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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

SNL Human Reliability Analysis (Capstone Final Report)

Sandia National Laboratories (SNL) requested a measure of possible human error for each state verification method for a safety mechanism performed at partnering production agencies. A team of three human factors individuals were tasked with conducting observations during site visits of both production agencies in order to complete a Human Reliability Analysis (HRA). A HRA will be used because it provides both qualitative and quantitative reports of human error. This report is the first phase of that effort, which will describe the methods which occur at one of the production agencies.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2020

AI-Science for Performance Optimization and Diagnosis of Science Instrument Federations

Next generation of science workflows are expected to be executed over complex federations composed of supercomputers, science instruments, storage systems and networks, with new additions of the edge and cloud systems and services. The sheer complexity of these multi-domain federations makes it hard to manage them and optimize their performance, as small impedance mismatches (that can dynamically develop between systems) could drastically degrade the entire federation performance. Recent proliferation of Software Defined Everything (SDX) technologies combined with containerization frameworks provide custom instruments that can monitor and collect critical measurements at various levels to support diagnoses and performance optimization; but their data too enormous for human operators and analysts to process and generate decisions. Machine Learning (ML) methods that extract critical parameters, relationships and trends from the data offer general solutions. Artificial Intelligence (AI) and ML methods must be custom-developed for these problems based on solid, rigorous foundations, since black-box approaches are often ineffective and unsound.We propose to develop comprehensive AI-Science for the performance of science federations to (i) monitor and control storage, networks, experiments, and computing systems across multiple domains via softwarization layers, at speeds and scales orders of magnitude superior to current practice, (ii) optimally realize and orchestrate complex workflows with high performance by using dynamic state and performance estimation methods, and (iii) aggregate measurements across sites and time to develop infrastructure-level profiles, optimizations and diagnoses using AI-Science based on foundational principles from ML, game theory, and information fusion areas.

Rao, Nageswara↗
DOE OSTI2022

Active Learning for Language Modeling

Foreign disinformation campaigns undermine national security. Various supervised language modeling techniques in NLP can help to understand and dismantle these campaigns, but they rely heavily on large, labeled (often by humans) datasets. This work provides a solution to this problem in the form of an active learning (AL) framework, which is used to generate labeled datasets and leverage human input for detecting disinformation. The developed AL framework utilizes task adaptive pretraining to fully leverage the unlabeled data and boost the performance of the classifier used for labeling. A disinformation rhetoric metric was developed to measure the presence of common rhetorical techniques used in text that are meant to deceive, for both the classifier and human to use in the task of identifying disinformation. This metric was combined with an uncertainty criterion to create a hybrid acquisition method for AL, and this hybrid method was tested alongside other acquisition functions. A sophisticated and robust stopping strategy was developed to signal the AL process should terminate, saving human time from being wasted on iterations that would not significantly benefit classifier performance.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2019

Multiscale porous elastomer substrates for multifunctional on-skin electronics with passive-cooling capabilities

In addition to mechanical compliance, achieving the full potential of on-skin electronics needs the introduction of other features. For example, substantial progress has been achieved in creating biodegradable, self-healing, or breathable, on-skin electronics. However, the research of making on-skin electronics with passive-cooling capabilities, which can reduce energy consumption and improve user comfort, is still rare. Herein, we report the development of multifunctional on-skin electronics, which can passively cool human bodies without needing any energy consumption. This property is inherited from multiscale porous polystyrene-block-poly(ethylene-ran-butylene)-block-polystyrene (SEBS) supporting substrates. The multiscale pores of SEBS substrates, with characteristic sizes ranging from around 0.2 to 7 µm, can effectively backscatter sunlight to minimize heat absorption but are too small to reflect human-body midinfrared radiation to retain heat dissipation, thereby delivering around 6 °C cooling effects under a solar intensity of 840 W∙m -2 . Other desired properties, rooted in multiscale porous SEBS substrates, include high breathability and outstanding waterproofing. The proof-of-concept bioelectronic devices include electrophysiological sensors, temperature sensors, hydration sensors, pressure sensors, and electrical stimulators, which are made via spray printing of silver nanowires on multiscale porous SEBS substrates. The devices show comparable electrical performances with conventional, rigid, nonporous ones. Also, their applications in cuffless blood pressure measurement, interactive virtual reality, and human–machine interface are demonstrated. Notably, the enabled on-skin devices are dissolvable in several organic solvents and can be recycled to reduce electronic waste and manufacturing cost. Such on-skin electronics can serve as the basis for future multifunctional smart textiles with passive-cooling functionalities.

42 ENGINEERING↗
DOE OSTI2023

From Neglect to Progress: Assessing Social Sustainability and Decent Work in the Tourism Sector

Measuring social sustainability performance involves assessing firms’ implementation of social goals, including working conditions, health and safety, employee relationships, diversity, human rights, community engagement, and philanthropy. The concept of social sustainability is closely linked to the notion of decent work, which emphasizes productive work opportunities with fair income, secure workplaces, personal development prospects, freedom of expression and association, and equal treatment for both genders. However, the tourism sector, known for its significant share of informal labor-intensive work, faces challenges that hinder the achievement of decent work, such as extended working hours, low wages, limited social protection, and gender discrimination. This study assesses the social sustainability of the Portuguese tourism industry. The study collected data from the “Quadros do Pessoal” statistical tables for the years 2010 to 2020 to analyze the performance of Portuguese firms in the tourism sector and compare them with one another and with the overall national performance. The study focused on indicators such as employment, wages, and work accidents. The findings reveal fluctuations in employment and remuneration within the tourism sector and high growth rates in the tourism sector compared to the national average. A persistent gender pay gap is identified, which emphasizes the need to address this issue within the tourism industry. Despite some limitations, such as the lack of comparable data on work quality globally, incomplete coverage of sustainability issues, and challenges in defining and measuring social sustainability indicators, the findings have implications for policy interventions to enhance social sustainability in the tourism industry. By prioritizing decent work, safe working conditions, and equitable pay practices, stakeholders can promote social sustainability, stakeholder relationships, and sustainable competitive advantage. Policymakers are urged to support these principles to ensure the long-term sustainability of the tourism industry and foster a more inclusive and equitable society. This study provides insights for Tourism Management, sustainable Human Resource Management, Development Studies, and organizational research, guiding industry stakeholders in promoting corporate social sustainability, firm survival, and economic growth.

Santos, Eleonora (ORCID:0000000346930804)↗
DOE OSTI2020

Polymers for Extreme Conditions Designed Using Syntax-Directed Variational Autoencoders

We report the design/discovery of new materials is highly nontrivial owing to the near-infinite possibilities of material candidates and multiple required property/performance objectives. Thus, machine learning tools are now commonly employed to virtually screen material candidates with desired properties by learning a theoretical mapping from material-to-property space, referred to as the forward problem. However, this approach is inefficient and severely constrained by the candidates that the human imagination can conceive. Thus, in this work on polymers, we tackle the materials discovery challenge by solving the inverse problem: directly generating candidates that satisfy desired property/performance objectives. We utilize syntax-directed variational autoencoders (VAE) in tandem with Gaussian process regression (GPR) models to discover polymers expected to be robust under three extreme conditions: (1) high temperatures, (2) high electric field, and (3) high temperature and high electric field, useful for critical structural, electrical, and energy storage applications. This approach to learn from and augment) human ingenuity is general and can be extended to discover polymers with other targeted properties and performance measures.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Graphene K‐Tape Meshes for Densely Distributed Human Motion Monitoring

Abstract Wearable sensors that measure parameters associated with physical activity and bodily motions have been regarded as an indispensable tool for assessing personal wellness. Recent advances in nanocomposite strain sensors have been successfully used for monitoring skin strains and other strain‐derived physiological parameters. This study complements the existing body of work and presents a flexible, self‐adhering, fabric‐based wearable sensor for measuring skin strains and human motions. Graphene nanosheet thin films are directly spray‐coated onto kinesiology tape (K‐Tape) to obtain a self‐adhering strain sensor of high sensitivity and linearity. Their stable sensing performance and high repeatability are verified, while human subject tests confirm that they adequately capture muscle engagement during functional movements. In addition, densely distributed strain monitoring can be achieved using an electrical impedance tomography measurement approach and algorithm. Spatial strain sensing is successfully demonstrated using a single strip of piezoresistive K‐Tape, as well as when a K‐Tape network or mesh pattern is formed. The results show promise for using graphene K‐Tape meshes to measure how specific major muscle groups engage during different physical activities.

Lin, Yun‐An↗
DOE OSTI2022

Realistic operation of two residential cordwood-fired outdoor hydronic heater appliances—Part 3: Optical properties of black and brown carbon emissions

Residential biomass combustion is a source of carbonaceous aerosol. Inefficient combustion, particularly of solid fuels produces large quantities of black and brown carbon (BC and BrC). These particle types are important as they have noted effects on climate forcing and human health. One method of measuring these quantities is by measurement of aerosol light-absorption and scattering, which can be performed using an aethalometer and nephelometer, respectively. These instruments are widely deployed in the study of ambient air and are frequently used in air quality modeling and source apportionment studies. In this study, we will describe (1) a method for measuring primary BC and BrC emissions from two residential log-fired wood hydronic heaters and (2) the BC and BrC emission from these devices over a wide range of operating conditions, such as cold-starts, warm-starts, four different levels of output ranging from 15% to 100% maximum rated output, and periods of repeated cycling. The range in flue-gas BC concentrations, measured using an aethalometer at the 880 nanometer (nm) wavelength, were between 5.09 × 10 2 and 2.24 × 10 4 micrograms per cubic meter (µg/m 3 ) while the scattering coefficient of the flue-gas, measured by a nephelometer at 880 nm, ranged between 2.20 × 10 3 and 8.56 × 10 5 inverse megameters (Mm –1 ). The BrC concentrations, measured using the 370 nm wavelength of an aethalometer, were between 9.10 × 10 1 and 3.56 × 10 4 µg/m 3 . The calculated Angstrom Absorption Exponent (AAE) of the flue-gas aerosol ranged between 1.54 and 3.63. Performing a comparison between the measured BC concentration and an external particulate matter (PM) concentration showed that overall BC makes up roughly a quarter of the PM emitted by either of the two appliances. Further for both appliances, the cold-start and the test phase immediately following it had the highest BC and BrC concentrations, the highest measured scattering coefficient, as well as a low AAE.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

Virtual Reality for Shoot/No-Shoot Decision Training in Law Enforcement: A Literature Review and Research Agenda

Virtual reality (VR) can materially improve “shoot / no-shoot” (SNS) training by giving officers realistic, repeatable practice making high-stakes decisions under pressure. Traditional tools—live-fire ranges and video simulators—build basics, but they cannot adapt to each officer in real time or fully mirror the complexity of the field. VR closes that gap by creating immersive scenarios that are safer, more flexible, easier to scale across units, and able to capture objective performance data. SNS decisions are not just about marksmanship; they rely on perception, judgment, memory, and the ability to hold fire when a threat is uncertain. Effective training therefore needs realism, decision complexity, and branching outcomes that reflect the true consequences of choices. These elements strengthen recognition of hostile intent while reducing false positives and building the self-control required in ambiguous situations. VR brings specific advantages: dynamic environments, full-body interaction, and the ability to measure performance with precision—enabling targeted feedback and better transfer of learning to the street. At the same time, responsible deployment must address scenario quality (credible environments and behaviors), lawful decision models, and user wellbeing (appropriate stress levels, comfort, and safety). Sandia’s VIPER Lab is positioned to lead this work. The team combines human-performance science, AI/ML, and VR/AR development with a deep equipment bench (e.g., omnidirectional treadmill, eye-tracking, haptics, multiple HMDs). This ecosystem supports building and validating next-generation SNS training that is immersive, measurable, and trustworthy. Bottom line: Investment in VR-enabled SNS training that blends evidence-based design with careful validation and legal safeguards is expected to pay off in safer, more consistent decision-making and improved community trust, delivered through training that is practical to deploy at scale.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
DOE OSTI2022

TiO 2 Nanocoatings with Controllable Crystal Type and Nanoscale Topography on Zirconia Implants to Accelerate Bone Formation

In dentistry, zirconia implants have emerged as a promising alternative for replacing missing teeth due to their superior aesthetic performance and chemical stability. To improve the osseointegration of zirconia implants, modifying their surface with hierarchical micro/nanotopography and bioactive chemical composition are two effective ways. In this work, a microscale topography was prepared on a zirconia surface using hydrofluoric acid etching, and then a 50 nm TiO 2 nanocoating was deposited via atomic layer deposition (ALD). Subsequently, an annealing treatment was used to transform the TiO 2 from amorphous to anatase and simultaneously generate nanoscale topography. Various investigations into the coating surface morphology, topography, wettability, and chemical composition were carried out using scanning electron microscopy, white light interferometry, contact‐angle measurement, X‐ray diffraction, and X‐ray photoelectron spectroscopy. In addition, in vitro cytocompatibility and osteogenic potential performance of the coatings were evaluated by human bone marrow mesenchymal stem cells (hBMSCs), and in vivo osseointegration performance was assessed in a rat femoral condyle model. Moreover, the possible mechanism was also investigated. The deposition of TiO 2 film with/without annealing treatment did not alter the microscale roughness of the zirconia surface, whereas the nanotopography changed significantly after annealing. The in vitro studies revealed that the anatase TiO 2 coating with regular wavelike nanostructure could promote the adhesion and proliferation of osteoblasts and further improve the osteogenic potential in vitro and osseointegration in vivo. These positive effects may be caused by nanoscale topography via the canonical Wnt/ β ‐catenin pathway. The results suggest that using ALD in combination with annealing treatment to fabricate a nanotopographic TiO 2 coating is a promising way to improve the osteogenic properties of zirconia implants.

Li, Nan (ORCID:0000000255156737)↗
DOE OSTI2022

Toward Human-Centric Transportation and Energy Metrics: Influence of Mode, Vehicle Occupancy, Trip Distance, and Fuel Economy

Traditional metrics measuring transportation and energy outcomes can be augmented to better represent impacts on people's lives and systems-level performance. In this context, this study introduces two novel metrics: road capacity (as number of people traveling and accessing services) and energy intensity (as energy use for people traveling and accessing services). Current national-level distributions of available data in the United States for factors contributing to the two new integrated metrics are used as context to evaluate potential outcomes. These factors include vehicle occupancy, mode share, fuel economy, and trip distance. Variations in input values provide insights on how these factors shape efficiencies in road capacity and energy intensity. Parametric sensitivity analysis indicates that the impact of each input depends upon the metric being evaluated. For the human-centered road capacity mobility metric, increasing vehicle occupancy has the largest effect – twice that of increasing mode share for bike, walk, and transit. For the energy intensity mobility metric, the effect of improving fuel economy is the largest. However, when the focus is on accessibility (instead of mobility), for both metrics the effect of lowering average trip distance is the largest. Additionally, a novel interactive tool to visualize the results for various parameter combinations makes the metrics practitioner ready. The findings suggest that the diffusion of new human-centric metrics that benchmark outcomes associated with road capacity and energy may be significant in motivating new sustainable transportation investments and efficient utilization of infrastructure, mobility assets, and services.

ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2024

Brain‐age prediction: Systematic evaluation of site effects, and sample age range and size

Abstract Structural neuroimaging data have been used to compute an estimate of the biological age of the brain (brain‐age) which has been associated with other biologically and behaviorally meaningful measures of brain development and aging. The ongoing research interest in brain‐age has highlighted the need for robust and publicly available brain‐age models pre‐trained on data from large samples of healthy individuals. To address this need we have previously released a developmental brain‐age model. Here we expand this work to develop, empirically validate, and disseminate a pre‐trained brain‐age model to cover most of the human lifespan. To achieve this, we selected the best‐performing model after systematically examining the impact of seven site harmonization strategies, age range, and sample size on brain‐age prediction in a discovery sample of brain morphometric measures from 35,683 healthy individuals (age range: 5–90 years; 53.59% female). The pre‐trained models were tested for cross‐dataset generalizability in an independent sample comprising 2101 healthy individuals (age range: 8–80 years; 55.35% female) and for longitudinal consistency in a further sample comprising 377 healthy individuals (age range: 9–25 years; 49.87% female). This empirical examination yielded the following findings: (1) the accuracy of age prediction from morphometry data was higher when no site harmonization was applied; (2) dividing the discovery sample into two age‐bins (5–40 and 40–90 years) provided a better balance between model accuracy and explained age variance than other alternatives; (3) model accuracy for brain‐age prediction plateaued at a sample size exceeding 1600 participants. These findings have been incorporated into CentileBrain ( https://centilebrain.org/#/brainAGE2 ), an open‐science, web‐based platform for individualized neuroimaging metrics.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

Gaussian process regression for radiological contamination mapping- Applied to optimal motion planning for mobile sensor platforms [Slides]

Want to achieve best representative characterization of the entire area efficiently and accurately-Unmanned aerial/ground vehicles (UAV/UGVs) for contamination mapping. Some major challenges include: Limited battery life (move smart), Human operated (fully autonomous controls) and Many measurements (predictive mapping capabilities). The objective: Develop fully autonomous controls for mobile sensor platforms to improve efficiency and maintain performance.

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2023

Disentangling Electronic Transport and Hysteresis at Individual Grain Boundaries in Hybrid Perovskites via Automated Scanning Probe Microscopy

Underlying the rapidly increasing photovoltaic efficiency and stability of metal halide perovskites (MHPs) is the advancement in the understanding of the microstructure of polycrystalline MHP thin film. Over the past decade, intense efforts have been aimed at understanding the effect of microstructures on MHP properties, including chemical heterogeneity, strain disorder, phase impurity, etc. It has been found that grain and grain boundary (GB) are tightly related to lots of microscale and nanoscale behavior in MHP thin films. Atomic force microscopy (AFM) is widely used to observe grain and boundary structures in topography and subsequently to study the correlative surface potential and conductivity of these structures. For now, most AFM measurements have been performed in imaging mode to study the static behavior; in contrast, AFM spectroscopy mode allows us to investigate the dynamic behavior of materials, e.g., conductivity under sweeping voltage. However, a major limitation of AFM spectroscopy measurements is that they require manual operation by human operators, and as such only limited data can be obtained, hindering systematic investigations of these microstructures. Here, we designed a workflow combining the conductive AFM measurement with a machine learning (ML) algorithm to systematically investigate grain boundaries in MHPs. The trained ML model can extract GBs locations from the topography image, and the workflow drives the AFM probe to each GB location to perform a current–voltage (IV) curve automatically. Then, we are able to have IV curves at all GB locations, allowing us to systematically understand the property of GBs. Using this method, we discovered that the GB junction points are less conductive, potentially more photoactive, and can play critical roles in MHP stability, while most previous works only focused on the difference between GB and grains.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

An expedited screening platform for the discovery of anti-ageing compounds in vitro and in vivo

Background: Restraining or slowing ageing hallmarks at the cellular level have been proposed as a route to increased organismal lifespan and healthspan. Consequently, there is great interest in anti-ageing drug discovery. However, this currently requires laborious and lengthy longevity analysis. Here, we present a novel screening readout for the expedited discovery of compounds that restrain ageing of cell populations in vitro and enable extension of in vivo lifespan. Methods: Using Illumina methylation arrays, we monitored DNA methylation changes accompanying long-term passaging of adult primary human cells in culture. This enabled us to develop, test, and validate the CellPopAge Clock, an epigenetic clock with underlying algorithm, unique among existing epigenetic clocks for its design to detect anti-ageing compounds in vitro. Additionally, we measured markers of senescence and performed longevity experiments in vivo in Drosophila, to further validate our approach to discover novel anti-ageing compounds. Finally, we bench mark our epigenetic clock with other available epigenetic clocks to consolidate its usefulness and specialisation for primary cells in culture. Results: We developed a novel epigenetic clock, the CellPopAge Clock, to accurately monitor the age of a population of adult human primary cells. We find that the CellPopAge Clock can detect decelerated passage-based ageing of human primary cells treated with rapamycin or trametinib, well-established longevity drugs. We then utilise the CellPopAge Clock as a screening tool for the identification of compounds which decelerate ageing of cell populations, uncovering novel anti-ageing drugs, torin2 and dactolisib (BEZ-235). We demonstrate that delayed epigenetic ageing in human primary cells treated with anti-ageing compounds is accompanied by a reduction in senescence and ageing biomarkers. Finally, we extend our screening platform in vivo by taking advantage of a specially formulated holidic medium for increased drug bioavailability in Drosophila. We show that the novel anti-ageing drugs, torin2 and dactolisib (BEZ-235), increase longevity in vivo. Conclusions: Our method expands the scope of CpG methylation profiling to accurately and rapidly detecting anti-ageing potential of drugs using human cells in vitro, and in vivo, providing a novel accelerated discovery platform to test sought after anti-ageing compounds and geroprotectors.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

Non-invasive (non-contact) measurements of human thermal physiology signals and thermal comfort/discomfort poses -A review

Heating, ventilation and air-conditioning (HVAC) systems have been adopted to create comfortable, healthy and safe indoor environments. In the control loop, the technical feature of the human demand-oriented supply can help operate HVAC effectively. Among many technical options, real time monitoring based on feedback signals from end users has been frequently reported as a critical technology to confirm optimizing building performance. Recent studies have incorporated human thermal physiology signals and thermal comfort/discomfort status as real-time feedback signals. A series of human subject experiments used to be conducted by primarily adopting subjective questionnaire surveys in a lab-setting study, which is limited in the application for reality. With the help of advanced technologies, physiological signals have been detected, measured and processed by using multiple technical formats, such as wearable sensors. Nevertheless, they mostly require physical contacts with the skin surface in spite of the small physical dimension and compatibility with other wearable accessories, such as goggles, and intelligent bracelets. Most recently, a low cost small infrared camera has been adopted for monitoring human facial images, which could detect the facial skin temperature and blood perfusion in a contactless way. Also, according to latest pilot studies, a conventional digital camera can generate infrared images with the help of new methods, such as the Euler video magnification technology. Human thermal comfort/discomfort poses can also be detected by video methods without contacting human bodies and be analyzed by the skeleton keypoints model. Here, new sensing technologies were summarized, their cons and pros were discussed, and extended applications for the demand-oriented ventilation were also reviewed as potential development and applications.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

A Scalable Hardware-and-Human-in-the-Loop Grid-interactive Efficient Building Equipment Performance Dataset

This project developed a publicly available, high-fidelity dataset about the interactions among humans, homes, and heat pumps supporting grid interactive efficient buildings to balance demand on the grid with comfort for occupants. Laboratory measurements and simulations of the hardware capture the second-scale electric power dynamics of heat pumps providing grid services like load shifting and load shedding. Field measurements, behavior tracking, and qualitative surveys of people in their homes over multiple years—including experimentally adjusting the heating and cooling system to provide grid services—to capture the reciprocal effect of human behavior on grid services, and grid services on human comfort. Taken together, these data capture the complete Hardware and Human in the loop system for residential heat pumps, reducing large uncertainties in simulation for design, and models for control of heat pumps, and grid-interactive buildings.

24 POWER TRANSMISSION AND DISTRIBUTION↗