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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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All sourcesNASA NTRSDOE OSTI
DOE OSTI2013

Summer Student Internship working on the GPU

This summer internship consisted of learning computer programming skills and languages including Thrust, CUDA, C++, C and Linux. The application of this programming is for use in GPU, graphics processing units, that are processors that perform math intensive calculations rapidly in three dimensions typically in advanced video games (“NVIDIA GeForce…”). Our goal is to apply this technology towards exa-scale computing on the LANL supercomputing infrastructure. My mentors use these machines to do computations. The completed algorithms include KD tree and Top K. In order to access the GPU, CUDA was used. CUDA is a parallel computer architecture for general computing on the GPUs. There are different means by which to access CUDA. The first approach uses libraries, like Thrust, Linear Algebra, and Signal and Image Processing. Another approach parallelizes loops in Fortran or C and in the last approach people develop custom parallel algorithms using familiar language (“What is CUDA”). The algorithms I worked on this summer use the first approach of using the thrust library of parallel algorithms and data structures.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2017

Beyond 100 Gb/s: Capacity, Flexibility, and Network Optimization

Here, we discuss building blocks that enable the exploitation of optical capacities beyond 100 Gb/s. Optical networks will benefit from more flexibility and agility in their network elements, especially from coherent transceivers. To achieve capacities of 400 Gb/s and more, coherent transceivers will operate at higher symbol rates. This will be made possible with higher bandwidth components using new electro-optic technologies implemented with indium phosphide and silicon photonics. Digital signal processing will benefit from new algorithms. Multi-dimensional modulation, of which some formats are already in existence in current flexible coherent transceivers, will provide improved tolerance to noise and fiber nonlinearities. Constellation shaping will further improve these tolerances while allowing a finer granularity in the selection of capacity. Frequency-division multiplexing will also provide improved tolerance to the nonlinear characteristics of fibers. Algorithms with reduced computation complexity will allow the implementation, at speeds, of direct pre-compensation of nonlinear propagation effects. Advancement in forward error correction will shrink the performance gap with Shannon's limit. At the network control and management level, new tools are being developed to achieve a more efficient utilization of networks. This will also allow for network virtualization, orchestration, and management. Lastly, FlexEthernet and FlexOTN will be put in place to allow network operators to optimize capacity in their optical transport networks without manual changes to the client hardware.

47 OTHER INSTRUMENTATION↗
DOE OSTI2015

Crossing Over: Nanostructures that Move Electrons and Ions Across Cellular Membranes

Critical biological processes such as energy generation and signal transduction are driven by the flow of electrons and ions across the membranes of living cells. As a result, there is substantial interest in creating nanostructured materials that control transport of these charged species across biomembranes. The recent advances in the synthesis of de novo and protein nanostructures for transmembrane ion and electron transport and the mechanistic understanding underlying this transport are described. Moreover, this body of work highlights the promise such nanostructures hold for directing transmembrane transport of charged species as well as challenges that must be overcome to realize that potential.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Functional Diversification of ER Stress Responses in Arabidopsis

The endoplasmic reticulum (ER) is responsible for the synthesis of one-third of the cellular proteome and is constantly challenged by physiological and environmental situations that can perturb its homeostasis and lead to the accumulation of misfolded secretory proteins, a condition referred to as ER stress. In response, the ER evokes a set of intracellular signaling processes, collectively known as the unfolded protein response (UPR), which are designed to restore biosynthetic capacity of the ER. As single-cell organisms evolved into multicellular life, the UPR complexity has increased to suit their growth and development. In this review, we discuss recent advances in the understanding of the UPR, emphasizing conserved UPR elements between plants and metazoans and highlighting unique plant-specific features.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2018

Cell concentrations and metabolites enhance the SIP response to biofilm matrix components

Here we investigated the relative effect of different bacterial biofilm components to influence spectral induced polarization (SIP) signals and Cole-Cole model parameters (i.e. normalized chargeability (S m –1 ) and relaxation time ($τ$)) in order to reduce the ambiguity associated with interpretation of SIP signatures in biostimulated environments. SIP measurements were collected over a frequency range, (i.e. 0.1 Hz –1 kHz) from experimental columns containing Pseudomonas aeruginosa PAO1 (non-mucoid bacterial strain) cell suspensions alone and cell suspensions mixed with different molecules associated with P. aeruginosa biofilm matrix (alginate, DNA and phenazine). Results from SIP measurements of cell density investigations suggest an increase in phase (~ 200%), imaginary conductivity (σ" S m –1 ), and normalized chargeability (S m –1 ) values as cell densities increase (1.50–5.55 × 10 9 CFUml –1 ), yet no significant change in the real conductivity (σ' S m –1 ) or relaxation time was observed. The addition of alginate resulted in a suppression of phase (φ mrads), imaginary conductivity (σ" S m –1 ), and normalized chargeability (S m –1 ) response. However, a mixture of cells and alginate increased all parameters but with no significant changes in real conductivity (σ' S m –1 ) or relaxation time. Similar trend was observed in the DNA treatments; increasing concentrations of DNA reduced the imaginary conductivity (σ" S m –1 ), phase (φ mrads), and normalized chargeability (S m –1 ) response. In contrast, the addition of phenazine, a secondary metabolite of P. aeruginosa, resulted in a significant increase in both imaginary conductivity (σ" S m –1 ), phase (φ mrads), and normalized chargeability (S m –1 ) values especially at larger concentrations (>50 mM). Our work suggests that cell density and metabolites are significant factors increasing the polarization response in biofilms and advances the interpretation of electrical signals associated with biofilms in the subsurface.

58 GEOSCIENCES↗
DOE OSTI2021

Temporal dynamics of protein and post-translational modification abundances in Populus leaf across a diurnal period

Populus spp. are dedicated woody biomass feedstocks for advanced biofuels and bioproducts. Proper growth and fitness of poplar as a sustainable feedstock depends on timely perception and response to environmental signals (e.g., light, temperature, water). Poplar leaves, like other C3 photosynthesis plants, have evolved oscillating or circadian rhythms that play important roles in synchronizing biological processes with external cues. To characterize this phenomenon at a molecular level, we employed bottom-up proteomics using high-resolution mass spectrometry and de novo-assisted database searching to identify abundance changes in proteins and post-translational modifications in poplar leaf tissue sampled across a 12/12-hour light/dark diurnal period.

09 BIOMASS FUELS↗
DOE OSTI2013

AMoRE: Collaboration for searches for the neutrinoless double-beta decay of the isotope of {sup 100}Mo with the aid of {sup 40}Ca{sup 100}MoO{sub 4} as a cryogenic scintillation detector

The AMoRE (Advanced Mo based Rare process Experiment) Collaboration is planning to employ {sup 40}Ca{sup 100}MoO{sub 4} single crystals as a cryogenic Scintillation detector for studying the neutrinoless double-beta decay of the isotope {sup 100}Mo. A simultaneous readout of phonon and scintillation signals is performed in order to suppress the intrinsic background. The planned sensitivity of the experiment that would employ 100 kg of {sup 40}Ca{sup 100}MoO{sub 4} over five years of data accumulation would be T{sub 1/2}{sup 0{nu}} = 3 Multiplication-Sign 10{sup 26} yr, which corresponds to values of the effective Majorana neutrino mass in the range of Left-Pointing-Angle-Bracket m{sub {nu}} Right-Pointing-Angle-Bracket {approx} 0.02-0.06 eV.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2024

Unsupervised multimodal fusion of in-process sensor data for advanced manufacturing process monitoring

Effective monitoring of manufacturing processes is crucial for maintaining product quality and operational efficiency. Modern manufacturing environments often generate vast amounts of complementary multimodal data, including visual imagery from various perspectives and resolutions, hyperspectral data, and machine health monitoring information such as actuator positions, accelerometer readings, and temperature measurements. However, fusing and interpreting this complex, high-dimensional data presents significant challenges, particularly when labeled datasets are unavailable or impractical to obtain. This paper presents a novel approach to multimodal sensor data fusion in manufacturing processes, inspired by the Contrastive Language-Image Pre-training (CLIP) model. We leverage contrastive learning techniques to correlate different data modalities without the need for labeled data, overcoming limitations of traditional supervised machine learning methods in manufacturing contexts. Our proposed method demonstrates the ability to handle and learn encoders for five distinct modalities: visual imagery, audio signals, laser position (x and y coordinates), and laser power measurements. By compressing these high-dimensional datasets into low-dimensional representational spaces, our approach facilitates downstream tasks such as process control, anomaly detection, and quality assurance. The unsupervised nature of our method makes it broadly applicable across various manufacturing domains, where large volumes of unlabeled sensor data are common. We evaluate the effectiveness of our approach through a series of experiments, demonstrating its potential to enhance process monitoring capabilities in advanced manufacturing systems. This research contributes to the field of smart manufacturing by providing a flexible, scalable framework for multimodal data fusion that can adapt to diverse manufacturing environments and sensor configurations. The proposed method paves the way for more robust, data-driven decision-making in complex manufacturing processes.

Contrastive Learning↗
DOE OSTI2026

Machine learning pipeline for denoising low signal-to-noise ratio and out-of-distribution transmission electron microscopy datasets

High-resolution transmission electron microscopy (HRTEM) is crucial for observing material’s structural and morphological evolution at Angstrom scales, but the electron beam can alter these processes. Devices such as CMOS-based direct-electron detectors operating in electron-counting mode can be utilized to substantially reduce the electron dosage. However, the resulting images often lead to a low signal-to-noise ratio, which requires frame integration that sacrifices temporal resolution. Several machine learning (ML) models have been recently developed to successfully denoise HRTEM images. Yet, these models are often computationally expensive, and their inference speeds on GPUs are outpaced by the imaging speed of advanced detectors, precluding in situ analysis. Furthermore, the performance of these denoising models on datasets with imaging conditions that deviate from the training datasets has not been evaluated. To mitigate these gaps, we propose a new self-supervised ML denoising pipeline specifically designed for time-series HRTEM images. This pipeline integrates a blind-spot convolution neural network with pre-processing and post-processing steps, including drift correction and low-pass filtering. Results demonstrate that our model outperforms various other ML and non-ML denoising methods in noise reduction and contrast enhancement, leading to improved visual clarity of atomic features. Additionally, the model is drastically faster than U-Net-based ML models and demonstrates excellent out-of-distribution generalization. The model’s computational inference speed is in the order of milliseconds per image, rendering it suitable for application in in-situ HRTEM experiments.

36 MATERIALS SCIENCE↗
DOE OSTI2013

The detective quantum efficiency of photon-counting x-ray detectors using cascaded-systems analyses

Purpose: Single-photon counting (SPC) x-ray imaging has the potential to improve image quality and enable new advanced energy-dependent methods. The purpose of this study is to extend cascaded-systems analyses (CSA) to the description of image quality and the detective quantum efficiency (DQE) of SPC systems. Methods: Point-process theory is used to develop a method of propagating the mean signal and Wiener noise-power spectrum through a thresholding stage (required to identify x-ray interaction events). The new transfer relationships are used to describe the zero-frequency DQE of a hypothetical SPC detector including the effects of stochastic conversion of incident photons to secondary quanta, secondary quantum sinks, additive noise, and threshold level. Theoretical results are compared with Monte Carlo calculations assuming the same detector model. Results: Under certain conditions, the CSA approach can be applied to SPC systems with the additional requirement of propagating the probability density function describing the total number of image-forming quanta through each stage of a cascaded model. Theoretical results including DQE show excellent agreement with Monte Carlo calculations under all conditions considered. Conclusions: Application of the CSA method shows that false counts due to additive electronic noise results in both a nonlinear image signal and increased image noise. There is a window of allowable threshold values to achieve a high DQE that depends on conversion gain, secondary quantum sinks, and additive noise.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2015

Combining of different data pools for calculating a reliable POD for real defects

Real defects are essential for the evaluation of the reliability of non destructive testing (NDT) methods, especially in relation to the integrity of components. But in most of the cases the amount of available real defects is not sufficient to evaluate the system. Model-assisted and transfer functions are one way to handle that challenge. This study is focused on a combination of different data pools to create a sufficient amount of data for the reliability estimation. A widespread approach for calculating the Probability of Detection (POD) was used on a radiographic testing (RT) method. The highest contrast to noise ratio (CNR) of each indication is usually selected as the signal in the 'â vs. a' (signal-response) approach for RT. By combining real and artificial defects (flat bottom holes, side drill holes, flat bottom squares, notches, etc) in RT the highest signals are close to each other, but the process of creating and evaluating real defects is much more complex. The solution is seen in the combination of real and artificial data using a weighted least square approach. The weights for real or artificial data were based on the importance, the value and the different detection behavior of the different data. For comparison, the alternative combination through the Bayesian Updating was also applied. As verification, a data pool with a large amount of real data was available. In an advanced approach for evaluating the digital RT data, the size of the indication (perpendicular to the X-ray beam) was introduced as additional information. The signal now consists of the CNR and the area of the indication. The detectability is changing depending on the area of the indication, a fact that was ignored in the previous POD calculations for RT. This points out that a weighted least square approach to pool the data might no longer be adequate. The Bayesian Updating of the estimated parameters of the relationship between the signal field (the area of the indication) and the geometry of the defects is seen as the appropriate model to combine the different defect types in a useful and meaningful way. This work was carried out together with the Finnish company for spent nuclear fuel and waste management - Posiva Oy. The digital RT is one of the NDT methods that might be used for the inspection of the weld of the copper canister to be used for the spent nuclear fuel in the Scandinavian concept of final disposal.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2026

Raman Spectroscopic In Situ Monitoring of Highly Turbid Media

The ability to run chemical processing more efficiently and cost effectively is a need that spans critical materials recovery and legacy nuclear waste cleanup. Sensors integrated to provide online monitoring are essential to addressing this need by providing near-real time feedback on process conditions, which can improve efficiency, aid in decision making, and reduce the need for grab sample measurements. Optical spectroscopy is well-suited for providing online chemical composition information and has been widely applied in varied chemical systems. However, applications in turbid matrices continue to represent substantial challenges to sensor performance, where absorption or scattering of excitation light can cause significant signal interference. Here, in this study, close-focus Raman probes are investigated for use in turbid media as a way to overcome the signal loss from the scattering of the Raman excitation source. This, paired with advanced data science techniques, allowed for the development of chemometric models for the accurate quantification of several analytes of interest (NO 3 – , NO 2 – , and PO 4 3– ) in highly turbid solutions with solids loadings of up to 20 wt %. This work focuses on offline sample measurement and characterization as an initial step toward the development of online monitoring capabilities. Chemical systems of interest were focused on nuclear waste at the Hanford Site, which represents highly complex matrices that could realize significant processing benefits through the integration of online monitoring.

Felmy, Heather M. [Pacific Northwest National Labo↗
DOE OSTI2013

Sertad1 encodes a novel transcriptional co-activator of SMAD1 in mouse embryonic hearts

Highlights: •SERTAD1 interacts with SMAD1. •Sertad1 is expressed in mouse embryonic hearts. •SERTAD1 is localized in both cytoplasm and nucleus of cardiomyocytes. •SERTAD1 enhances expression of BMP target cardiogenic genes as a SMAD1 co-activator. -- Abstract: Despite considerable advances in surgical repairing procedures, congenital heart diseases (CHDs) remain the leading noninfectious cause of infant morbidity and mortality. Understanding the molecular/genetic mechanisms underlying normal cardiogenesis will provide essential information for the development of novel diagnostic and therapeutic strategies against CHDs. BMP signaling plays complex roles in multiple cardiogenic processes in mammals. SMAD1 is a canonical nuclear mediator of BMP signaling, the activity of which is critically regulated through its interaction partners. We screened a mouse embryonic heart yeast two-hybrid library using Smad1 as bait and identified SERTAD1 as a novel interaction partner of SMAD1. SERTAD1 contains multiple potential functional domains, including two partially overlapping transactivation domains at the C terminus. The SERTAD1-SMAD1 interaction in vitro and in mammalian cells was further confirmed through biochemical assays. The expression of Sertad1 in developing hearts was demonstrated using RT-PCR, western blotting and in situ hybridization analyses. We also showed that SERTAD1 was localized in both the cytoplasm and nucleus of immortalized cardiomyocytes and primary embryonic cardiomyocyte cultures. The overexpression of SERTAD1 in cardiomyocytes not only enhanced the activity of two BMP reporters in a dose-dependent manner but also increased the expression of several known BMP/SMAD regulatory targets. Therefore, these data suggest that SERTAD1 acts as a SMAD1 transcriptional co-activator to promote the expression of BMP target genes during mouse cardiogenesis.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

Improved tomographic reconstruction of large-scale real-world data by filter optimization

In advanced tomographic experiments, large detector sizes and large numbers of acquired datasets can make it difficult to process the data in a reasonable time. At the same time, the acquired projections are often limited in some way, for example having a low number of projections or a low signal-to-noise ratio. Direct analytical reconstruction methods are able to produce reconstructions in very little time, even for large-scale data, but the quality of these reconstructions can be insufficient for further analysis in cases with limited data. Iterative reconstruction methods typically produce more accurate reconstructions, but take significantly more time to compute, which limits their usefulness in practice. In this paper, we present the application of the SIRT-FBP method to large-scale real-world tomographic data. The SIRT-FBP method is able to accurately approximate the simultaneous iterative reconstruction technique (SIRT) method by the computationally efficient filtered backprojection (FBP) method, using precomputed experiment-specific filters. We specifically focus on the many implementation details that are important for application on large-scale real-world data, and give solutions to common problems that occur with experimental data. We show that SIRT-FBP filters can be computed in reasonable time, even for large problem sizes, and that precomputed filters can be reused for future experiments. Reconstruction results are given for three different experiments, and are compared with results of popular existing methods. The results show that the SIRT-FBP method is able to accurately approximate iterative reconstructions of experimental data. Furthermore, they show that, in practice, the SIRT-FBP method can produce more accurate reconstructions than standard direct analytical reconstructions with popular filters, without increasing the required computation time.

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

Experimental Assessment of Mammalian Lipidome Complexity Using Multimodal 21 T FTICR Mass Spectrometry Imaging

Herein, we assess the complementarity and complexity of data that can be detected within mammalian lipidome mass spectrometry imaging (MSI) via matrix-assisted laser desorption ionization (MALDI) and nanospray desorption electrospray ionization (nano-DESI). We do so by employing 21 T Fourier transform ion cyclotron resonance mass spectrometry (FTICR-MS) with absorption mode FT processing in both cases, allowing unmatched mass resolving power per unit time (≥613k at m/z 760, 1.536 s transients). While our results demonstrated that molecular coverage and dynamic range capabilities were greater in MALDI analysis, nano-DESI provided superior mass error, and all annotations for both modes had sub-ppm error. Taken together, these experiments highlight the coverage of 1676 lipids and serve as a functional guide for expected lipidome complexity within nano-DESI-MSI and MALDI-MSI. To further assess the lipidome complexity, mass splits (i.e., the difference in mass between neighboring peaks) within single pixels were collated across all pixels from each respective MSI experiment. The spatial localization of these mass splits was powerful in informing whether the observed mass splits were biological or artificial (e.g., matrix related). Mass splits down to 2.4 mDa were observed (i.e., sodium adduct ambiguity) in each experiment, and both modalities highlighted comparable degrees of lipidome complexity. Further, we highlight the persistence of certain mass splits (e.g., 8.9 mDa; double bond ambiguity) independent of ionization biases. Here, we also evaluate the need for ultrahigh mass resolving power for mass splits ≤4.6 mDa (potassium adduct ambiguity) at m/z > 1000, which may only be resolved by advanced FTICR-MS instrumentation.

21T-FTICR-MS↗
DOE OSTI2020

What can neutron stars reveal about the equation of state of dense matter?

Neutron stars are astrophysical objects of extremes, reaching the highest densities we can observe in the cosmos, and probing matter under conditions that cannot be recreated in terrestrial experiments. In August 2017, the first neutron-star merger has been observed, which provided compelling evidence that these events are an important site for r-process nucleosynthesis. Furthermore, the gravitational-wave signal of such events might shed light upon the nature of strongly interacting matter in the neutron-star core. To understand these remarkable events, reliable nuclear physics input is essential. In this contribution, I explain how to use chiral effective field theory and advanced many-body methods to provide a consistent and systematic approach to strongly inter- acting systems from nuclei to neutron stars with controlled theoretical uncertainties. I will discuss recent results for the equation of state relevant for the nuclear astrophysics of neutron stars and neutron-star mergers.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

5G Enabled Energy Innovation: Advanced Wireless Networks for Science

Digital wireless communication has become a foundational technology for the nation. The U.S. Department of Energy’s Office of Science (DOE-SC) is the Nation’s largest supporter of basic research in the physical sciences discovering new materials, designing advanced microelectronics, and understanding the physics of radio frequency signaling. The expanding national rollout of a new fifth-generation (5G) mobile network, coupled with the torrent of scientific data generated by next-generation devices such as battery-powered Internet of Things (IoT) sensors, has created an urgent need to enhance cutting-edge wireless technology. Breakthroughs in the deployment, integration, security, and operational range of wireless networking can provide new scientific capabilities for the next decade - from autonomous mobile instruments for scientific user facilities to intelligent sensors networks distributed over thousands of kilometers to study environmental processes. To realize this promise, however, we must continue to drive innovations in computing, artificial intelligence (AI), advanced materials, high-speed networking, and microelectronics. In March 2020, the DOE-SC convened a workshop to identify the potential opportunities and explore the scientific challenges of advanced wireless technologies.

42 ENGINEERING↗
DOE OSTI2025

Distinguishing isotropic and anisotropic signals for X-ray total scattering using machine learning

Understanding structure–property relationships is essential for advancing technologies based on thin films. X-ray pair distribution function (PDF) analysis can access relevant atomic structure details spanning local-, mid- and long-range structure. While X-ray PDF has been adapted for thin films on amorphous substrates, measurements on single-crystal substrates are necessary to accurately determine structure origins for some thin film materials, especially those for which the substrate changes the accessible structure and properties. However, when measuring films on single-crystal substrates, high-intensity anisotropic Bragg spots saturate 2D detector images, overshadowing the thin films' isotropic scattering signal. This renders previous data processing methods for films on amorphous substrates unsuitable for films on single-crystal substrates. To address this measurement need, we developed IsoDAT2D, an innovative data processing approach using unsupervised machine learning algorithms. The program combines dimensionality reduction and clustering algorithms to separate thin film and single-crystal substrate X-ray scattering signals. We use SimDAT2D , a program we developed to generate simulated thin film data, to validate IsoDAT2D . Here we also use IsoDAT2D to isolate X-ray total scattering signal from a thin film on a single-crystal substrate. The resulting PDF data are compared with similar data processed using previous methods, especially substrate subtraction for single-crystal and amorphous substrates. PDF data from IsoDAT2D -identified X-ray total scattering data are significantly better than from single-crystal substrate subtraction, but not as reliable as PDF data from amorphous substrate subtraction. With IsoDAT2D , there are new opportunities to expand PDF to a wider variety of thin films, including those on single-crystal substrates, with which new structure–property relationships can be elucidated to enable fundamental understanding and technological advances.

36 MATERIALS SCIENCE↗