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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 865 records · Page 48
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

FPGA-based tracking for the CMS Level-1 trigger using the tracklet algorithm

The high instantaneous luminosities expected following the upgrade of the Large Hadron Collider (LHC) to the High-Luminosity LHC (HL-LHC) pose major experimental challenges for the CMS experiment. A central component to allow efficient operation under these conditions is the reconstruction of charged particle trajectories and their inclusion in the hardware based trigger system. There are many challenges involved in achieving this: a large input data rate of about 20–40 Tb/s; processing a new batch of input data every 25 ns, each consisting of about 15,000 precise position measurements and rough transverse momentum measurements of particles (“stubs”); performing the pattern recognition on these stubs to find the trajectories; and producing the list of trajectory parameters within 4 µs. Here, this paper describes a proposed solution to this problem, specifically, it presents a novel approach to pattern recognition and charged particle trajectory reconstruction using an all-FPGA solution. The results of an end-to-end demonstrator system, based on Xilinx Virtex-7 FPGAs, that meets timing and performance requirements are presented along with a further improved, optimized version of the algorithm together with its corresponding expected performance.

47 OTHER INSTRUMENTATION↗
DOE OSTI2023

Radiation tolerance studies of the HV-mux GaNFETs for the HL-LHC ATLAS ITk Strip detector

For the High-Luminosity upgrade of the LHC, the current ATLAS inner detector will be replaced with a new silicon charged-particle tracker, the ITk, which consists of the ITk Pixel and the ITk Strip subdetector. The high voltage multiplexing (HV-Mux) GaNFETs are radiation-tolerant transistors that permit switching off high voltage to malfunctioning sensors on the ITk Strip modules. To ensure the reliability of the GaNFETs in the high radiation environment expected at the HL-LHC, a sample of the production batch was exposed to gamma radiation. Finally, the GaNFETs were characterized pre-irradiation and post-irradiation, and monitored during irradiation.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2023

Establishing the Quality Assurance programme for the strip sensor production of the ATLAS tracker upgrade including irradiation with neutrons, photons and protons to HL-LHC fluences

The successful pre-production delivery of strip sensors for the new Inner Tracker (ITk) for the upgraded ATLAS detector at the High Luminosity LHC (HL-LHC) at CERN was completed and based on their performance full production has commenced. The overall delivery period is anticipated to last 4 years to complete the approximately 22000 sensors required for the ITk. For Quality Assurance (QA), a number of test structures designed by the collaboration, along with a large area diode and miniature version of the main sensor, are produced in every wafer by the foundry Hamamatsu Photonics K.K (HPK). As well as Quality Control (QC) checks on every main sensor, samples of the QA pieces from each delivery batch are tested both before and after irradiation with results after exposure to neutrons, gammas or protons to fluences and doses corresponding to those anticipated after operation at the HL-LHC to roughly 1.5 times the ultimate integrated luminosity of 4000 fb -1 . In this paper the procedures are presented and the studies carried out to establish that the seven ITk QA Strip Sensor irradiation and test sites meet all the requirements to support this very extensive programme throughout the strip sensor production phase for the ITk project.

47 OTHER INSTRUMENTATION↗
DOE OSTI2024

A novel diamond-like carbon based photocathode for PICOSEC Micromegas detectors

The PICOSEC Micromegas (MM) detector is a precise timinggaseous detector based on a MM detector operating in a two-stageamplification mode and a Cherenkov radiator. Prototypes equippedwith cesium iodide (CsI) photocathodes have shown promising timeresolutions as precise as 24 picoseconds (ps) for Minimum IonizingParticles. However, due to the high hygroscopicity andsusceptibility to ion bombardment of the CsI photocathodes,alternative photocathode materials are needed to improve therobustness of PICOSEC MM. Diamond-like Carbon (DLC) film have beenintroduced as a novel robust photocathode material, which have shownpromising results. A batch of DLC photocathodes with differentthicknesses were produced and evaluated using ultraviolet light. Thequantum efficiency measurements indicate that the optimizedthickness of the DLC photocathode is approximately3 nm. Furthermore, DLC photocathodes show good resistance to ionbombardment in aging test compared to the CsI photocathode. Finally,a PICOSEC MM prototype equipped with DLC photocathodes was tested inmuon beams. A time resolution of around 42 ps with a detectionefficiency of 97% for 150 GeV/c muons were obtained. These resultsindicate the great potential of DLC as a photocathode for thePICOSEC MM detector.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2026

Cryogenic light detectors with thermal signal amplification for 0 νββ search experiments

As a step towards the realization of cryogenic-detector experiments to search for neutrinoless double-beta decay (such as CROSS, BINGO, and CUPID), we investigated a batch of 10 Ge light detectors (LDs) assisted by Neganov-Trofimov-Luke (NTL) signal amplification. Each LD was assembled with a large cubic light-emitting crystal (45 mm side) using the recently developed CROSS mechanical structure. The detector array was operated at milli-Kelvin temperatures in a pulse-tube cryostat at the Canfranc underground laboratory in Spain. We achieved good performance with scintillating bolometers from CROSS, made of Li 2 100 MoO 4 crystals and used as reference detectors of the setup, and with all LDs tested (except for a single device that encountered an electronics issue). No leakage current was observed for 8 LDs with an electrode bias up to 100 V. Operating the LDs at an 80 V electrode bias applied in parallel, we obtained a gain of around 9 in the signal-to-noise ratio of these devices, allowing us to achieve a baseline noise RMS of O(10 eV). Thanks to the strong current polarization of the temperature sensors, the time response of the devices was reduced to around half a millisecond in rise time. The achieved performance of the LDs was extrapolated via simulations of pile-up rejection capability for several configurations of the CUPID detector structure. Despite the sub-optimal noise conditions of the LDs (particularly at high frequencies), we demonstrated that the NTL technology provides a viable solution for background reduction in CUPID.

47 OTHER INSTRUMENTATION↗
DOE OSTI2021

Natural evolutionary strategies for variational quantum computation

Abstract Natural evolutionary strategies (NES) are a family of gradient-free black-box optimization algorithms. This study illustrates their use for the optimization of randomly initialized parameterized quantum circuits (PQCs) in the region of vanishing gradients. We show that using the NES gradient estimator the exponential decrease in variance can be alleviated. We implement two specific approaches, the exponential and separable NES, for parameter optimization of PQCs and compare them against standard gradient descent. We apply them to two different problems of ground state energy estimation using variational quantum eigensolver and state preparation with circuits of varying depth and length. We also introduce batch optimization for circuits with larger depth to extend the use of ES to a larger number of parameters. We achieve accuracy comparable to state-of-the-art optimization techniques in all the above cases with a lower number of circuit evaluations. Our empirical results indicate that one can use NES as a hybrid tool in tandem with other gradient-based methods for optimization of deep quantum circuits in regions with vanishing gradients.

Anand, Abhinav (ORCID:0000000280812310)↗
DOE OSTI2022

Real-time semantic segmentation on FPGAs for autonomous vehicles with hls4ml

In this paper, we investigate how field programmable gate arrays can serve as hardware accelerators for real-time semantic segmentation tasks relevant for autonomous driving. Considering compressed versions of the ENet convolutional neural network architecture, we demonstrate a fully-on-chip deployment with a latency of 4.9 ms per image, using less than 30% of the available resources on a Xilinx ZCU102 evaluation board. The latency is reduced to 3 ms per image when increasing the batch size to ten, corresponding to the use case where the autonomous vehicle receives inputs from multiple cameras simultaneously. We show, through aggressive filter reduction and heterogeneous quantization-aware training, and an optimized implementation of convolutional layers, that the power consumption and resource utilization can be significantly reduced while maintaining accuracy on the Cityscapes dataset.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2025

Feature learning and generalization in deep networks with orthogonal weights

Fully-connected deep neural networks with weights initialized from independent Gaussian distributions can be tuned to criticality, which prevents the exponential growth or decay of signals propagating through the network. However, such networks still exhibit fluctuations that grow linearly with the depth of the network, which may impair the training of networks with width comparable to depth. We show analytically that rectangular networks with tanh activations and weights initialized from the ensemble of orthogonal matrices have corresponding preactivation fluctuations which are independent of depth, to leading order in inverse width. Moreover, we demonstrate numerically that, at initialization, all correlators involving the neural tangent kernel (NTK) and its descendants at leading order in inverse width—which govern the evolution of observables during training—saturate at a depth of ~20, rather than growing without bound as in the case of Gaussian initializations. We speculate that this structure preserves finite-width feature learning while reducing overall noise, thus improving both generalization and training speed in deep networks with depth comparable to width. We provide some experimental justification by relating empirical measurements of the NTK to the superior performance of deep non-linear orthogonal networks trained under full-batch gradient descent on the MNIST and CIFAR-10 classification tasks.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Crystal generation using the fully differentiable pipeline and latent space optimization

We present a materials generation framework that couples a symmetry-conditioned variational autoencoder with a differentiable SO(3) power spectrum objective to steer candidates toward a specified local environment under the crystallographic constraints. In particular, we implement a fully differentiable pipeline that performs batch-wise optimization on both direct and latent crystallographic representations. Using the GPU acceleration, the implementation achieves about fivefold speed compared to our previous CPU workflow, while yielding comparable outcomes. In addition, we introduce the optimization strategy that alternatively performs optimization on the direct and latent crystal representations. This dual-level relaxation approach can effectively escape local minima defined by different objective gradients, thus increasing the success rate of generating complex structures satisfying the target local environments. This framework can be extended to systems consisting of multi-components and multi-environments, providing a scalable route to generate material structures with the target local environment.

conditional VAE↗
DOE OSTI2021

Demethylation or Sorption? The Fate of Methylmercury in the Presence of Manganese Dioxide

Manganese oxide (MnO 2 ) amendments to mercury (Hg)-contaminated sediment have been shown to decrease methylmercury (MeHg) concentrations in sediment porewater. In the absence of solid-phase MeHg measurements, it is unclear whether the decreased porewater concentrations are due to inhibition of methylation, adsorption of the MeHg to the MnO 2 , demethylation of MeHg catalyzed by the MnO 2 , or some combination of these mechanisms. We conducted controlled laboratory experiments to determine whether MeHg losses from solution in the presence of amorphous MnO 2 are due to sorption or demethylation. In this work, we quantified MeHg in the dissolved phase and solid phase and found that no demethylation occurred. We used batch and isotherm experiments to determine MeHg sorption efficiency to MnO 2 in a variety of solutions with varying ionic strength and dissolved organic matter (DOM) concentration. Isotherms over an initial concentration range of 5–500 ng/L of MeHg showed no saturation effects. Increasing ionic strength from 0.012 M to 0.1 M produced relatively minor decreases in MeHg sorption. Increasing DOM concentrations from 0.64 to 16.4 mg C/L yielded substantial decreases in MeHg sorption. Under the experimental conditions, MnO2 is a less efficient MeHg sorbent compared to other sorbent materials, such as Thiol-SAMMS®, activated carbon, and biochar.

42 ENGINEERING↗
DOE OSTI2021

RhizoVision Explorer: open-source software for root image analysis and measurement standardization

Abstract Roots are central to the function of natural and agricultural ecosystems by driving plant acquisition of soil resources and influencing the carbon cycle. Root characteristics like length, diameter and volume are critical to measure to understand plant and soil functions. RhizoVision Explorer is an open-source software designed to enable researchers interested in roots by providing an easy-to-use interface, fast image processing and reliable measurements. The default broken roots mode is intended for roots sampled from pots and soil cores, washed and typically scanned on a flatbed scanner, and provides measurements like length, diameter and volume. The optional whole root mode for complete root systems or root crowns provides additional measurements such as angles, root depth and convex hull. Both modes support providing measurements grouped by defined diameter ranges, the inclusion of multiple regions of interest and batch analysis. RhizoVision Explorer was successfully validated against ground truth data using a new copper wire image set. In comparison, the current reference software, the commercial WinRhizo™, drastically underestimated volume when wires of different diameters were in the same image. Additionally, measurements were compared with WinRhizo™ and IJ_Rhizo using a simulated root image set, showing general agreement in software measurements, except for root volume. Finally, scanned root image sets acquired in different labs for the crop, herbaceous and tree species were used to compare results from RhizoVision Explorer with WinRhizo™. The two software showed general agreement, except that WinRhizo™ substantially underestimated root volume relative to RhizoVision Explorer. In the current context of rapidly growing interest in root science, RhizoVision Explorer intends to become a reference software, improve the overall accuracy and replicability of root trait measurements and provide a foundation for collaborative improvement and reliable access to all.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

PRODeepSyn: predicting anticancer synergistic drug combinations by embedding cell lines with protein–protein interaction network

Abstract Although drug combinations in cancer treatment appear to be a promising therapeutic strategy with respect to monotherapy, it is arduous to discover new synergistic drug combinations due to the combinatorial explosion. Deep learning technology holds immense promise for better prediction of in vitro synergistic drug combinations for certain cell lines. In methods applying such technology, omics data are widely adopted to construct cell line features. However, biological network data are rarely considered yet, which is worthy of in-depth study. In this study, we propose a novel deep learning method, termed PRODeepSyn, for predicting anticancer synergistic drug combinations. By leveraging the Graph Convolutional Network, PRODeepSyn integrates the protein–protein interaction (PPI) network with omics data to construct low-dimensional dense embeddings for cell lines. PRODeepSyn then builds a deep neural network with the Batch Normalization mechanism to predict synergy scores using the cell line embeddings and drug features. PRODeepSyn achieves the lowest root mean square error of 15.08 and the highest Pearson correlation coefficient of 0.75, outperforming two deep learning methods and four machine learning methods. On the classification task, PRODeepSyn achieves an area under the receiver operator characteristics curve of 0.90, an area under the precision–recall curve of 0.63 and a Cohen’s Kappa of 0.53. In the ablation study, we find that using the multi-omics data and the integrated PPI network’s information both can improve the prediction results. Additionally, the case study demonstrates the consistency between PRODeepSyn and previous studies.

Wang, Xiaowen↗
DOE OSTI2025

NGPINT V3: a containerized orchestration Python software for discovery of next-generation protein–protein interactions

Abstract Summary Batch yeast two-hybrid (Y2H) assays, leveraged with next-generation sequencing, have afforded successful innovations for the analysis of protein–protein interactions. NGPINT is a Conda-based software designed to process the millions of raw sequencing reads resulting from Y2H–next-generation interaction screens. Over time, increasing compatibility and dependency issues have prevented clean NGPINT installation and operation. A system-wide update was essential to continue effective use with its companion software, Y2H-SCORES. We present NGPINT V3, a containerized implementation built with both Singularity and Docker, allowing accessibility across virtually any operating system and computing environment. Availability and implementation This update includes streamlined dependencies and container images hosted on Sylabs (https://cloud.sylabs.io/library/schuyler/ngpint/ngpint) and Dockerhub (https://hub.docker.com/r/schuylerds/ngpint), facilitating easier adoption and integration into high-throughput and cloud-computing workflows. Full instructions and software can be also found in the GitHub repository https://github.com/Wiselab2/NGPINT_V3 and Zenodo https://doi.org/10.5281/zenodo.15256036.

Biochemistry & Molecular Biology↗
DOE OSTI2026

Linear complexity

We present factorization and solution phases for a new linear complexity direct solver designed for concurrent batch operations on fine-grained parallel architectures, for matrices amenable to hierarchical representation. We focus on the strong-admissibility-based $\mathscr{H}^{2}$ format, where strong recursive skeletonization factorization compresses remote interactions. We build upon previous implementations of $\mathscr{H}^{2}$ matrix construction for efficient factorization and solution algorithm design, which are illustrated graphically in stepwise detail. The algorithms are ‘blackbox’ in the sense that the only inputs are the matrix and right-hand side, without analytical or geometrical information about the origin of the system. We demonstrate linear complexity scaling in both time and memory on four representative families of dense matrices up to one million in size. Parallel scaling up to 16 threads is enabled by a multi-level matrix graph coloring and avoidance of dynamic memory allocations thanks to prefix-sum memory management. An experimental backward error analysis is included. We break down the timings of different phases, identify phases that are memory-bandwidth limited, and discuss alternatives for phases that may be sensitive to the trend to employ lower precisions for performance.

Boukaram, Wajih↗
DOE OSTI2021

Determination of Seven Organic Impurities in FD&C Yellow No. 6 by Ultra-High-Performance Liquid Chromatography

The U.S. Food and Drug Administration (FDA) batch certifies FD&C Yellow No. 6 (Y6) to ensure that the color additive meets requirements published in the Code of Federal Regulations (CFR), including specifications for seven organic manufacturing impurities consisting of two intermediates, a reaction by-product, an impurity originating from an intermediate, and three subsidiary colors. An ultra-high-performance liquid chromatography (UHPLC) method was developed and validated for determining seven organic impurities in Y6. For comparison, the currently used high-performance liquid chromatography (HPLC) method was also validated. The new UHPLC method uses a 1.7 µm particle size biphenyl column with aqueous ammonium formate and methanol as eluants. Analytes are identified by comparing their retention times and UV-visible spectra to those of reference standards. Calibration is performed in the presence of the dye matrix and analyte levels are determined from their peak areas. UHPLC and HPLC validation studies obtained linear calibration curves and excellent values for limits of detection, limits of quantitation, recovery, repeatability, and intermediate precision for all analytes. Survey analyses of 30 samples using the UHPLC and HPLC methods yielded results that were consistent within experimental error; however, the UHPLC method provided more accurate results for two analytes that coelute using the HPLC method. The UHPLC method satisfies the accuracy and precision requisites for routine certification of Y6. The UHPLC method is faster and generates less waste than the HPLC method while delivering better sensitivity, separation, and accuracy.

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

Determining mating type and ploidy in Rhodotorula toruloides and its effect on growth on sugars from lignocellulosic biomass

Rhodotorula toruloides is being developed for the use in industrial biotechnology processes because of its favorable physiology. This includes its ability to produce and store large amounts of lipids in the form of intracellular lipid bodies. Nineteen strains were characterized for mating type, ploidy, robustness for growth, and accumulation of lipids on inhibitory switchgrass hydrolysate (SGH). Mating type was determined using a novel polymerase chain reaction (PCR)-based assay, which was validated using the classical microscopic test. Three of the strains were heterozygous for mating type (A1/A2). Ploidy analysis revealed a complex pattern. Two strains were triploid, eight haploid, and eight either diploid or aneuploid. Two of the A1/A2 strains were compared to their parents for growth on 75%v/v concentrated SGH. The A1/A2 strains were much more robust than the parental strains, which either did not grow or had extended lag times. The entire set was evaluated in 60%v/v SGH batch cultures for growth kinetics and biomass and lipid production. Lipid titers were 2.33–9.40 g/L with a median of 6.12 g/L, excluding the two strains that did not grow. Lipid yields were 0.032–0.131 (g/g) and lipid contents were 13.5–53.7% (g/g). Four strains had significantly higher lipid yields and contents. Finally, one of these strains, which had among the highest lipid yield in this study (0.131 ± 0.007 g/g), has not been previously described in the literature.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Sustainable production of 2,3,5,6-Tetramethylpyrazine at high titer in engineered Corynebacterium glutamicum

The industrial amino acid production workhorse, Corynebacterium glutamicum naturally produces low levels of 2,3,5,6-tetramethylpyrazine (TMP), a valuable flavor, fragrance, and commodity chemical. Here, we demonstrate TMP production (~0.8 g L –1 ) in C. glutamicum type strain ATCC13032 via overexpression of acetolactate synthase and/or α-acetolactate decarboxylase from Lactococcus lactis in CGXII minimal medium supplemented with 40 g L –1 glucose. This engineered strain also demonstrated growth and TMP production when the minimal medium was supplemented with up to 40% (v v –1 ) hydrolysates derived from ionic liquid-pretreated sorghum biomass. Further, a key objective was to take the fully engineered strain developed in this study and interrogate medium parameters that influence the production of TMP, a critical post-strain engineering optimization. Design of experiments in a high-throughput plate format identified glucose, urea, and their ratio as significant components affecting TMP production. These two components were further optimized using response surface methodology. In the optimized CGXII medium, the engineered strain could produce up to 3.56 g L –1 TMP (4-fold enhancement in titers and 2-fold enhancement in yield, mol mol –1 ) from 80 g L –1 glucose and 11.9 g L –1 urea in shake flask batch cultivation.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Corn stover variability drives differences in bisabolene production by engineered Rhodotorula toruloides

Microbial conversion of lignocellulosic biomass represents an alternative route for production of biofuels and bioproducts. While researchers have mostly focused on engineering strains such as Rhodotorula toruloides for better bisabolene production as a sustainable aviation fuel, less is known about the impact of the feedstock heterogeneity on bisabolene production. Critical material attributes like feedstock composition, nutritional content, and inhibitory compounds can all influence bioconversion. Further, the given feedstocks can have a marked influence on selection of suitable pretreatment and hydrolysis technologies, optimizing the fermentation conditions, and possibly even modifying the microorganism's metabolic pathways, to better utilize the available feedstock. Here, this work aimed to examine and understand how variations in corn stover batches, anatomical fractions, and storage conditions impact the efficiency of bisabolene production by R. toruloides. All of these represent different facets of feedstock heterogeneity. Deacetylation, mechanical refining, and enzymatic hydrolysis of these variable feedstocks served as the basis of this research. The resulting hydrolysates were converted to bisabolene via fermentation, a sustainable aviation fuel precursor, using an engineered R. toruloides strain. This study showed that different sources of feedstock heterogeneity can influence microbial growth and product titer in counterintuitive ways, as revealed through global analysis of protein expression. The maximum bisabolene produced by R. toruloides was on the stalk fraction of corn stover hydrolysate (8.89 ± 0.47 g/L). Further, proteomics analysis comparing the protein expression between the anatomic fractions showed that proteins relating to carbohydrate metabolism, energy production, and conversion as well as inorganic ion transport metabolism were either significantly upregulated or downregulated. Specifically, downregulation of proteins related to the iron–sulfur cluster in stalk fraction suggests a coordinated response by R. toruloides to maintain overall metabolic balance, and this was corroborated by the concentration of iron in the feedstocks.

09 BIOMASS FUELS↗