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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 703 records · Page 39
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Guided construction of single cell reference for human and mouse lung

Accurate cell type identification is a key and rate-limiting step in single-cell data analysis. Single-cell references with comprehensive cell types, reproducible and functionally validated cell identities, and common nomenclatures are much needed by the research community for automated cell type annotation, data integration, and data sharing. Here, we develop a computational pipeline utilizing the LungMAP CellCards as a dictionary to consolidate single-cell transcriptomic datasets of 104 human lungs and 17 mouse lung samples to construct LungMAP single-cell reference (CellRef) for both normal human and mouse lungs. CellRefs define 48 human and 40 mouse lung cell types catalogued from diverse anatomic locations and developmental time points. We demonstrate the accuracy and stability of LungMAP CellRefs and their utility for automated cell type annotation of both normal and diseased lungs using multiple independent methods and testing data. We develop user-friendly web interfaces for easy access and maximal utilization of the LungMAP CellRefs.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Quantized resistance revealed at the criticality of the quantum anomalous Hall phase transitions

In multilayered magnetic topological insulator structures, magnetization reversal processes can drive topological phase transitions between quantum anomalous Hall, axion insulator, and normal insulator states. Here we report an examination of the critical behavior of two such transitions: the quantum anomalous Hall to normal insulator (QAH-NI), and quantum anomalous Hall to axion insulator (QAH-AXI) transitions. By introducing a new analysis protocol wherein temperature dependent variations in the magnetic coercivity are accounted for, the critical behavior of the QAH-NI and QAH-AXI transitions are evaluated over a wide range of temperature and magnetic field. Despite the uniqueness of these different transitions, quantized longitudinal resistance and Hall conductance are observed at criticality in both cases. Furthermore, critical exponents were extracted for QAH-AXI transitions occurring at magnetization reversals of two different magnetic layers. The observation of consistent critical exponents and resistances in each case, independent of the magnetic layer details, demonstrates critical behaviors in quantum anomalous Hall transitions to be of electronic rather than magnetic origin. Our finding offers a new avenue for studies of phase transition and criticality in QAH insulators.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2026

Causes and consequences of experimental variation in Nicotiana benthamiana transient expression

Infiltration of Agrobacterium tumefaciens into Nicotiana benthamiana has become a foundational technique in plant biology, enabling efficient delivery of transgenes in planta with technical ease, robust signal, and relatively high throughput. Despite transient expression’s prevalence in disciplines such as synthetic biology, little work has been done to describe and address the variability inherent in this system, a concern for experiments that rely on highly quantitative readouts. In a comprehensive analysis of N. benthamiana agroinfiltration experiments, we model sources of variability that affect transient expression. Our findings emphasize the need to validate normalization methods under the specific conditions of each study, as distinct normalization schemes do not always reduce variation either within or between experiments. Using a dataset of 1915 plants collected over three years, we develop a model of variation in N. benthamiana transient expression, using power analysis to determine the number of individual plants required for a given effect size. Drawing on our longitudinal data, these findings inform practical guidelines for minimizing variability through strategic experimental design and power analysis, providing a foundation for more robust and reproducible use of N. benthamiana in quantitative plant biology and synthetic biology applications.

Tang, Sophia N. [Joint BioEnergy Institute (JBEI),↗
DOE OSTI2021

A study of real-world micrograph data quality and machine learning model robustness

Abstract Machine-learning (ML) techniques hold the potential of enabling efficient quantitative micrograph analysis, but the robustness of ML models with respect to real-world micrograph quality variations has not been carefully evaluated. We collected thousands of scanning electron microscopy (SEM) micrographs for molecular solid materials, in which image pixel intensities vary due to both the microstructure content and microscope instrument conditions. We then built ML models to predict the ultimate compressive strength (UCS) of consolidated molecular solids, by encoding micrographs with different image feature descriptors and training a random forest regressor, and by training an end-to-end deep-learning (DL) model. Results show that instrument-induced pixel intensity signals can affect ML model predictions in a consistently negative way. As a remedy, we explored intensity normalization techniques. It is seen that intensity normalization helps to improve micrograph data quality and ML model robustness, but microscope-induced intensity variations can be difficult to eliminate.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Machine learning model to project the impact of COVID-19 on U.S. motor gasoline demand

Owing to the global lockdowns that resulted from the COVID-19 pandemic, fuel demand plummeted and the price of oil futures went negative in April 2020. Robust fuel demand projections are crucial to economic and energy planning and policy discussions. Here we incorporate pandemic projections and people's resulting travel and trip activities and fuel usage in a machine-learning-based model to project the US medium-term gasoline demand and study the impact of government intervention. We found that under the reference infection scenario, the US gasoline demand grows slowly after a quick rebound in May, and is unlikely to fully recover prior to October 2020. Under the reference and pessimistic scenario, continual lockdown (no reopening) could worsen the motor gasoline demand temporarily, but it helps the demand recover to a normal level quicker. Under the optimistic infection scenario, gasoline demand will recover close to the non-pandemic level by October 2020. The COVID 19 pandemic and consequent lockdown has had a substantial impact on mobility and therefore fuel demand and it is not clear when demand will recover. Ou et al. use a machine learning model that integrates health recovery scenarios to project the near-term future of gasoline demand.

COVID-19 pandemic↗
DOE OSTI2020

Pre-formed Cooper pairs in copper oxides and LaAlO 3 —SrTiO 3 heterostructures

The Bardeen–Cooper–Schrieffer theory of superconductivity and the Landau–Fermi liquid theory form the basis of our current understanding of conventional superconductors and their parent non-superconducting phases. However, some exotic superconductors do not conform to this physical picture but instead feature an unusual ‘normal’ state that is not a Fermi liquid. One explanation of this unusual behaviour is that pre-formed pairs of electrons are established above the superconducting temperature T c . Here, we highlight recent experiments that show the likely existence of these pre-formed pairs in two rather different materials—a high-temperature cuprate superconductor and strontium titanate. Moreover, in both materials the normal state from which superconductivity emerges has other shared properties, including a pseudogap and electronic nematicity—rotational symmetry breaking in the electron fluid that is not expected in Fermi liquid theory nor more generally from the crystal lattice symmetry. These experimental findings should provoke more interaction between the communities working on these materials and new insights into the underlying mechanism of the creation of pre-formed pairs.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2023

Linear-in-temperature resistivity for optimally superconducting (Nd,Sr)NiO 2

The occurrence of superconductivity in proximity to various strongly correlated phases of matter has drawn extensive focus on their normal state properties, to develop an understanding of the state from which superconductivity emerges. The recent finding of superconductivity in layered nickelates raises similar interests. However, transport measurements of doped infinite-layer nickelate thin films have been hampered by materials limitations of these metastable compounds: in particular, a high density of extended defects. Here, by moving to a substrate (LaAlO 3 ) 0.3 (Sr 2 TaAlO 6 ) 0.7 that better stabilizes the growth and reduction conditions, we can synthesize the doping series of Nd 1– x Sr x NiO 2 essentially free from extended defects. In their absence, the normal state resistivity shows a low-temperature upturn in the underdoped regime, linear behaviour near optimal doping and quadratic temperature dependence for overdoping. This is phenomenologically similar to the copper oxides despite key distinctions—namely, the absence of an insulating parent compound, multiband electronic structure and a Mott–Hubbard orbital alignment rather than the charge-transfer insulator of the copper oxides. In this study, we further observe an enhancement of superconductivity, both in terms of transition temperature and range of doping. These results indicate a convergence in the electronic properties of both superconducting families as the scale of disorder in the nickelates is reduced.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2025

In vivo mapping of mutagenesis sensitivity of human enhancers

Distant-acting enhancers are central to human development1. However, our limited understanding of their functional sequence features prevents the interpretation of enhancer mutations in disease2. Here we determined the functional sensitivity to mutagenesis of human developmental enhancers in vivo. Focusing on seven enhancers that are active in the developing brain, heart, limb and face, we created over 1,700 transgenic mice for over 260 mutagenized enhancer alleles. Systematic mutation of 12-base-pair blocks collectively altered each sequence feature in each enhancer at least once. We show that 69% of all blocks are required for normal in vivo activity, with mutations more commonly resulting in loss (60%) than in gain (9%) of function. Using predictive modelling, we annotated critical nucleotides at the base-pair resolution. The vast majority of motifs predicted by these machine learning models (88%) coincided with changes in in vivo function, and the models showed considerable sensitivity, identifying 59% of all functional blocks. Taken together, our results reveal that human enhancers contain a high density of sequence features that are required for their normal in vivo function and provide a rich resource for further exploration of human enhancer logic.

Kosicki, Michael↗
DOE OSTI2022

Development and validation of a mathematical model of heart rate response to fluid perturbation

Physiological closed-loop controlled (PCLC) medical devices monitor and automatically adjust the patient’s condition by using physiological variables as feedback, ideally with minimal human intervention to achieve the target levels set by a clinician. PCLC devices present a challenge when it comes to evaluating their performance, where conducting large clinical trials can be expensive. Virtual physiological patients simulated by validated mathematical models can be utilized to obtain preclinical evidence of safety and assess the performance of the PCLC medical device during normal and worst-case conditions that are unlikely to happen in a limited clinical trial. A physiological variable that plays a major role during fluid resuscitation is heart rate (HR). For in silico assessment of PCLC medical devices regarding fluid perturbation, there is currently no mathematical model of HR validated in terms of its predictive capability performance. This paper develops and validates a mathematical model of HR response using data collected from sheep subjects undergoing hemorrhage and fluid infusion. The model proved to be accurate in estimating the HR response to fluid perturbation, where averaged between 21 calibration datasets, the fitting performance showed a normalized root mean square error (NRMSE) of 7.41 ± 2.8%. The model was also evaluated in terms of model predictive capability performance via a leave-one-out procedure (21 subjects) and an independent validation dataset (6 subjects). Two different virtual cohort generation tools were used in each validation analysis. The generated envelope of virtual subjects robustly met the defined acceptance criteria, in which 95% of the testing datasets presented simulated HR patterns that were within a deviation of 50% from the observed data. In addition, out of 16000 and 18522 simulated subjects for the leave one-out and independent datasets, the model was able to generate at least one virtual subject that was close to the real subject within an error margin of 9.56 ± 3.15% and 11.1 ± 1.22% NRMSE, respectively. In conclusion, the model can generate valid virtual HR physiological responses to fluid perturbation and be incorporated into future non-clinical simulated testing setups for assessing PCLC devices intended for fluid resuscitation.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Short-lead seasonal precipitation forecast in northeastern Brazil using an ensemble of artificial neural networks

This study assesses the deterministic and probabilistic forecasting skill of a 1-month-lead ensemble of Artificial Neural Networks (EANN) based on low-frequency climate oscillation indices. The predictand is the February-April (FMA) rainfall in the Brazilian state of Ceará, which is a prominent subject in climate forecasting studies due to its high seasonal predictability. Additionally, the study proposes combining the EANN with dynamical models into a hybrid multi-model ensemble (MME). The forecast verification is carried out through a leave-one-out cross-validation based on 40 years of data. The EANN forecasting skill is compared with traditional statistical models and the dynamical models that compose Ceará’s operational seasonal forecasting system. A spatial comparison showed that the EANN was among the models with the smallest Root Mean Squared Error (RMSE) and Ranked Probability Score (RPS) in most regions. Moreover, the analysis of the area-aggregated reliability showed that the EANN is better calibrated than the individual dynamical models and has better resolution than Multinomial Logistic Regression for above-normal (AN) and below-normal (BN) categories. It is also shown that combining the EANN and dynamical models into a hybrid MME reduces the overconfidence of the extreme categories observed in a dynamically-based MME, improving the reliability of the forecasting system.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Analysis of multipacting threshold sensitivity to the random distributions of the secondary electron yield parameters

The way multipacting develops, depends strongly on the secondary emission property of the surface material. The knowledge of secondary electron yield is crucial for accurate prediction of the multipacting threshold. Variations in secondary electron yield parameters from experimental measurements create uncertainty, stemming from handling and surface preparation, and these uncertainties significantly affect multipacting threshold predictions. Despite their significance, the previous studies on the multipacting phenomenon did not adequately address the effect of an assumed random distribution of the secondary emission parameters on the multipacting threshold. Therefore, this paper aims to provide a comprehensive statistical study on how the different random distributions of the secondary emission parameters and, as a result, the uncertainty in the secondary electron yield affect multipacting thresholds. We focus on three commonly used distributions, namely uniform, normal, and truncated normal distributions, to define the uncertainty of random inputs. We use the chaos polynomial expansion method to determine how much each of the random parameters contributes to the multipacting threshold uncertainty. Additionally, we calculate Sobol sensitivity indices to evaluate the impact of the individual parameters or groups of parameters on the model outputs and study how different random distributions of these parameters affected the Sobol index results.

physics↗
DOE OSTI2026

Explainable machine learning for incipient anomaly detection in compact molten salt heat exchanger with overlapping feature distributions

High-temperature molten salt-cooled reactors (MSCRs) are a promising next-generation nuclear technology option, offering efficient power conversion and inherent safety features. However, the reliability of these systems depends on the robust operation of heat exchangers (HXs), which are susceptible to failure due to temperature gradients and channel plugging caused by fluid freezing. Conventional monitoring methods, relying on inlet and outlet measurements, lack the spatial resolution needed to detect early-stage faults. We propose a novel design of a compact salt-to-salt matrix-type HX design consisting of interleaved arrays of parallel tubes, with integrated synthetic fiber optic distributed temperature sensing (DTS) to enable localized detection of incipient faults. To evaluate performance of this design, we generate high-fidelity synthetic data using heat transfer computational modeling to simulate channel plugging, and introduce sensor noise for realistic modeling of measurements. The dataset comprises of 97% normal operation and 3% anomaly cases, with each anomaly class representing 1% of the data. These early anomalies result in overlapping temperature profiles between normal and faulty channels, producing a non-separable dataset that challenges traditional classification techniques. We benchmark eight supervised machine learning (ML) models and demonstrate that XGBoost achieves the highest performance. To improve transparency, we develop an explainability framework combining Shapley values and partially ordered sets (POSETs) to quantify and structurally analyze feature importance. This approach identifies both dominant predictors and ambiguous feature relationships, enhancing trust and interpretability. Our results highlight the potential of combining DTS and explainable ML with intelligent feature selection to improve predictive maintenance and ensure operational resilience in advanced nuclear systems.

Prantikos, Konstantinos [Argonne National Laborato↗
DOE OSTI2021

Evidence for accelerated aging in mammary epithelia of women carrying germline BRCA1 or BRCA2 mutations

During aging in the human mammary gland, luminal epithelial cells lose lineage fidelity by expressing markers normally expressed in myoepithelial cells. We hypothesize that loss of lineage fidelity is a general manifestation of epithelia that are susceptible to cancer initiation. In the present study, we show that histologically normal breast tissue from younger women who are susceptible to breast cancer, as a result of harboring a germline mutation in BRCA1, BRCA2 or PALB2 genes, exhibits hallmarks of accelerated aging. These include proportionately increased luminal epithelial cells that acquired myoepithelial markers, decreased proportions of myoepithelial cells and a basal differentiation bias or failure of differentiation of cKit + progenitors. High-risk luminal and myoepithelial cells are transcriptionally enriched for genes of the opposite lineage, inflammatory- and cancer-related pathways. We have identified breast-aging hallmarks that reflect a convergent biology of cancer susceptibility, regardless of the specific underlying genetic or age-dependent risk or the associated breast cancer subtype.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

Subtoxic dose of lithium cobalt oxide nanosheets impacts critical molecular pathways in trout gill epithelial cells

Increasing use of nanoscale lithium cobalt oxide (LCO) particles in nanotechnologies, including catalysis and energy storage, raises concerns over their release into the environment and subsequent biological impact. Here we study the impact of LCO nanosheets on trout gill epithelial cells – model cells for environmental exposures – by global transcriptomics using RNA-Seq. We identify molecular processes impacted by subtoxic and toxic nanoparticle (NP) doses as well as dissolved Li+ and Co2+ ions. We found that the ions, at concentrations released from the toxic NP dose, did not impact cell viability and downregulated the expression of few genes following 24 h exposure, which recovered to normal levels at 48 h. In contrast, the toxic NP dose upregulated the expression of over 1000 genes at each time point, indicating the intact NPs are responsible for perturbing gene expression and toxicity. Importantly, the subtoxic NP dose, despite having no impact on cell viability, upregulated the expression of over 500 genes at 24 h, and 150 genes at 48 h. Clustering analysis showed distinct gene expression profiles induced by the toxic and subtoxic NP doses, and functional enrichment identified pathways with distinct patterns of regulations. The impacted pathways fell into four main functional categories: metabolic and energy related processes, oxygen and hypoxia related processes, membrane binding and internalization processes, and developmental processes. Together, our observations indicate that LCO NP toxicity originates from the intact NP, not the dissolved ions, and even subtoxic NP dose impacts multiple pathways critical to the normal function of the cell.

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

Classroom aerosol dispersion modeling: experimental assessment of a low-cost flow simulation tool

The purpose of this study was to assess the utility of a low-cost flow simulation tool for an indoor air modeling application by comparing its outputs with the results of a physical experiment, as well as those from a more advanced computational fluid dynamics (CFD) software package. In this study, five aerosol dispersion tests were performed in two different classrooms by releasing a CO 2 tracer gas from six student locations. Resultant steady-state concentrations were monitored at 13 locations around the periphery of the room. Subsequently, the experiments were modeled using both a low-cost tool (SolidWorks Flow Simulation) and a more sophisticated tool (STAR-CCM+). Models were evaluated based on their ability to predict the experimentally measured concentrations at the 13 monitoring locations by calculating four performance parameters commonly used in the evaluation of dispersion models: fractional mean bias (FB), normalized mean-square error (NMSE), fraction of predicted value within a factor of two (FAC2), and normalized absolute difference (NAD). The more sophisticated model performed better in 15 of the 20 possible cases (five tests at four parameters each), with parameters meeting acceptance criteria in 19 of 20 cases. However, the lower-cost tool was only slightly worse, with parameters meeting acceptance criteria in 18 of 20 cases, and it performed better than the other tool in 3 of 20 cases. Because it provides useful results at a fraction of the monetary and training cost and is already widely accessible to many institutions, such a tool may be worthwhile for many indoor aerosol dispersion applications, especially for students or researchers just beginning CFD modeling.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

The influence of protein electrostatics on potential inversion in flavoproteins

Biology uses relatively few electron-transfer cofactors, tuning their potentials, electronic couplings, and reorganization energies to carry out the required chemistry. It is remarkable that the potential ordering of two-electron transfer active flavins can be normal (first oxidation at low potential and second oxidation at high potential) or inverted, and the gap between the potentials can be as large as one volt. Analysis based on structural bioinformatics and electrostatics indicates that the ordering of the flavin redox potential is influenced by protein electrostatics. In all 36 flavoproteins examined, the introduction of a negative charge near the flavin in silico increases the extent of potential inversion (by lowering the electrochemical potential of the second electron-transfer step); the introduction of a positive charge near the flavin favors normally ordered potentials. We also find that the addition of positive charges increases the electrochemical potential for the naturally occurring one-electron transition in flavodoxins (between deprotonated hydroquinone and neutral semiquinone) and also increases the second one-electron transition in bifurcating flavins (between anionic semiquinone and fully oxidized flavin). Finally, we find that proximity of a proton acceptor, notably conserved arginine, supports proton-coupled electron transfer because it may act as a proton acceptor, promoting potential inversion. This key arginine residue may enable two-electron transfer chemistry by promoting the proton-coupled electron transfer process over the pure electron transfer process, suggesting how a protein's flavin environment may influence one- or two-electron chemistry in flavoproteins.

Singh, Niven [Duke Univ., Durham, NC (United State↗
DOE OSTI2022

Quasars with large proper motions: A selection from the LQAC-5 catalogue combined with Gaia EDR3: Focusing on astrometric and photometric properties

Thanks to the cross-identification between the Gaia EDR3 (E-Data Release 3) catalogue and the quasars’ compiled catalogue LQAC-5 (Large Quasar Astrometric Catalogue-5), accurate astrometric parameters as well as photometric measurements were available for a set of 416 113 quasars. After analysing the astrometric and photometric properties of these quasars, we concentrate our study on objects characterized by significant proper motion, which contradicts the postulate that they are fixed in the celestial sphere. We derived statistical properties of the positions, parallaxes, and proper motions of the quasars cross-matched with Gaia EDR3. We carried out a specific investigation of quasars with a proper motion larger than 10 mas yr -1 , and used colour-colour diagrams in mid-infrared to discriminate these quasars from stellar contamination. We discuss, in detail, a few cases where the abnormal proper motion could be caused by misidentifications. We note that 43% of LQAC-5 quasars were not detected by Gaia EDR3, mainly because of the cut-off magnitude at G = 21. The comparison between r(G) (Gaia) and r (SDSS) shows a normal distribution. The normalized distributions of parallaxes and proper motions are identical to that of the Gaia CRF2, as well as the distribution of the G BP -G RP colour index. We have identified 937 LQAC-5 objects (0.27% of the sample) with Gaia EDR3 proper motion which is significant at the 5σ level. We have clearly shown that below z = 0.35, the integrated magnitude G(r) obtained from SDSS spectra is smaller than G (Gaia), leading to the statement that the quasars are generally resolved under this threshold, which should lead to a bad determination of proper motion. Moreover, to discriminate quasars from stellar contamination, we have plotted them in a mid-infrared colour–colour diagram. Finally, we have made a final selection of 41 objects with μ > 10 mas yr -1 , satisfying drastic constraints, which makes them potential candidates for considering their proper motion as real. In this paper we carried out a complete analysis of the quasars of the LQAC-5 which were cross-identified with Gaia EDR3 ; this serves as an alternative and complementary study with respect to Gaia CRF2, involving a different population of quasars. We selected a set of 41 quasars with a proper motion exceeding 10 mas yr -1 , which can be considered as very high for objects which are a priori fixed in the celestial sphere.

79 ASTRONOMY AND ASTROPHYSICS↗