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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 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
DOE OSTI2012

Utility-Scale Solar Power Converter: Agile Direct Grid Connect Medium Voltage 4.7-13.8 kV Power Converter for PV Applications Utilizing Wide Band Gap Devices

Solar ADEPT Project: Satcon is developing a compact, lightweight power conversion device that is capable of taking utility-scale solar power and outputting it directly into the electric utility grid at distribution voltage levels—eliminating the need for large transformers. Transformers “step up” the voltage of the power that is generated by a solar power system so it can be efficiently transported through transmission lines and eventually “stepped down” to usable voltages before it enters homes and businesses. Power companies step up the voltage because less electricity is lost along transmission lines when the voltage is high and current is low. Satcon’s new power conversion devices will eliminate these heavy transformers and connect a utility-scale solar power system directly to the grid. Satcon’s modular devices are designed to ensure reliability—if one device fails it can be bypassed and the system can continue to run.

ARPA-E↗
DOE OSTI2021

Instruction Roofline: An insightful visual performance model for GPUs

The Roofline performance model provides an intuitive approach to identify performance bottlenecks and guide performance optimization. However, the classic FLOP-centric approach is inappropriate for the emerging applications that perform more integer operations than floating point operations. In this article, we reintroduce our Instruction Roofline Model on NVIDIA GPUs and expand our evaluation of it. The Instruction Roofline incorporates instructions and memory transactions across all memory hierarchies together, and provides more performance insights than the FLOP-oriented Roofline Model, that is, instruction throughput, stride memory access patterns, bank conflicts, and thread predication. We use our Instruction Roofline methodology to analyze eight proxy applications: HPGMG from AMReX, Matrix Transpose benchmarks, ADEPT from MetaHipMer's sequence alignment phase, EXTENSION from MetaHipMer's local assembly phase, CUSP, cuSPARSE, cudaTensorCoreGemm, and cuBLAS. We demonstrate the ability of our methodology to understand various aspects of performance and performance bottlenecks on NVIDIA GPUs and motivate code optimizations.

Ding, N↗
DOE OSTI2012

Patterns of root growth acclimation: constant processes, changing boundaries

Plasticity, the hallmark of plant morphogenesis, extends to kinetics. To enhance acclimation, growing plant organs adeptly adjust their growth rate, up or down. In roots, rates of division and elemental expansion as well as the length of division and elongation zones are readily characterized because of their linear organization, radial symmetry, and indeterminate growth, and can be measured accurately with kinematic methods. Here, for roots, I describe key concepts from kinematics and review patterns of growth and division during acclimation. The growth rate of a root reflects the integral of elemental expansion activity over the span of the growth zone; therefore, an acclimating plant can change the rate of root growth by changing either or both the span of the growth zone or the rate of elemental expansion. The analogous dichotomy exists for cell division where the rate at which cells are produced reflects the integral of cell division rate over the span of the division zone. Surprisingly, expansion responses nearly always involve changes in the length of the growth zone. Similarly, although based on fewer data, changes in cell division rate are rare, whereas changes in meristem length are common. Furthermore, these patterns imply that setting the boundaries for meristem and elongation zone is the key regulatory act for root growth rate acclimation.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Out-of-Distribution Detection and Radiological Data Monitoring Using Statistical Process Control

Abstract Machine learning (ML) models often fail with data that deviates from their training distribution. This is a significant concern for ML-enabled devices as data drift may lead to unexpected performance. This work introduces a new framework for out of distribution (OOD) detection and data drift monitoring that combines ML and geometric methods with statistical process control (SPC). We investigated different design choices, including methods for extracting feature representations and drift quantification for OOD detection in individual images and as an approach for input data monitoring. We evaluated the framework for both identifying OOD images and demonstrating the ability to detect shifts in data streams over time. We demonstrated a proof-of-concept via the following tasks: 1) differentiating axial vs. non-axial CT images, 2) differentiating CXR vs. other radiographic imaging modalities, and 3) differentiating adult CXR vs. pediatric CXR. For the identification of individual OOD images, our framework achieved high sensitivity in detecting OOD inputs: 0.980 in CT, 0.984 in CXR, and 0.854 in pediatric CXR. Our framework is also adept at monitoring data streams and identifying the time a drift occurred. In our simulations tracking drift over time, it effectively detected a shift from CXR to non-CXR instantly, a transition from axial to non-axial CT within few days, and a drift from adult to pediatric CXRs within a day—all while maintaining a low false positive rate. Through additional experiments, we demonstrate the framework is modality-agnostic and independent from the underlying model structure, making it highly customizable for specific applications and broadly applicable across different imaging modalities and deployed ML models.

Zamzmi, Ghada↗
DOE OSTI2014

A Pair Production Telescope for Medium-Energy Gamma-Ray Polarimetry

We describe the science motivation and development of a pair production telescope for medium-13 energy gamma-ray polarimetry. Our instrument concept, the Advanced Energetic Pair Telescope 14 (AdEPT), takes advantage of the Three-Dimensional Track Imager, a low-density gaseous time 15 projection chamber, to achieve angular resolution within a factor of two of the pair production 16 kinematics limit (~0.6° at 70 MeV), continuum sensitivity comparable with the Fermi-LAT front 17 detector (<3×10-6 MeV cm-2 s-1 at 70 MeV), and minimum detectable polarization less than 10% 18 for a 10 millicrab source in 106 seconds.

Hunter, Stanley D.↗
DOE OSTI2018

Engineering of Escherichia coli β-lactamase TEM-1 variants showing higher activity under acidic conditions than at the neutral pH

Highlights: • Genetic screening is useful for obtaining β-lactamases with altered pH dependency. • A few mutations each suppress the activity of a β-lactamase at pH 7.0. • The engineered β-lactamase variants are more active under the acidic conditions. • The relevant amino acid substitutions are located far from the catalytic site. Escherichia coli β-lactamase TEM-1 is potentially useful in the antibody-directed enzyme/prodrug therapy (ADEPT), converting nontoxic prodrugs to toxic agents. The produced toxin would kill cancer cells, when the enzyme is attached to a tumor-antigen-specific antibody. However, the off-site reaction possibly occurring in the blood or normal tissues raises safety concern. In the present study, we engineered TEM-1 variants preferentially active at pH 5.8–6.2, near the pH of the acidic microenvironment of tumor. A library of randomly mutagenized variants was screened for the ability to confer an antibiotic resistance on E. coli cells in acidic growth media and not in neutral media, to isolate a variant with a Thr-to-Ile substitution at position 160. An extensive mutagenesis study was then conducted in the proximity of this position, to show that a Leu162Glu mutation also causes the acid preference. Kinetic analyses indicated that the overall activity of the wild-type TEM-1 hardly changes over a pH range from 5.8 to 7.0, whereas TEM-1(T160I) is 1.5-times as active at pH 6.2 than pH 7.0, and TEM-1(T160I) is 3.1-fold as active at pH 5.8 than pH 7.0. A further mutagenesis study suggested that a change in the overall structure of the enzyme underlies the pH dependency of the variants.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

Uncertainty quantification of graph convolution neural network models of evolving processes

The application of neural network models to scientific machine learning tasks has proliferated in recent years. In particular, neural networks have proved to be adept at modeling processes with spatial–temporal complexity. Nevertheless, these highly parameterized models have garnered skepticism in their ability to produce outputs with quantified error bounds over the regimes of interest. Hence there is a need to find uncertainty quantification methods that are suitable for neural networks. In this work we present comparisons of the parametric uncertainty quantification of neural networks modeling complex spatial–temporal processes with Hamiltonian Monte Carlo and Stein variational gradient descent and its projected variant. Specifically we apply these methods to graph convolutional neural network models of evolving systems modeled with recurrent neural network and neural ordinary differential equations architectures. We show that Stein variational inference is a viable alternative to Monte Carlo methods with some clear advantages for complex neural network models. For our exemplars, Stein variational interference gave similar pushed forward uncertainty profiles through time compared to Hamiltonian Monte Carlo, albeit with generally more generous variance. As a result, projected Stein variational gradient descent also produced similar uncertainty profiles to the non-projected counterpart, but large reductions in the active weight space were confounded by the stability of the neural network predictions and the convoluted likelihood landscape.

36 MATERIALS SCIENCE↗
DOE OSTI2023

A mesoscale crystal plasticity model to predict room-temperature deformation and martensitic transformation of high-strength Quenching and Partitioning (Q&P) Steels and validation with synchrotron X-ray diffraction

Renowned for the superior mechanical properties and adeptness at cold-forming, Quenching and Partitioning (QP) steels have gained prominence as a promising candidate material in fabricating safety-critical components in various industries. The pertinent research on QP steels focus on the martensitic transformation of the Retained Austenite (RA) phase during cold-forming, a crucial mechanism that substantially influences the overall strength and ductility of QP steels. The austenite stability and transformation rate heavily rely on the local strain path and the initial microstructure, which is challenging for analytical prediction. In this paper, a mesoscale model is developed to capture the deformation and transformation kinetics of QP steels inside the microstructure. The model integrates the detailed explicit microstructure, acquired from characterization experiments, into a high-resolution finite element (FE) mesh. It distinctly model the deformation and interaction between the various phases and the effect on the transformation of RA. The model is validated with high energy X-ray diffraction (HEXRD) data, and shows excellent capability in predicting the asymmetric stress-strain behavior under uniaxial tension and compression, as well as the martensitic transformation rate. The model is used to investigate the strain and load partitioning effect of surrounding matrix to the transformation of RA, offering insights into the complex behavior of QP980 and facilitates further material development.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Fission yeast Duf89 and Duf8901 are cobalt/nickel-dependent phosphatase–pyrophosphatases that act via a covalent aspartyl–phosphate intermediate

Domain of Unknown Function 89 (DUF89) proteins are metal-dependent phosphohydrolases. Exemplary DUF89 enzymes differ in their metal and phosphosubstrate preferences. Here, we interrogated the activities and structures of two DUF89 paralogs from fission yeast—Duf89 and Duf8901. We find that Duf89 and Duf8901 are cobalt/nickel-dependent phosphohydrolases adept at hydrolyzing p-nitrophenylphosphate and PP i . Crystal structures of metal-free Duf89 and Co 2+ -bound Duf8901 disclosed two enzyme conformations that differed with respect to the position of a three-helix module, which is either oriented away from the active site in Duf89 or forms a lid over the active site in Duf8901. Lid closure results in a 16 Å movement of Duf8901 Asp195, vis-à-vis Asp199 in Duf89, that brings Asp195 into contact with an octahedrally coordinated cobalt. Reaction of Duf8901 with BeCl 2 and NaF in the presence of divalent cations Co 2+ , Ni 2+ , or Zn 2+ generated covalent Duf8901-(Asp248)–beryllium trifluoride (BeF 3 )•Co 2+ , Duf8901-(Asp248)–BeF 3 •Ni 2+ , or Duf8901-(Asp248)–BeF 3 •Zn 2+ adducts, the structures of which suggest a two-step catalytic mechanism via formation and hydrolysis of an enzyme-(aspartyl)–phosphate intermediate. Alanine mutations of Duf8901 Asp248, Asn249, Lys401, Asp286, and Asp195 that interact with BeF 3 •Co 2+ squelched p-nitrophenylphosphatase activity. A 1.8 Å structure of a Duf8901-(Asp248)–AlF 4 –OH 2 •Co 2+ transition-state mimetic suggests an associative mechanism in which Asp195 and Asp363 orient and activate the water nucleophile. Whereas deletion of the duf89 gene elicited a phenotype in which expression of phosphate homeostasis gene pho1 was derepressed, deleting duf8901 did not, thereby hinting that the DUF89 paralogs have distinct functional repertoires in vivo.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Object detection with deep learning for rare event search in the GADGET II TPC

In the pursuit of identifying rare two-particle events within the GADGET II Time Projection Chamber (TPC), this paper presents a comprehensive approach for leveraging Convolutional Neural Networks (CNNs) and various data processing methods. To address the inherent complexities of 3D TPC track reconstructions, the data is expressed in 2D projections and 1D quantities. This approach capitalizes on the diverse data modalities of the TPC, allowing for the efficient representation of the distinct features of the 3D events, with no loss in topology uniqueness. Additionally, it leverages the computational efficiency of 2D CNNs and benefits from the extensive availability of pre-trained models. Given the scarcity of real training data for the rare events of interest, simulated events are used to train the models to detect real events. To account for potential distribution shifts when predominantly depending on simulations, significant perturbations are embedded within the simulations. This produces a broad parameter space that works to account for potential physics parameter and detector response variations and uncertainties. These parameter-varied simulations are used to train sensitive 2D CNN object detectors. When combined with 1D histogram peak detection algorithms, this multi-modal detection framework is highly adept at identifying rare, two-particle events in data taken during experiment 21072 at the Facility for Rare Isotope Beams (FRIB), demonstrating a 100% recall for events of interest. Here, we present the methods and outcomes of our investigation and discuss the potential future applications of these techniques.

Convolutional neural network↗
DOE OSTI2015

Metallothionein blocks oxidative DNA damage induced by acute inorganic arsenic exposure

We studied how protein metallothionein (MT) impacts arsenic-induced oxidative DNA damage (ODD) using cells that poorly express MT (MT-I/II double knockout embryonic cells; called MT-null cells) and wild-type (WT) MT competent cells. Arsenic (as NaAsO{sub 2}) was less cytolethal over 24 h in WT cells (LC{sub 50} = 11.0 ± 1.3 μM; mean ± SEM) than in MT-null cells (LC{sub 50} = 5.6 ± 1.2 μM). ODD was measured by the immuno-spin trapping method. Arsenic (1 or 5 μM; 24 h) induced much less ODD in WT cells (121% and 141% of control, respectively) than in MT-null cells (202% and 260%). In WT cells arsenic caused concentration-dependent increases in MT expression (transcript and protein), and in the metal-responsive transcription factor-1 (MTF-1), which is required to induce the MT gene. In contrast, basal MT levels were not detectable in MT-null cells and unaltered by arsenic exposure. Transfection of MT-I gene into the MT-null cells markedly reduced arsenic-induced ODD levels. The transport genes, Abcc1 and Abcc2 were increased by arsenic in WT cells but either showed no or very limited increases in MT-null cells. Arsenic caused increases in oxidant stress defense genes HO-1 and GSTα2 in both WT and MT-null cells, but to much higher levels in WT cells. WT cells appear more adept at activating metal transport systems and oxidant response genes, although the role of MT in these responses is unclear. Overall, MT protects against arsenic-induced ODD in MT competent cells by potential sequestration of scavenging oxidant radicals and/or arsenic. - Highlights: • Metallothionein blocks arsenic toxicity. • Metallothionein reduces arsenic-induced DNA damage. • Metallothionein may bind arsenic or radicals produced by arsenic.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2013

Microbial communities and greenhouse gas emissions associated with the biodegradation of specified risk material in compost

Highlights: ► Addition of feathers altered bacterial and fungal communities in compost. ► Microbial communities degrading SRM and compost matrix were distinct. ► Addition of feathers may enrich for microbial communities that degrade SRM. ► Inclusion of feather in compost increased both CH{sub 4} and N{sub 2}O emissions from compost. ► Density of methanogens and methanotrophs were weakly associated with CH{sub 4} emissions. - Abstract: Provided that infectious prions (PrP{sup Sc}) are inactivated, composting of specified risk material (SRM) may be a viable alternative to rendering and landfilling. In this study, bacterial and fungal communities as well as greenhouse gas emissions associated with the degradation of SRM were examined in laboratory composters over two 14 day composting cycles. Chicken feathers were mixed into compost to enrich for microbial communities involved in the degradation of keratin and other recalcitrant proteins such as prions. Feathers altered the composition of bacterial and fungal communities primarily during the first cycle. The bacterial genera Saccharomonospora, Thermobifida, Thermoactinomycetaceae, Thiohalospira, Pseudomonas, Actinomadura, and Enterobacter, and the fungal genera Dothideomycetes, Cladosporium, Chaetomium, and Trichaptum were identified as candidates involved in SRM degradation. Feathers increased (P < 0.05) headspace concentrations of CH{sub 4} primarily during the early stages of the first cycle and N{sub 2}O during the second. Although inclusion of feathers in compost increases greenhouse gas emissions, it may promote the establishment of microbial communities that are more adept at degrading SRM and recalcitrant proteins such as keratin and PrP{sup Sc}.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Rapid characterization of MSW and RDF feedstocks for waste-to-energy process using LIBS and ML techniques

The heterogeneity in the composition of municipal solid wastes (MSW) poses significant challenges in the production of biofuel and bioproducts. This research aims to enhance the accuracy and efficiency of waste analysis and characterization by introducing a fast characterization approach for MSW-derived refuse-derived fuels (RDF) by combining Laser-Induced Breakdown Spectroscopy (LIBS) with advanced machine learning (ML) techniques. The approach combines data pre-processing of LIBS spectra of RDF, and the development of ML models trained on domain and theory-based spectral features for predicting process parameters. These models are adept at predicting key process parameters like High Heating Value (HHV), carbon content, and volatile matter. This approach can achieve an average RRMSE of 2.13% and R 2 of 0.98 or higher for all considered parameters on testing data. This work demonstrates significant potential for improving waste sorting, processing efficiency, and environmental compliance over traditional labor- and time-intensive laboratory waste analysis and characterization.

09 BIOMASS FUELS↗
DOE OSTI2022

Augmentation of MS/MS Libraries with Spectral Interpolation for Improved Identification

Tandem mass spectrometry (MS/MS) is a primary tool for identification of small molecules and metabolites where resultant spectra are most commonly identified by matching them with spectra in MS/MS reference libraries. The high degree of variability in MS/MS spectrum acquisition techniques and parameters creates a significant challenge for building standardized reference libraries. Here we present a method to improve the usefulness of existing MS/MS libraries by augmenting available experimental spectra datasets with statistically interpolated spectra at unreported collision energies. We find that highly accurate spectral approximations can be interpolated from as few as three experimental spectra and that the interpolated spectra will be consistent with true spectra gathered from the same instrument as the experimental spectra. Supplementing existing spectral databases with interpolated spectra yields consistent improvements to identification accuracy on a range of instruments and precursor types. Applying this method yields significant improvements (~10% more spectra correctly identified) on large datasets (2,000 - 10,000 spectra), indicating this is a quick yet adept tool for improving spectral matching in situations where available reference libraries are not yet sufficient. We also find improvements of matching spectra across instrument types (between an Agilent Q-TOF and an Orbitrap Elite), at high collision energies (50 - 90 eV), and with smaller datasets available through MassBank.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2018

Silicon Nanoribbon pH Sensors Protected by a Barrier Membrane with Carbon Nanotube Porins

Limited biocompatibility and fouling propensity can restrict real-world applications of a large variety of biosensors. Biological systems are adept at protecting and separating vital components of biological machinery with semipermeable membranes that often contain defined pores and gates to restrict transmembrane transport only to specific species. Here we use a similar approach for creating fouling-resistant pH sensors. We integrate silicon nanoribbon transistor sensors with an antifouling lipid bilayer coating that contains proton-permeable carbon nanotube porin (CNTP) channels and demonstrate robust pH detection in a variety of complex biological fluids.

47 OTHER INSTRUMENTATION↗
DOE OSTI2025

Organophosphorus Binding Thermodynamics in Metal–Organic Frameworks: Interplay between Oxidation State, Lewis Acidity, and Node Structure

Organophosphorus compounds, including nerve agents and pesticides, represent a class of toxic chemicals causing harm to troops, civilians, and the environment. Metal–organic frameworks (MOFs) have emerged as a class of highly porous, crystalline, tunable materials adept at both capturing and catalytically neutralizing these harmful toxins. In particular, MOFs whose nodes display strong Lewis acidic character can hydrolyze such chemicals nearly instantaneously. However, without the help of a basic buffer to regenerate the active site, the benign organophosphorus product strongly binds to the node and prevents catalyst turnover. Here, we investigate a series of MOFs whose nodes contain metals of varying Lewis acidities and employ isothermal titration calorimetry (ITC) to directly measure the heat from the binding of an organophosphorus probe molecule, allowing the construction of a full thermodynamic binding profile (ΔH, ΔS, ΔG, K a ). We couple this with potentiometric titrations and solid state 31 P magic angle spinning (MAS) NMR to gain a clearer picture of how node identity, structure, and Lewis acidity interplay to impact binding strength and favorability. Furthermore, this study is the first to integrate these three complementary techniques to investigate binding interactions in MOFs, further showcasing the viability of ITC for probing MOF systems, which is still relatively underexplored.

Lewis acidity↗
DOE OSTI2020

Advancement of Actinide Metal–Organic Framework Chemistry via Synthesis of Pu-UiO-66

We report the synthesis and characterization of the first plutonium metal–organic framework (MOF). Pu-UiO-66 expands the established UiO-66 series, which includes transition metal, lanthanide, and early actinide elements in the hexanuclear nodes. The thermal stability and porosity of Pu-UiO-66 were experimentally determined, and multifaceted computational methods were used to corroborate experimental values, examine inherent defects in the framework, decipher spectroscopic signatures, and elucidate the electronic structure. The crystallization of a plutonium chain side product provides direct evidence of the competition that occurs between modulator and linker in MOF syntheses. Ultimately, the synthesis of Pu-UiO-66 demonstrates adept control of Pu(IV) coordination under hydrolysis-prone conditions, provides an opportunity to extend trends across isostructural UiO-66 frameworks, and serves as the foundation for future plutonium MOF chemistry.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗