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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 325 records · Page 18
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Optimization of Heterologous Glucoraphanin Production In Planta

Glucoraphanin is a plant specialized metabolite found in cruciferous vegetables that has long been a target for production in a heterologous host because it can subsequently be hydrolyzed to form the chemopreventive compound sulforaphane before and during consumption. However, previous studies have only been able to produce small amounts of glucoraphanin in heterologous plant and microbial systems compared to the levels found in glucoraphanin-producing plants, suggesting that there may be missing auxiliary genes that play a role in improving production in planta. Here, in an effort to identify auxiliary genes required for high glucoraphanin production, we leveraged transient expression in Nicotiana benthamiana to screen a combination of previously uncharacterized coexpressed genes and rationally selected genes alongside the glucoraphanin biosynthetic pathway. This strategy alleviated metabolic bottlenecks, which improved glucoraphanin production by 4.74-fold. Our optimized glucoraphanin biosynthetic pathway provides a pathway amenable for high glucoraphanin production.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRSDate not supplied

STARscan: Spatial Targeting and Alignment Rig for Scanning

The Spatial Targeting and Alignment Rig for Scanning (STARScan) is a 3D photogrammetry system developed at NASA Ames Research Center to address bottlenecks in pre/post-test scanning of arcjet test articles. It reduces scan time from 15 minutes with handheld laser scanners to under 2 minutes, while maintaining high accuracy (±0.2-0.5 mm). STARScan integrates an array of cameras, a 3D-printed rack, turntable, and LED light panels, all controlled via a user-friendly graphical user interface (GUI). The system offers tools for scan visualization, mesh analysis, and data export, automating tasks such as alignment of pre/post-test scans, material recession measurements, surface roughness assessment, and curvature analysis. By integrating scanning, imaging, and post-processing into one application, STARScan significantly improves efficiency in scanning and analyzing arcjet test samples.

Ablation↗
DOE OSTI2022

Vertex protein PduN tunes encapsulated pathway performance by dictating bacterial metabolosome morphology

Engineering subcellular organization in microbes shows great promise in addressing bottlenecks in metabolic engineering efforts; however, rules guiding selection of an organization strategy or platform are lacking. Here, we study compartment morphology as a factor in mediating encapsulated pathway performance. Using the 1,2-propanediol utilization microcompartment (Pdu MCP) system from Salmonella enterica serovar Typhimurium LT2, we find that we can shift the morphology of this protein nanoreactor from polyhedral to tubular by removing vertex protein PduN. Analysis of the metabolic function between these Pdu microtubes (MTs) shows that they provide a diffusional barrier capable of shielding the cytosol from a toxic pathway intermediate, similar to native MCPs. However, kinetic modeling suggests that the different surface area to volume ratios of MCP and MT structures alters encapsulated pathway performance. Finally, we report a microscopy-based assay that permits rapid assessment of Pdu MT formation to enable future engineering efforts on these structures.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Revealing EDL-driven reduction mechanisms in binary, ternary, and quaternary fluorinated electrolytes via an integrated MD–DFT–ML framework

Accurately predicting solid electrolyte interphase (SEI) formation requires explicitly resolving the electric double layer (EDL) structure, which deviates significantly from that of the bulk electrolyte. Although an established molecular dynamics (MD) and Density Functional Theory (DFT) framework can model SEI formation by evaluating reduction reactions of local clusters in the EDL, it suffers from a combinatorial computational bottleneck. To overcome this limitation, we introduce a machine-learning-accelerated simulation workflow (MD–DFT–ML), integrating a gradient-boosted regression model trained on EDL composition data to efficiently predict reduction potentials. We apply this framework to seven fluorinated electrolytes comprising fluorinated anions, a fluorinated ester solvent, two types of diluent (ion-solvating ester vs. non-solvating ether), and an FEC additive. The analysis shows that the EDL selectively accumulates cation-binding species; consequently, the non–cation-binding ether diluent rarely enters the EDL and makes minimal contributions to SEI formation. DFT calculations on statistically representative EDL clusters provide reduction potentials and fluorine-release pathways, while the ML model, which substantially reduces the DFT workload, predicts cluster reduction energies with a mean absolute error of 0.1 eV. The combined MD–DFT–ML approach also quantifies contributions from different sources to LiF formation in the SEI. This methodology establishes a generalizable route for multiscale modeling electrolyte and interphase design for next-generation electrochemical energy-storage systems.

DFT-MD-ML workflow↗
DOE OSTI2016

Systematic identification and analysis of frequent gene fusion events in metabolic pathways

Here, gene fusions are the most powerful type of in silico -derived functional associations. However, many fusion compilations were made when <100 genomes were available, and algorithms for identifying fusions need updating to handle the current avalanche of sequenced genomes. The availability of a large fusion dataset would help probe functional associations and enable systematic analysis of where and why fusion events occur. As a result, here we present a systematic analysis of fusions in prokaryotes. We manually generated two training sets: (i) 121 fusions in the model organism Escherichia coli ; (ii) 131 fusions found in B vitamin metabolism. These sets were used to develop a fusion prediction algorithm that captured the training set fusions with only 7 % false negatives and 50 % false positives, a substantial improvement over existing approaches. This algorithm was then applied to identify 3.8 million potential fusions across 11,473 genomes. The results of the analysis are available in a searchable database. A functional analysis identified 3,000 reactions associated with frequent fusion events and revealed areas of metabolism where fusions are particularly prevalent. In conclusion, customary definitions of fusions were shown to be ambiguous, and a stricter one was proposed. Exploring the genes participating in fusion events showed that they most commonly encode transporters, regulators, and metabolic enzymes. The major rationales for fusions between metabolic genes appear to be overcoming pathway bottlenecks, avoiding toxicity, controlling competing pathways, and facilitating expression and assembly of protein complexes. Finally, our fusion dataset provides powerful clues to decipher the biological activities of domains of unknown function.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Development of a high-throughput method for processing sponge-stick samples to detect viable Bacillus anthracis spores

Since the national validation of the sponge-stick based method for detection of Bacillus anthracis spores in environmental samples, there have not been focused efforts to address the low throughput nature of the method, which processes only one sample at one time. Sample processing remains a serious bottleneck for rapidly analyzing large numbers of samples expected from a biological warfare attack. Therefore, we developed a high-throughput method to simultaneously process multiple sponge-stick samples to be better prepared for rapid response and recovery after wide area anthrax incidents. In this method, sponges are placed in 50 mL tubes containing 25 mL extraction buffer and shaken to release spores, after which the suspension is recovered for analysis. Here, we determined that an additional extraction step, conducted in the same tubes with 10 mL buffer, further increased spore recovery from sponge-stick by approximately 10 %. We determined that orbital shaking and multi-tube vortexing were both more effective than reciprocating shaking for recovering spores. We conducted simultaneous processing of up to 12 sponge-stick samples and demonstrated comparable spore recovery efficiencies to the traditional low-throughput stomacher-based method (approximately 60 % recovery at 10 2 -spore level and 75 % recovery at 10 4 -spore level for both methods in three replicate experiments, P > 0.05 for two-tailed t-tests for each experiment and spore level). We also demonstrated that our high-throughput method could be integrated with Rapid Viability-Polymerase Chain Reaction (RV-PCR) analysis and could detect levels as low as 40 spores per sponge even when challenged by a PCR particulate contaminant.

Anthrax↗
DOE OSTI2021

Probing atomic-scale symmetry breaking by rotationally invariant machine learning of multidimensional electron scattering

Abstract The 4D scanning transmission electron microscopy (STEM) method maps the structure and functionality of solids on the atomic scale, yielding information-rich data sets describing the interatomic electric and magnetic fields, structural and electronic order parameters, and other symmetry breaking distortions. A critical bottleneck is the dearth of analytical tools that can reduce complex 4D-STEM data to physically relevant descriptors. We propose an approach for the systematic exploration of 4D-STEM data using rotationally invariant variational autoencoders (rrVAE), which disentangle the general rotation of the object from other latent representations. The implementation of purely rotational rrVAE is discussed as are applications to simulated data for graphene and zincblende structures. The rrVAE analysis of experimental 4D-STEM data of defects in graphene is illustrated and compared to the classical center-of-mass analysis. This approach is universal for probing symmetry-breaking phenomena in complex systems and can be implemented for a broad range of diffraction methods.

36 MATERIALS SCIENCE↗
DOE OSTI2015

Three-dimensional morphology of CuInS{sub 2}:P3HT hybrid blends for photovoltaic applications

Despite potential advantages, the performance of hybrid solar cells with colloidal nanocrystals remains low compared to pure organic solar cells, in particular, when Cd- and Pb-free nanocrystals are employed. To understand this discrepancy, we analyzed possible limiting factors of the performance of hybrid solar cells with CuInS{sub 2} nanoparticles and the polymer poly(3-hexylthiophene) (P3HT). Optimizing the thickness of the active layer indicated that charge transport limits the performance of the solar cells. Since charge transport is among others influenced by the morphology of the bulk heterojunction layer, we performed a detailed analysis of the blend morphology. Therefore, we used electron tomography which provides three-dimensional information on the interpenetrating network formed by the hybrid CuInS{sub 2}:P3HT system. Using statistical methods, we analyzed the distribution of the nanoparticles inside the polymer matrix and the structure of the percolation paths. We found that the morphology appears well suited for application in hybrid solar cells, meaning that other factors must be the bottleneck. Therefore, we investigated in a second step the influence of a post-deposition ligand exchange with acetic acid. This strategy resulted in a strong relative improvement of the solar cell performance, although absolute performance parameters remain low in comparison to hybrid solar cells with colloidal cadmium or lead chalcogenide nanocrystals.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2021

sas-temper

Modeling remains a considerable challenge for practitioners of SAXS and SANS because the materials being studied are not highly ordered, the length scales being studied are large, and the information content of the data is low. Data analysis is both challenging and time consuming. Novices often rely on the assistance of an expert, such as the instrument scientist who supported them at the facility where the experiment was performed, to analyze their data. The high flux provided by modern facilities makes it possible to study dozens of samples or sample conditions during a single trip. Ultimately, the high throughput of modern instruments limits access to instrument scientists. A bottleneck in the research effort results that reduces facility productivity. Sas-temper seeks to address this problem, as well as the intrinsic difficulties of being confident in non-linear least squared fitting of data, by providing tools that automate much of the manual process of initial data fitting and refinement, as well as by providing tools for characterizing the nature of the parameter space that fits the measured data. The tools provided by sas-temper help small-angle scattering practitioners transition data into results.

Heller, William [Oak Ridge National Lab. (ORNL), O↗
NASA NTRS2017

Performance Enhancement of a Computational Persistent Homology Package

In recent years, persistent homology has become an attractive method for data analysis. It captures topological features, such as connected components, holes, voids, etc., from a point cloud by finding out when these features appear and disappear in the filtration sequence. In this project, we focus on improving the performance of Eirene, a fancy computational persistent homology package. Eirene is a 5000-line opensource software implemented by using the dynamic programming language Julia. We use the Julia profiling tools to identify the performance bottlenecks and develop different methods to manage the bottlenecks, including the parallelization of some time-consuming functions on the multicore/manycore hardware. The empirical results show that the performance can be greatly improved.

Profiling↗
DOE OSTI2020

Rapid Memory Footprint Access Diagnostics

Footprint and reuse distance measure temporal locality and therefore do not capture the significance of access patterns (spacial locality). A strided access pattern has the largest possible footprint but usually has the best performance. To highlight exposed memory latency, we separate footprint into strided (prefetchable) and irregular (non-prefetchable) access components and calculate the growth rate of each. To rapidly compute these footprint access diagnostics, we present two methods, whole-program and precise. Current footprint analyses can cause 200× or more slowdown with realistic inputs and are therefore impractical. Our whole-program method reduces the overhead to 10% by computing upper bounds, but still yields inter-procedural insight through a call path profile. Our precise method uses additional static analysis and profiling to refine the upper bounds for intra-procedural loop nests. We evaluate our approaches using benchmarks that vary access patterns (strided \vs unpredictable), sparsity (all words in a cache line \vs some), and reuse (varying and repeated accesses per element). Notably, for loop nests with unpredictable accesses, the precise method's accuracy is within 10% of ideal. The whole-program method has sufficient accuracy to diagnose bottlenecks.

data locality, memory footprint, footprint access ↗
NASA NTRS2019

Tuning the Performance of a Computational Persistent Homology Package

In recent years, persistent homology has become an attractive method for data analysis. It captures topological features, such as connected components, holes, and voids from point cloud data and summarizes the way in which these features appear and disappear in a filtration sequence. In this project, we focus on improving the performanceof Eirene, a computational package for persistent homology. Eirene is a 5000-line open-source software library implemented in the dynamic programming language Julia. We use the Julia profiling tools to identify performance bottlenecks and develop novel methods to manage them, including the parallelization of some time-consuming functions on multicore/manycore hardware. Empirical results show that performance can be greatly improved.

Persistent Homology↗
DOE OSTI2017

Roofline Analysis in the Intel® Advisor to Deliver Optimized Performance for applications on Intel® Xeon Phi™ Processor

In this session we show, in two case studies, how the roofline feature of Intel Advisor has been utilized to optimize the performance of kernels of the XGC1 and PICSAR codes in preparation for Intel Knights Landing architecture. The impact of the implemented optimizations and the benefits of using the automatic roofline feature of Intel Advisor to study performance of large applications will be presented. This demonstrates an effective optimization strategy that has enabled these science applications to achieve up to 4.6 times speed-up and prepare for future exascale architectures. # Goal/Relevance of Session The roofline model [1,2] is a powerful tool for analyzing the performance of applications with respect to the theoretical peak achievable on a given computer architecture. It allows one to graphically represent the performance of an application in terms of operational intensity, i.e. the ratio of flops performed and bytes moved from memory in order to guide optimization efforts. Given the scale and complexity of modern science applications, it can often be a tedious task for the user to perform the analysis on the level of functions or loops to identify where performance gains can be made. With new Intel tools, it is now possible to automate this task, as well as base the estimates of peak performance on measurements rather than vendor specifications. The goal of this session is to demonstrate how the roofline feature of Intel Advisor can be used to balance memory vs. computation related optimization efforts and effectively identify performance bottlenecks. A series of typical optimization techniques: cache blocking, structure refactoring, data alignment, and vectorization illustrated by the kernel cases will be addressed. # Description of the codes ## XGC1 The XGC1 code [3] is a magnetic fusion Particle-In-Cell code that uses an unstructured mesh for its Poisson solver that allows it to accurately resolve the edge plasma of a magnetic fusion device. After recent optimizations to its collision kernel [4], most of the computing time is spent in the electron push (pushe) kernel, where these optimization efforts have been focused. The kernel code scaled well with MPI+OpenMP but had almost no automatic compiler vectorization, in part due to indirect memory addresses and in part due to low trip counts of low-level loops that would be candidates for vectorization. Particle blocking and sorting have been implemented to increase trip counts of low-level loops and improve memory locality, and OpenMP directives have been added to vectorize compute-intensive loops that were identified by Advisor. The optimizations have improved the performance of the pushe kernel 2x on Haswell processors and 1.7x on KNL. The KNL node-for-node performance has been brought to within 30% of a NERSC Cori phase I Haswell node and we expect to bridge this gap by reducing the memory footprint of compute intensive routines to improve cache reuse. ## PICSAR is a Fortran/Python high-performance Particle-In-Cell library targeting at MIC architectures first designed to be coupled with the PIC code WARP for the simulation of laser-matter interaction and particle accelerators. PICSAR also contains a FORTRAN stand-alone kernel for performance studies and benchmarks. A MPI domain decomposition is used between NUMA domains and a tile decomposition (cache-blocking) handled by OpenMP has been added for shared-memory parallelism and better cache management. The so-called current deposition and field gathering steps that compose the PIC time loop constitute major hotspots that have been rewritten to enable more efficient vectorization. Particle communications between tiles and MPI domain has been merged and parallelized. All considered, these improvements provide speedups of 3.1 for order 1 and 4.6 for order 3 interpolation shape factors on KNL configured in SNC4 quadrant flat mode. Performance is similar between a node of cori phase 1 and KNL at order 1 and better on KNL by a factor 1.6 at order 3 with the considered test case (homogeneous thermal plasma).

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

Enhancing Operational Performance and Productivity Benefits by Implementing Smart Manufacturing Technologies in Breweries

This paper uses case studies to demonstrate the potential of smart manufacturing (SM) and Internet of Things (IoT) technologies to enhance operational performance and productivity in the industry. The analysis highlights benefits like cost reduction, production flexibility, shorter product times-to-market, energy/water efficiency and environmental impact reduction, and increased productivity. To illustrate the effectiveness of SM and IoT approaches, the authors sought out manufacturers currently implementing or seeking to implement SM & IoT technologies. The authors identified beer brewing (NAICS Code —312120) as a rapidly expanding industry whose members appear eager to implement SM technologies to optimize production lines by revealing bottlenecks and identifying performance-reducing nodes. The paper presents two case studies based on SM and IoT technologies in breweries. It briefly describes a systematic framework introduced elsewhere by the authors and uses it to assess the energy productivity and competitiveness of SM applications in breweries. The paper addresses questions concerning the information and communications technology infrastructure needed to build smart breweries, and how corporations simplify the installation and deployment of SM and IoT components.

Nimbalkar, Sachin U.↗
DOE OSTI2016

Ancestral genome reconstruction identifies the evolutionary basis for trait acquisition in polyphosphate accumulating bacteria

The evolution of complex traits is hypothesized to occur incrementally. Identifying the transitions that lead to extant complex traits may provide a better understanding of the genetic nature of the observed phenotype. A keystone functional group in wastewater treatment processes are polyphosphate accumulating organisms (PAOs), however the evolution of the PAO phenotype has yet to be explicitly investigated and the specific metabolic traits that discriminate non-PAO from PAO are currently unknown. Here we perform the first comprehensive investigation on the evolution of the PAO phenotype using the model uncultured organism Candidatus Accumulibacter phosphatis (Accumulibacter) through ancestral genome reconstruction, identification of horizontal gene transfer, and a kinetic/stoichiometric characterization of Accumulibacter Clade IIA. The analysis of Accumulibacter’s last common ancestor identified 135 laterally derived genes, including genes involved in glycogen, polyhydroxyalkanoate, pyruvate and NADH/NADPH metabolisms, as well as inorganic ion transport and regulatory mechanisms. In contrast, pathways such as the TCA cycle and polyphosphate metabolism displayed minimal horizontal gene transfer. We show that the transition from non-PAO to PAO coincided with horizontal gene transfer within Accumulibacter’s core metabolism; likely alleviating key kinetic and stoichiometric bottlenecks, such as anaerobically linking glycogen degradation to polyhydroxyalkanoate synthesis. These results demonstrate the utility of investigating the derived genome of a lineage to identify key transitions leading to an extant complex phenotype.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Exploring the dependence of gas cooling and heating functions on the incident radiation field with machine learning

ABSTRACT Gas cooling and heating functions play a crucial role in galaxy formation. But, it is computationally expensive to exactly compute these functions in the presence of an incident radiation field. These computations can be greatly sped up by using interpolation tables of pre-computed values, at the expense of making significant and sometimes even unjustified approximations. Here, we explore the capacity of machine learning to approximate cooling and heating functions with a generalized radiation field. Specifically, we use the machine learning algorithm XGBoost to predict cooling and heating functions calculated with the photoionization code cloudy at fixed metallicity, using different combinations of photoionization rates as features. We perform a constrained quadratic fit in metallicity to enable a fair comparison with traditional interpolation methods at arbitrary metallicity. We consider the relative importance of various photoionization rates through both a principal component analysis (PCA) and calculation of SHapley Additive exPlanation (shap) values for our XGBoost models. We use feature importance information to select different subsets of rates to use in model training. Our XGBoost models outperform a traditional interpolation approach at each fixed metallicity, regardless of feature selection. At arbitrary metallicity, we are able to reduce the frequency of the largest cooling and heating function errors compared to an interpolation table. We find that the primary bottleneck to increasing accuracy lies in accurately capturing the metallicity dependence. This study demonstrates the potential of machine learning methods such as XGBoost to capture the non-linear behaviour of cooling and heating functions.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

Enhancing Operational Performance and Productivity Benefits in Breweries through Smart Manufacturing Technologies

This article uses case studies to demonstrate the potential of smart manufacturing (SM) and Internet of Things (IoT) technologies to enhance operational performance and productivity in industry. The analysis highlights benefits such as cost reduction, production flexibility, shorter product time-to-market, energy/water efficiency and environmental impact reduction, and increased productivity. To illustrate the effectiveness of SM and IoT approaches, the authors sought out manufacturers currently implementing or seeking to implement SM and IoT technologies. The authors identified beer brewing (NAICS Code 312120) as a rapidly expanding industry whose members appear eager to implement SM technologies to optimize production lines by revealing bottlenecks and identifying performance-reducing nodes. This article presents two case studies based on SM and IoT technologies in breweries. It briefly describes a systematic framework introduced elsewhere by the authors and uses it to assess the energy productivity and competitiveness of SM applications in breweries. This article addresses questions concerning the information and communications technology infrastructure needed to build smart breweries and how corporations simplify the installation and deployment of SM and IoT components. This article was originally published in the Proceedings of the 2019 ACEEE Summer Study on Energy Efficiency in Industry.

42 ENGINEERING↗
DOE OSTI2025

Breaking the barrier of human-annotated training data for machine learning-aided plant research using aerial imagery

Machine learning (ML) can accelerate biological research. However, the adoption of such tools to facilitate phenotyping based on sensor data has been limited by (i) the need for a large amount of human-annotated training data for each context in which the tool is used and (ii) phenotypes varying across contexts defined in terms of genetics and environment. This is a major bottleneck because acquiring training data is generally costly and time-consuming. This study demonstrates how a ML approach can address these challenges by minimizing the amount of human supervision needed for tool building. A case study was performed to compare ML approaches that examine images collected by an uncrewed aerial vehicle to determine the presence/absence of panicles (i.e. “heading”) across thousands of field plots containing genetically diverse breeding populations of 2 Miscanthus species. Automated analysis of aerial imagery enabled the identification of heading approximately 9 times faster than in-field visual inspection by humans. Leveraging an Efficiently Supervised Generative Adversarial Network (ESGAN) learning strategy reduced the requirement for human-annotated data by 1 to 2 orders of magnitude compared to traditional, fully supervised learning approaches. The ESGAN model learned the salient features of the data set by using thousands of unlabeled images to inform the discriminative ability of a classifier so that it required minimal human-labeled training data. This method can accelerate the phenotyping of heading date as a measure of flowering time in Miscanthus across diverse contexts (e.g. in multistate trials) and opens avenues to promote the broad adoption of ML tools.

59 BASIC BIOLOGICAL SCIENCES↗