, we provide a discussion on promising research areas.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

The Artificial Intelligence Ontology: LLM-Assisted Construction of AI Concept Hierarchies

The Artificial Intelligence Ontology (AIO) is a systematization of artificial intelligence (AI) concepts, methodologies, and their interrelations. Developed via manual curation, with the additional assistance of large language models (LLMs), AIO aims to address the rapidly evolving landscape of AI by providing a comprehensive framework that encompasses both technical and ethical aspects of AI technologies. The primary audience for AIO includes AI researchers, developers, and educators seeking standardized terminology and concepts within the AI domain. We use the term “branches” for classes, and their subclasses, in our ontology that are subclasses of owl:Thing. AIO contains eight branches: Bias, Layer, Machine Learning Task, Mathematical Function, Model, Network, Preprocessing, and Training Strategy, each designed to support the modular composition of AI methods and facilitate a deeper understanding of deep learning architectures and ethical considerations in AI. AIO uses the Ontology Development Kit (ODK) for its creation and maintenance, with its content being more easily updated through AI-driven curation support. This approach not only ensures the ontology's relevance amidst the fast-paced advancements in AI but also significantly enhances its utility for researchers, developers, and educators by simplifying the integration of new AI concepts and methodologies. The ontology's utility is demonstrated through the annotation of AI methods data in a catalog of AI research publications and the integration into the BioPortal ontology resource, highlighting its potential for cross-disciplinary research. The AIO ontology is open source and is available on GitHub ( https://w3id.org/aio/ ) and BioPortal ( https://bioportal.bioontology.org/ontologies/AIO ).

Joachimiak, Marcin P. [Biosystems Data Science Dep↗
DOE OSTI2020

Biocompatibility of a novel heat-treated and ceramic-coated magnesium alloy (Mg–1.2Zn–0.5Ca–0.5Mn) for resorbable skeletal fixation devices

Our recent exploration into the use of biodegradable metals and surface treatments resulting in sufficient strength for skeletal reconstruction applications has led to the need to test these devices’ cytotoxicity. More specifically, our group has developed a resorbable magnesium alloy, Mg–1.2Zn–0.5Ca–0.5Mn, that can be strengthened by heat treatment and coated with a ceramic layer offering time-certain resorption of a medical device. This in vitro study shows that these treatments do not result in cytotoxicity. Both heat-treated (HT) and HT + ceramic-coated (sol–gel) coupons demonstrated more than 70% viability. Thus, these processing steps are likely to be useful in producing biocompatible, resorbable implants that incorporate our Mg–1.2Zn–0.5Ca–0.5Mn alloy.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Pathogen Management Kit (Technical Bulletin)

Sandia National Laboratories (SNL) has developed and produced a simple add-on kit (Pathogen Management Kit, or PMK) that can quickly add on to ventilators of all types to disinfect exhaled air to keep healthcare workers safe.

42 ENGINEERING↗
DOE OSTI2020

COVID-19 LDRD Project Summaries

Sandia National Laboratories currently has 27 COVID-related Laboratory Directed Research & Development (LDRD) projects focused on helping the nation during the pandemic. These LDRD projects cross many disciplines including bioscience, computing & information sciences, engineering science, materials science, nanodevices & microsystems, and radiation effects & high energy density science.

36 MATERIALS SCIENCE↗
DOE OSTI2020

2019 Lighting R&D Opportunities

BTO’s Lighting R&D Opportunities is updated annually and covers foundational R&D needs for lighting technology and lighting application understanding that will result in maximum energy savings while also optimizing for building occupant well-being and productivity.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

Swab Tensile Testing - HP Rev 10 Summary

This tensile test is to mimic the worst-case scenario of the swab being caught on an obstruction when being pulled out of the nasopharyngeal space. It is to determine how much tensile force the swab can withstand without breaking.

42 ENGINEERING↗
DOE OSTI2020

Swab Testing Results- HP PA11 Rev 8 Summary

This tensile test is to mimic the worst-case scenario of the swab being caught on an obstruction when being pulled out of the nasopharyngeal space. It is to determine how much tensile force the swab can withstand without breaking.

42 ENGINEERING↗
DOE OSTI2020

Swab Testing Results Summary

Swabs Tested: 1. FAST Spiral NP Swab, designed and supplied by Abiogenix, printed from polyamide 11 (Nylon) using HP Multi Jet Fusion printers. 2. NP Swab designed by USF Health and Northwell Health in collaboration with Formlabs. Printed at LLNL using the Form 3B stereolithography printer in Formlabs Surgical Guide resin.

42 ENGINEERING↗
DOE OSTI2020

Lawrence Livermore National Laboratory ABSL-3 Tissue Digester (8 July 2020 Run Data Report, Report 3)

Purpose: Provide data from a water run of the LLNL tissue digester to confirm operational status for possible extension of Medical Waste permit, since the annual recertification maintenance and testing could not take place due to the shelter-in-place of the tissue digester vendor who is located outside of California. This is a unique medical waste device and the vendor who certifies our other medical waste equipment (permitted autoclaves), does not service the tissue digester.

42 ENGINEERING↗
DOE OSTI2020

Filter Adapters for BI-PAP and CPAP Equipment

Researchers at Los Alamos National Laboratory are researching and designing technology intended to help reduce the exposure of patients and medical staff to pathogens, such as SARS-CoV-2, while providing medical staff the dexterity and flexible work conditions necessary to conduct routine and complex patient care on infected patients. This investigative technology would potentially enable hospitals to adapt, utilize, and repurpose existing Bilevel Positive Airway Pressure (BiPAP) or Continuous Positive Airway Pressure (C-PAP) equipment in the fight against COVID-19, which would potentially save hospitals significant money, reduce exposure to staff and patients, and increase the supply of critically needed devices at a time when supplies are scarce in the market place. A provisional patent has been filed on the technology and we are seeking a commercialization partner to license and further develop the product for commercial purposes, including processing the device through the FDA emergency use authorization or clearance process. One hospital system has indicated that upon FDA authorization or clearance, it would have interest in the product.

42 ENGINEERING↗
DOE OSTI2020

Air Evacuation Bridge

Researchers at Los Alamos National Laboratory are researching and designing technology intended to help reduce the exposure of patients and medical staff to pathogens, such as SARS-CoV-2 while providing medical staff the dexterity and flexible work conditions necessary to conduct routine and complex patient care on infected patients. This investigative technology would potentially enable hospitals to adapt, utilize, and re-purpose existing and otherwise infrequently used equipment in the fight against COVID-19, thereby saving hospitals significant money, reducing exposure to staff and patients, and increasing the supply of critically needed devices at a time when supplies are scarce in the market place. A provisional patent has been filed on the technology and we are seeking a commercialization partner to license and further develop the product for commercial purposes, including processing the device through the FDA authorization or clearance process. Once FDA has cleared the product, at least one hospital system has expressed interest in the experimental test components for use in their hospitals.

42 ENGINEERING↗
DOE OSTI2020

CPAP Ventilators Needed for Rapid Response to COVID-19 by Modification of CPAP Equipment

Early on in the COVID-19 pandemic, potential ventilator shortages were a critical issue identified by national health care providers. Capacity modeling at the time suggested patient demand may exceed ventilator supply. Thus, the challenge became finding an urgent interim solution to meet health care needs. Our initial hypothesis was that CPAP technology could be modified to provide similar functionality to a ventilator, relieving demand and allowing physicians to decide which patients need high end machines, ultimately saving lives. In conjunction with medical experts and pulmonologists, we were able to identify three key thrusts associated with this research problem: (1) modification of CPAP technology to allow for 02 input that would be capable of providing ventilation; (2) development of an alarming function that would provide real-time audible alarms to alert medical personnel to critical conditions, which would be used inline with CPAP technology; and (3) a method of sterilizing expiratory air from such a system in order to protect medical personnel from biohazard, since CPAPs vent to the atmosphere. We were unable to realize results for thrust 1 (CPAP modification for 02); we identified potential safety issues associated with utilizing medical grade oxygen with a common CPAP device. In order to characterize and mitigate these issues, we would need to partner closely with a device manufacturer; such a partnership could not be achieved in the timeframe needed for this rapid response work. However, we determined that some medical grade BiPAP devices do not need this modification and that the significant progress on thrusts 2 and 3 would be sufficient to buy down risk of a massive ventilator shortage. Our team built a prototype alarm system that can be utilized with any assistive respiratory device to alert on all key conditions identified by medical personnel (high pressure, low pressure, apnea, loss of power, low battery). Finally, our team made significant progress in the rapid prototyping and demonstration of an inline UV air purifier device. The device is cost efficient and can be manufactured at scale with both commercially available and additively manufactured parts. Initial tests with SARS-CoV-2 analog bacteriophage MS2 show 99% efficacy at reducing bioburden. Following a successful demonstration of the prototype device with medical personnel, we were able to obtain follow-on (non-LDRD) funding to provide additional device characterization, validation, and production in order to respond to an immediate regional need.

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