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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 469 records · Page 26
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Inappropriate antibiotic access practices at the community level in Eastern Ethiopia

Access to antibiotic medications is critical to achieving the Sustainable Development Goal for good health and well-being. However, non-prescribed and informal sources are implicated as the most common causes of inappropriate antibiotic access practices, resulting in untargeted therapy, which leads to antibiotic resistance. Hence, knowing antibiotic access practices at the community level is essential to target misuse sources. In this study, 2256 household representatives were surveyed between July and September 2023 to examine their antibiotic access practices. Of 1245 household members who received antibiotics, 45.6% did so inappropriately. Non-prescribed antibiotic access was more common among urban residents and individuals not enrolled in health insurance schemes. This means of antibiotic access was also more common among individuals concerned about distance, drug availability, and healthcare convenience at public facilities. In addition, women and rural individuals were more likely to get antibiotics from unauthorized sources. Unrestricted antibiotic dispensing practices in urban areas enabled their non-prescribed access, while unlicensed providers prevailed with this access practice in rural areas. In this regard, personal behaviors and healthcare-related gaps such as the lack of health insurance, inconvenience, and drug unavailability have led community members to seek antibiotics from unofficial and non-prescribed sources. Targeting the identified behavioral and institutional factors can enhance antibiotic access through prescriptions, hence reducing antibiotic resistance.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

Simulating nationwide coupled disease and fear spread in an agent-based model

Human cognitive responses, behavioral responses, and disease dynamics co-evolve over the course of any disease outbreak, and can result in complex feedbacks. We present a dynamic agent-based model that explicitly couples the spread of disease with the spread of fear surrounding the disease, implemented within the EpiCast simulation framework. EpiCast models transmission within a realistic synthetic population, capturing individual-level interactions. In our model, fear propagates through both in-person contact and broadcast media, prompting individuals to adopt protective behaviors that reduce disease spread. In order to better understand these coupled dynamics, we create and compare a range of compartmental models to ensure that introducing additional disease states does not prevent the emergence of multiple waves in these simpler models. Additionally, we compare a range of behavioral scenarios within EpiCast, varying the level and intensity of fear and behavior change. Our results show that the addition of asymptomatic, exposed, and pre-symptomatic disease states can impact both the rate at which an outbreak progresses and its overall trajectory in compartmental models. In EpiCast, the combination of non-local fear spread via broadcasters and strong behavioral responses by fearful individuals generally leads to multiple epidemic waves, an outcome that occurs only within a narrow parameter range when fear spreads purely through local contact. Accounting for the coupled spread of fear and disease is critical for understanding disease dynamics and designing timely, targeted responses to emerging infectious threats.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

Machine learning prediction of incidence of Alzheimer’s disease using large-scale administrative health data

Nationwide population-based cohort provides a new opportunity to build an automated risk prediction model based on individuals’ history of health and healthcare beyond existing risk prediction models. We tested the possibility of machine learning models to predict future incidence of Alzheimer’s disease (AD) using large-scale administrative health data. From the Korean National Health Insurance Service database between 2002 and 2010, we obtained de-identified health data in elders above 65 years (N = 40,736) containing 4,894 unique clinical features including ICD-10 codes, medication codes, laboratory values, history of personal and family illness and socio-demographics. To define incident AD we considered two operational definitions: “definite AD” with diagnostic codes and dementia medication (n = 614) and “probable AD” with only diagnosis (n = 2026). We trained and validated random forest, support vector machine and logistic regression to predict incident AD in 1, 2, 3, and 4 subsequent years. For predicting future incidence of AD in balanced samples (bootstrapping), the machine learning models showed reasonable performance in 1-year prediction with AUC of 0.775 and 0.759, based on “definite AD” and “probable AD” outcomes, respectively; in 2-year, 0.730 and 0.693; in 3-year, 0.677 and 0.644; in 4-year, 0.725 and 0.683. The results were similar when the entire (unbalanced) samples were used. Important clinical features selected in logistic regression included hemoglobin level, age and urine protein level. This study may shed a light on the utility of the data-driven machine learning model based on large-scale administrative health data in AD risk prediction, which may enable better selection of individuals at risk for AD in clinical trials or early detection in clinical settings.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

A Representation Fusion Framework for Decoupling Diagnostic Information in Multimodal Learning

Modern medicine increasingly relies on multimodal data, ranging from clinical notes to imaging and genomics, to guide diagnosis and treatment. However, integrating these heterogeneous data sources in a principled and interpretable manner remains a major challenge. We present MODES (Multi-mOdal Disentangled Embedding Space), a representation fusion framework that explicitly separates shared and modality-specific factors of variation, offering a structured latent space for multimodal information that improves both prediction and interpretability. By leveraging pre-trained unimodal foundation models, MODES mitigates the dependency on extensive paired datasets, crucial in data-scarce clinical settings. We introduce a masking strategy that optimizes representation dimensionality by eliminating low-information dimensions, to achieve compact, information-rich representations. Our framework demonstrates superior performance in predicting diagnoses and phenotypes compared to unimodal and conventional fusion models. MODES also enables robust diagnostic inference in missing data scenarios, offering an opportunity toward interpretable and efficient multimodal diagnostics in personalized healthcare.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Bioinformatics-aided identification, characterization and applications of mushroom linalool synthases

Enzymes empower chemical industries and are the keystone for metabolic engineering. For example, linalool synthases are indispensable for the biosynthesis of linalool, an important fragrance used in 60–80% cosmetic and personal care products. However, plant linalool synthases have low activities while expressed in microbes. Aided by bioinformatics analysis, four linalool/nerolidol synthases (LNSs) from various Agaricomycetes were accurately predicted and validated experimentally. Furthermore, we discovered a linalool synthase (Ap.LS) with exceptionally high levels of selectivity and activity from Agrocybe pediades, ideal for linalool bioproduction. It effectively converted glucose into enantiopure (R)-linalool in Escherichia coli, 44-fold and 287-fold more efficient than its bacterial and plant counterparts, respectively. Phylogenetic analysis indicated the divergent evolution paths for plant, bacterial and fungal linalool synthases. More critically, structural comparison provided catalytic insights into Ap.LS superior specificity and activity, and mutational experiments validated the key residues responsible for the specificity.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Non-invasive ventral cervical magnetoneurography as a proxy of in vivo lipopolysaccharide-induced inflammation

Maintenance of autonomic homeostasis is continuously calibrated by sensory fibers of the vagus nerve and sympathetic chain that convey compound action potentials (CAPs) to the central nervous system. Lipopolysaccharide (LPS) intravenous challenge reliably elicits a robust inflammatory response that can resemble systemic inflammation and acute endotoxemia. Here, we administered LPS intravenously in nine healthy subjects while recording ventral cervical magnetoneurography (vcMNG)-derived CAPs at the rostral Right Nodose Ganglion (RNG) and the caudal Right Carotid Artery (RCA) with optically pumped magnetometers (OPM). We observed vcMNG RNG and RCA neural firing rates that tracked changes in TNF-α levels in the systemic circulation. Further, endotype subgroups based on high and low IL-6 responders segregate RNG CAP frequency (at 30-120 min) and based on high and low IL-10 response discriminate RCA CAP frequency (at 0-30 min). These vcMNG tools may enhance understanding and management of the neuroimmune axis that can guide personalized treatment based on an individual’s distinct endophenotype.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Ultra-low field 13 C MRI of hyperpolarized pyruvate

Medicine is evolving beyond therapy largely predicated on anatomical information and towards incorporating patient-specific molecular biomarkers of disease for more accurate diagnosis and effective treatment. The complementary combination of hyperpolarization by spin-lock induced crossing signal amplification by reversible exchange (SLIC SABRE) and low field magnetic resonance imaging (MRI) can enable accessible metabolic imaging to advance personalized medicine. Hyperpolarized 13 C-enriched pyruvate has demonstrated promise for imaging metabolism in cancer, heart disease and neurodegenerative disorders; however, broader clinical adoption awaits validated clinical indications, and is further constrained by the cost and limited availability of current hyperpolarization technology. Parahydrogen-based polarization techniques, paired with low-cost high-performance MRI at millitesla fields, offer a means of broadening the reach of metabolic imaging. Here we show results demonstrating in situ hyperpolarization of pyruvate at 6.5 mT by SLIC SABRE, followed by immediate readout without field cycling or sample shuttling. We achieve 13 C signal enhancements several million times above thermal equilibrium at 6.5 mT, corresponding to polarization levels of approximately 3%. Leveraging this enhancement, we perform 13 C MRI and acquire NMR spectra with resolution sufficient to distinguish chemical shifts between pyruvate isotopomers. These results show a viable pathway towards accessible metabolic imaging with hyperpolarized 13 C MRI at ultra-low field.

Medical and clinical diagnostics↗
DOE OSTI2023

Federated benchmarking of medical artificial intelligence with MedPerf

Medical artificial intelligence (AI) has tremendous potential to advance healthcare by supporting and contributing to the evidence-based practice of medicine, personalizing patient treatment, reducing costs, and improving both healthcare provider and patient experience. Unlocking this potential requires systematic, quantitative evaluation of the performance of medical AI models on large-scale, heterogeneous data capturing diverse patient populations. Here, to meet this need, we introduce MedPerf, an open platform for benchmarking AI models in the medical domain. MedPerf focuses on enabling federated evaluation of AI models, by securely distributing them to different facilities, such as healthcare organizations. This process of bringing the model to the data empowers each facility to assess and verify the performance of AI models in an efficient and human-supervised process, while prioritizing privacy. We describe the current challenges healthcare and AI communities face, the need for an open platform, the design philosophy of MedPerf, its current implementation status and real-world deployment, our roadmap and, importantly, the use of MedPerf with multiple international institutions within cloud-based technology and on-premises scenarios. Finally, we welcome new contributions by researchers and organizations to further strengthen MedPerf as an open benchmarking platform.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

Exploration of advanced porous organic polymers as a platform for biomimetic catalysis and molecular recognition

Nature has long been a dominant source of inspiration in the area of chemistry, serving as prototypes for the design of materials with proficient performance. In this Feature article, we present our efforts to explore porous organic polymers (POPs) as a platform for the construction of biomimetic materials to enable new technologies to achieve efficient conversions and molecular recognition. For each aspect, we first present the chemical basis of nature, followed by depicting the principles and design strategies involved for functionalizing POPs along with a summary of critical requirements for materials, culminating in a demonstration of unique features of POPs. Our endeavours in using POPs to address the fundamental scientific problems related to biomimetic catalysis and adsorption are then illustrated to show their enormous potential and capabilities for applications ranging from concerted catalysis to radionuclide sequestration. To conclude, we present a personal perspective on the challenges and opportunities in this emerging field.

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

Advancing electrocatalytic nitrogen fixation: insights from molecular systems

Nitrogen fixation has a rich history within the inorganic chemistry community. In recent years attention has (re)focused on developing electrocatalytic systems capable of mediating the nitrogen reduction reaction (N 2 RR). Well-defined molecular catalyst systems have much to offer in this context. This personal perspective summarizes recent progress from our laboratory at Caltech, pulling together lessons learned from a number of studies we have conducted, placing them within the broader context of thermodynamic efficiency and selectivity for the N 2 RR. Here, in particular, proton-coupled electron transfer (PCET) provides an attractive strategy to achieve enhanced efficiency for the multi-electron/proton reduction of N 2 to produce NH 3 (or NH 4 + ), and electrocatalytic PCET (ePCET) via an ePCET mediator affords a promising means of mitigating HER such that the N 2 RR can be achieved in a catalytic fashion.

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

High-purity Polypropylene from Disposable Face Masks via Solvent-Targeted Recovery and Precipitation

The high use of disposable face masks since the start of COVID-19 has globally generated 4.68–6.24 million tons per year of waste from personal protective equipment. Disposable face masks are generally disposed of in landfills. Polypropylene (PP) is the main component in face masks, which is one of the least recycled plastics but has a high market value. In this work, we extract high-quality PP from face masks using solvent-targeted recovery and precipitation (STRAP) with 90 wt% recovery. N,N-Dimethylacetamide removed the color from the recovered PP to produce a clear high-purity PP, as verified by CIELAB color space. The decolored PP shows similar thermochemical properties and color to virgin PP resin. Here, a techno-economic analysis of the process indicates that high-purity PP recovery can be economically viable at a scale of 5000 tons per year or higher.

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

Multifunctional textured graphene-based coatings on elastomeric gloves for chemical protection

Nanotechnology offers a variety of new tools for the design of next-generation personal protective equipment (PPE). One example is the use of two-dimensional materials as coatings that enhance the performance and ergonomics of elastomeric gloves designed to protect users from hazardous chemicals. Desirable features in such coatings may include molecular barrier function, liquid droplet repellency, stretchability for compatibility with the elastomer, breathability, and an ultrathin profile that preserves the user's manual dexterity and tactile sensation. The present work explores the potential of engineered graphene-based films with out-of-plane texturing as a novel platform to meet these multifold requirements. Graphene-based films in different formulations were fabricated from water-borne inks by vacuum filtration and solution casting methods on glove-derived nitrile rubber substrates. The various coatings were then subjected to tests of molecular permeation by model volatile organic compounds, droplet contact angle, breathability, and mechanical stability during stretching and solvent immersion. The films dramatically improve the barrier properties of glove-derived nitrile. The out-of-plane graphene texturing imparts stretchability through microscale folding/unfolding, while also enhancing droplet repellency in some cases through a lotus-like roughening effect. The combined results suggest that engineered textured graphene-based films are a promising platform for creating multifunctional coatings for a next generation of chemically protective gloves and other elastomer-based PPE.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Tunable Conversion of Poly(Acrylic Acid) to Vinyl Methyl Ether Copolymers via Electrochemical Decarboxylation

Recognizing the potential of polymer-to-polymer transformations for plastics valorization, a new electrochemical strategy for the decarboxylative functionalization of polyacrylates has been developed. In contrast to most electrochemical approaches to plastic waste valorization that use small-molecule mediators or only form deconstruction products, this report describes one of the first direct electrochemical redox upconversions of a commodity polymer and involves activation of poly(acrylic acid), a material commonly found in disposable diapers and other personal hygiene products. Via electrochemical decarboxylation, poly(acrylic acid) is converted to methyl ether-containing copolymers. Although methods to prepare the 1:1 regularly alternating poly(vinyl methyl ether-alt-maleic anhydride) are known, routes to generate copolymers with variable levels of vinyl methyl ether and acrylate moieties are unknown, but such polymers may have utility in similar applications, such as dentifrices. Tunable conversions with up to 62 mol% vinyl methyl ether content were achieved via controlled-current electrolyses at >100 mA. This approach enables efficient access to copolymers with broad range of comonomer content from a common waste polymer starting material.

Dauzvardis, Fabian [University of Delaware, Newark↗
DOE OSTI2026

Measured indoor PM2.5, black carbon, and oxidative potential before and after replacing gas with induction cooking in asthmatic households

Cooking is a major source of fine particulate matter (PM) in homes and evidence to date is inconclusive about the impact of cooking fuel on measures of residential PM exposure. The Cooking Energy and Ventilation Impacts on Children's Asthma (CEVICA) study measured cooking frequency, range hood use, indoor air quality (IAQ) and respiratory health indicators of children with asthma living in homes with gas stoves in California's San Joaquin Valley. Intensive measurements occurred over three 2-week intensive periods: at baseline and at the end of two consecutive 3-month study phases. Participants were randomly assigned to have their gas stoves replaced with electric induction at the start of Phase 1 or Phase 2. As part of the IAQ assessment, we collected particulate matter on Teflon filters using ultrasonic personal air samplers (UPAS). The filters were analyzed to quantify time-integrated PM 2.5 mass concentration, black carbon (BC) oxidative potential (OP). OP was measured using the SLF-OH assay, an acellular chemical assay that quantifies production of OH radicals in simulated lung fluid (SLF). Across all comparisons, PM 2.5 mass tended to be higher during gas cooking than induction electric cooking, although only one transition (Baseline to Phase 1) was statistically discernible at p<0.05. BC and mass-normalized OP showed no measurable differences across stove types or across phases. These preliminary findings suggest that stove fuel alone may not be the dominant determinant of indoor PM 2.5 concentrations in this sample, as previous studies have reported that indoor PM 2.5 during cooking can also be influenced by factors such as cooking practices, food and oil type, kitchen ventilation, and outdoor infiltration.

Lin, Zhuoying↗
DOE OSTI2023

Mobility Gaps between Low-Income and Not Low-Income Households: A Case Study in New York State

Understanding the travel challenges faced by low-income residents has always been and continues to be one of the most important transportation equity topics. This study aims to explore the mobility gaps between low-income households (HHs) and not low-income HHs, and how the gaps vary within different socio-demographic population groups in New York State (NYS). The latest National Household Travel Survey data was used as the primary data source for the analysis. The study first employed the K-prototype clustering algorithm to categorize the HHs in NYS based on their socio-demographic attributes. Five population groups were identified based on nine different household (HH) features such as HH size, vehicle ownership, and elderly status of its members. Then, the mobility differences, measured by trip frequency, trip distance, travel time, and person miles traveled, were examined among the five population groups. Results suggest that the individuals in low-income HHs consistently took fewer trips and made shorter trips compared to their not low-income counterparts in NYS. The travel distance gaps were most obvious among white HHs with more vehicles than drivers. In addition, while the population from low-income HHs made shorter trips on average (2.7 mi shorter per trip), they experienced longer travel time than those from not low-income HHs (1.8 min longer per trip). These key findings provide a deeper understanding of the travel behavior disparities between low-income and not low-income households. The findings could also support policymakers and transportation planners in addressing the critical needs of residents in low-income households in NYS and provide inputs for designing a more equitable transportation system.

Liu, Yuandong↗
DOE OSTI2024

Shared Use Travel Behavior for Improving Rural Mobility: Insights from Greene County, Pennsylvania

Rural communities are considered disadvantaged communities as they suffer from a lack of transport options. Thus, rural regionsprovide less accessibility for commuters to reach their destination as opposed to urban regions. However, the issues of transport disadvantageand shared use mobility in rural areas within the United States (US) have not been well investigated. Furthermore, transport disadvantagediffers between communities and regions across the globe; thus, there is a need to study the behavioral choices of rural commuters within theUS context. This study contributes by analyzing the behavioral choices of rural communities within the US through a case study site ofWaynesburg, Pennsylvania, for adopting a shared use shuttle service. K-means clusters showed that trips from the survey data were a goodrepresentation of real trips from Ecolane. Furthermore, random parameter-based binary logit models were calibrated using data collected fromstudents, faculty, and residents in Waynesburg, Greene County, to study the behavioral choices of commuters. The findings for the faculty andstudents group revealed that prior experience with shared services increases the likelihood of using a shared shuttle. An important personalcharacteristic of inconvenience showed a higher propensity toward using existing modes as opposed to a shared shuttle. Such commutersvalue personal vehicles as more convenient as they have childcare responsibilities and varying schedules for work that require them to moveback and forth across locations, thus making a shared shuttle less attractive for them. The socioeconomic factors of age and gender show ahigher propensity for using shared shuttles. Furthermore, the findings from this study could be helpful for agencies in improving rural mobility andconsidering such shared mobility services for rural communities

42 ENGINEERING↗
DOE OSTI2020

A microchip microcontroller-based transducer controller for non-contact scanning probe microscopy with phase-locked loop, amplitude, and Q control

An inexpensive yet versatile transducer controller for non-contact scanning probe microscopy (SPM) based on a PIC32 microcontroller from Microchip Technology, Inc is described. In addition to feedback control using the amplitude or phase of the signal from the non-contact transducer, the controller includes a phase-locked loop for frequency-shift feedback, as well as fixed-amplitude, quality factor (Q) control, and self-excitation modes. Apart from the input amplifiers, output buffers, and the Q-control circuit, all other functions of the controller are instantiated in software on the microchip, enabling rapid changes in operating parameters if needed. The controller communicates with a host personal computer via a simple serial connection. The controller has been tested with a quartz tuning-fork transducer but can be used with any oscillating non-contact transducer.

47 OTHER INSTRUMENTATION↗
DOE OSTI2020

A parametric SPICE model for the simulation of spark gap switches

The use of SPICE-based software for the simulation of pulsed power systems—even large complex systems—has become commonplace in the pulsed power community. This is in contrast to earlier work in the field that relied on specially developed simulation codes such as Sandia’s Screamer or the Navy Research Lab’s Bertha, which natively incorporated models for common pulsed power components such as spark gap switches. Unlike these programs, SPICE programs provide a simple and familiar user interface and wide availability. However, SPICE programs do not include realistic models for key pulsed power circuit devices—including the spark gap switch. While simple switch models do exist in SPICE programs, these can only crudely approximate the behavior of a spark gap. This effort focuses on developing an SPICE circuit model for a gas-filled spark gap switch that is physically realistic while being simple enough to permit simulations to run in reasonable times on typical personal computers. Detailed information is provided for implementation in two common versions of SPICE: LTspice and Orcad PSPICE. Adaptation to other SPICE programs is possible with minimal modification. The model is intended as a design tool that uses physical parameters as inputs to connect it directly to the development of useable pulsed power systems. Data collected from the operation of a high-pressure pulsed-charged switch and a complete 12-stage Marx generator have been used to demonstrate the implementation and accuracy of the model over a wide range of parameters.

Pouncey, J. Cameron (ORCID:0000000153500816)↗