Results for “UC” — Page 8 | Engineering Papers

DOE OSTI2021

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

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

36 MATERIALS SCIENCE↗

DOE OSTI2021

Direct comparison of wire, foil, and hybrid X-pinches on a 200 kA, 150 ns current driver

Wire X-pinches (WXPs) have been studied comprehensively as fast (~1 ns pulse width), small (~1 μm) x-ray sources, created by twisting two or more fine wires into an “X” to produce a localized region of extreme magnetic pressure at the cross-point. Recently, two alternatives to the traditional WXP have arisen: the hybrid X-pinch (HXP), composed of two conical electrodes bridged by a thin wire or capillary, and the laser-cut foil X-pinch (LCXP), cut from a thin foil using a laser. We present a comparison of copper wire, hybrid, and laser-cut foil X-pinches on a single experimental platform: UC San Diego’s ~200 kA, 150 ns rise time GenASIS driver. All configurations produced 1–2 ns pulse width, ≤5 μm soft x-ray (Cu L-shell, ~1 keV) sources (resolutions diagnostically limited) with comparable fluxes. WXP results varied with linear mass and wire count, but consistently showed separate pinch and electron-beam-driven sources. LCXPs produced the brightest (~1 MW), smallest (≤5 μm) Cu K-shell sources, and spectroscopic data showed both H-like Cu K α lines indicative of source temperatures ≥2 keV, and cold K α (~8050 eV) characteristic of electron beam generated sources, which were not separately resolved on other diagnostics (within 1–2 ns and ≤200 μm). HXPs produced minimal K-shell emission and reliably single, bright, and small L-shell sources after modifications to shape the early current pulse through them. Benefits and drawbacks for each configuration are discussed to provide potential X-pinch users with the information required to choose the configuration best suited to their needs.

42 ENGINEERING↗

DOE OSTI2022

Thickness and temperature dependence of the atomic-scale structure of SrRuO 3 thin films

The temperature-dependent layer-resolved structure of 3 to 44 unit cell thick SrRuO 3 (SRO) films grown on Nb-doped SrTiO 3 substrates is investigated using a combination of high-resolution synchrotron x-ray diffraction and high-resolution electron microscopy to understand the role that structural distortions play in suppressing ferromagnetism in ultra-thin SRO films. The oxygen octahedral tilts and rotations and Sr displacements characteristic of the bulk orthorhombic phase are found to be strongly dependent on temperature, the film thickness, and the distance away from the film–substrate interface. For thicknesses, t, above the critical thickness for ferromagnetism (t > 3 uc), the orthorhombic distortions decrease with increasing temperature above T C . Below T C , the structure of the films remains constant due to the magneto-structural coupling observed in bulk SRO. The orthorhombic distortions are found to be suppressed in the 2–3 interfacial layers due to structural coupling with the SrTiO 3 substrate and correlate with the critical thickness for ferromagnetism in uncapped SRO films.

36 MATERIALS SCIENCE↗

DOE OSTI2026

Activation of methane by U + studied by guided ion beam tandem mass spectrometry and quantum chemistry

Reaction pathways of all products formed in the U + + CH 4 (CD 4 ) reaction were explored as a function of kinetic energy using guided ion beam tandem mass spectrometry and quantum chemical calculations. UH + , UC + , UCH + , UCH 2 + , and UCH 3 + (and their perdeuterated analogues) are formed in endothermic reactions. In both systems, the UCH 2 + (UCD 2 + ) dehydrogenated product was the dominant product in the low-energy region, whereas the UH + (UD + ) hydride product became predominant at high energies. The kinetic energy behavior of the various products is consistent with a common intermediate of H–U + –CH 3 (D–U + –CD 3 ). Here, the kinetic energy dependence of all product cross sections was modeled to obtain experimental bond dissociation energies at 0 K (in eV): D 0 (U + –H) = 2.42 ± 0.10, D 0 (U + –C) = 3.95 ± 0.12, D 0 (U + –CH) = 4.91 ± 0.09, D 0 (U + –CH 2 ) = 4.11 ± 0.04, and D 0 (U + –CH 3 ) = 2.41 ± 0.09. Quantum chemical calculations using the UCCSD(T) and UB3LYP approaches with the cc-pwCVXZ-PP basis set with MDF-60 pseudopotential for U + and the aug-cc-pCVXZ and aug-cc-pVXZ (X = T, Q) basis set for carbon and hydrogen, respectively, validate the experimental bond dissociation energies and outline the potential energy surface for all reactions observed. In addition, spin–orbit corrections of the bond energies for all products were calculated at a CASSCF-CASPT2-RASSI level.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2023

Preliminary Conceptual Design of Nuclear Thermal Rocket Reactor Cores Using Ceramic Fuels with Beryllium or Composite Neutron Moderators

Herein several preliminary conceptual designs of nuclear thermal rocket reactor cores are presented that use tin-bonded monolithic ceramic [mononitride (UN), monocarbide (UC), and uranium dioxide (UO 2 )] fuel plates or pins with molybdenum-tungsten alloy clad. Neutron moderation is provided by a block of Be metal or composite materials using metal hydrides such as ZrH 1.6 or YH 1.6 with different matrices (MgO or Be). Mainly high-assay low-enriched uranium is considered, but highly enriched uranium is also assessed for a few configurations. Nominal core thermal power is 300 MW corresponding to about 66 kN (15 klbf) of thrust, and with minimal modifications, 500 MW may be possible (25 klbf of thrust). Depending on the configurations, the amount of 235 U needed for criticality is 30 to 90 kg, and reactor weight is 2.5 to 3.8 tonnes.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2024

A first look at AS-ROC: a Si-Ge integrated chip readout for fast timing

Abstract Advances in timing detector technology require new specialized readout electronics. Applications demand below 10 ps time of arrival resolution and low power for a low repetition rate. A possible path to achieve O(10 ps) time resolution is an integrated chip using Silicon Germanium (SiGe) technology. Using DoE SBIR funding, Anadyne, Inc., in collaboration with UC Santa Cruz, has developed a prototype SiGe front-end readout chip optimized for low power and timing resolution. Two versions of the chip were produced with performance in simulation: a more power version with 10 ps resolution at 5 fC with 1.1 mW/channel, and a less power version with 10 ps resolution at 8 fC with 0.6 mW/channel.The chip was produced at Tower Semiconductor with 350 nm technology. The ASIC from the prototype run shows good performance: a rise time of 0.7–1 ns and 25 mV per fC response with RMS noise <1 mV. Simulation and results from the prototype will be reported in this paper.

Instruments & Instrumentation↗

DOE OSTI2024

Improved Evaluation of Large Network Matrices for Linear Power Flow Within Optimization Problems

This work presents methods for evaluating the Power Transfer Distribution Factor (PTDF) and Line Outage Distribution Factor (LODF) matrices by employing sparse linear algebra for large-scale computing applications. These matrices play a critical role in many power system applications, such as the Unit Commitment Problem (UC), pre- and post-contingency power flow analysis, and transmission expansion. These matrices are typically dense, which means they require a significant amount of time and memory to be computed for large networks. However, by analyzing the structure of the matrices and their computation method, it is possible to use reduced memory methods based on sparse matrix operations. This paper shows that sparse linear algebra algorithms are faster and require less memory and time than traditional dense approaches. Additionally, we explore the effect of matrix sparsification by eliminating trailing digits on power flow calculations.

large scale↗

DOE OSTI2024

Study of Shock Formation Parameters With Drive Conditions in Magnetically Accelerated Plasma Flows

We present experimental data regarding the formation of high-energy-density shocks in magnetically accelerated plasma flows using pulsed power drivers. We quantify the flow velocity and temperature of the ablated plasma using optical Thomson scattering and gated emission imaging across two different generators. We show that, regardless of the drive parameters, the plasma flows show continuous acceleration over centimeter spatial scales, in line with trends in published simulation work. When stationary targets are placed in these supersonic flows, bow-shock formation is observed at all drive parameters in a range of materials. In the higher density flow generated on the 1-MA COBRA generator at Cornell University, heating of the upstream flow ahead of the shock is observed and quantified, which is not observed at the lower density flow on the 0.2-MA Bertha driver at UC San Diego. Here, when combined with previous work on the XP generator at Cornell, we can show that these three experimental setups allow control of the effect of radiation loss and upstream absorption on the formation of the bow shock.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2026

Design Rules for Carboborothermic Reduction Synthesis of High Uranium Density UB 4 –UBC Composites

Uranium borides are promising candidate fuel forms for use in advanced nuclear reactors due to their high thermal conductivity and potential for dual use as both fuel and burnable absorber. In this work, uranium tetraboride () and uranium monoboroncarbide (UBC) composite were synthesized by using industrially scalable carboborothermic reduction method. The final uranium boride phase composition is sensitive to the sample holding crucibles ( and graphite) such that graphite supply excess carbon, promoting the formation of a predominant UBC phase. The high‐temperature in situ synchrotron X‐ray diffraction of pristine –UBC show persistence , UBC, and phases while preoxidized –UBC leads to predominant and formation due to progressive oxidation and boron loss at high temperature. The oxidation behavior was further characterized using thermogravimetric analysis, allowing direct comparison with other potential accident tolerant fuels such as , , UC, and UN. The –UBC shows higher uranium loading than monolithic and demonstrates promising oxidation behavior at high temperature, pointing to its potential as an improved uranium boride‐based fuel form.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2020

slaclab/ucsc-hn

UC Santa Cruz Head and Neck.

Herbst, Ryan↗

DOE OSTI2023

Few-shot leaf segmentation

In this work, we use few-shot learning to segment the body and vein architecture of Populus trichocarpa leaves from high-resolution scans obtained in the UC Davis common garden. Leaf and vein segmentation are formulated as separate tasks, in which convolutional neural networks (CNNs) are used to iteratively expand partial segmentations until reaching stopping criteria. Our leaf and vein segmentation approaches use just 50 and 8 manually traced images for training, respectively, and are applied to a set of 2,634 top and bottom leaf scans. We show that both methods achieve high segmentation accuracy, in some cases exceeding even human-level segmentation. The leaf and vein segmentations are subsequently used to extract 68 morphological traits using traditional open-source image processing tools, which are validated using real-world physical measurements. For a biological perspective, we perform a genome-wide association study using the "vein density" trait to discover novel genetic architectures associated with multiple physiological processes relating to leaf development and function. In addition to sharing all of the few-shot learning code, we are releasing all images, manual segmentations, model predictions, 68 extracted leaf phenotypes, and a new set of SNPs called against the v4 P. trichocarpa genome for 1,419 genotypes.

Lagergren, John [Oak Ridge National Lab. (ORNL), O↗

DOE OSTI2025

QUANT-NET Control Plane Framework (QNCP) v1.0.0

The QUANT-NET Control Plane (QNCP) provides a software framework for expressing and managing quantum network resources. It may be used to orchestrate a physical quantum testbed with real device driver implementations, or it may be used as a proving ground when developing new protocols and management functions. In practice, both approaches may be useful when undertaking research and development in emerging quantum testbeds. While a number of control systems have been developed for specific quantum platform demonstrations, an openly available and general solution for operating quantum networks has not emerged. QNCP is designed to fill this gap. The framework has been designed to provide extensible, modular capabilities that include scheduling, routing, monitoring, and pluggable protocols. A number of reference implementations in each module category have been included in the installable packages; however, the intent is that each of these modules may be extended or re-implemented to meet the needs of the particular deployment or research need. The software is currently being used in the QUANT-NET testbed project, which spans resources between LBNL and UC Berkeley Physics.

Zhang, Liang [Lawrence Berkeley National Laborator↗

DOE OSTI2026

Quantum Stochastic Programming [SWR-26-040]

The Quantum Stochastic Programming tool contains quantum computing algorithms for two-stage stochastic optimization, with a focus on the Unit Commitment (UC) problem in power systems. The algorithms combine Discrete Quantum Annealing (DQA) with Quantum Amplitude Estimation (QAE) to compute expected-value objective functions over a probability distribution of wind-power scenarios. Based on: arXiv 2402.15029 - "Quantum algorithms for the two-stage stochastic unit commitment problem"

Maack, Jonathan [National Laboratory of the Rockie↗

DOE OSTI2023

Supporting information for Few-Shot Learning Enables Population-Scale Analysis of Leaf Traits in Populus trichocarpa

In this work, we use few-shot learning to segment the body and vein architecture of P. trichocarpa leaves from high-resolution scans obtained in the UC Davis common garden. Leaf and vein segmentation are formulated as separate tasks, in which convolutional neural networks (CNNs) are used to iteratively expand partial segmentations until reaching stopping criteria. Our leaf and vein segmentation approaches use just 50 and 8 manually traced images for training, respectively, and are applied to a set of 2,634 top and bottom leaf scans. We show that both methods achieve high segmentation accuracy, in some cases exceeding even human-level segmentation. The leaf and vein segmentations are subsequently used to extract 68 morphological traits using traditional open-source image processing tools, which are validated using real-world physical measurements. For a biological perspective, we perform a genome-wide association study using the vein density trait to discover novel genetic architectures associated with multiple physiological processes relating to leaf development and function. In addition to sharing all of the few-shot learning code (see https://github.com/jlager/few-shot-leaf-segmentation), we are releasing all images, manual segmentations, model predictions, 68 extracted leaf phenotypes, and a new set of SNPs called against the v4 P. trichocarpa genome for 1,419 genotypes. The data folder includes all images, ground truth segmentations, predicted segmentations, and extracted leaf traits. All images encode the sample ID in the file name by indicating the treatment, block, row, position, and leaf side, respectively. For example, the file, C_1_1_2_bot.jpeg, indicates the control treatment, block 1, row 1, position 2, and the bottom side of the leaf. Tabulated results include position IDs as well as the corresponding genotype IDs. The images folder includes the 2,906 high-resolution leaf scans taken in the field. The leaf_masks folder includes 50 ground truth segmentations used for training the leaf tracing algorithm. The leaf_preds folder includes the 2,906 predicted segmentations from the leaf tracing algorithm. The vein_masks folder includes 8 ground truth segmentations used for training the vein growing algorithm. The vein_preds folder includes the 1,453 predicted segmentations from the vein growing algorithm. The vein_probs folder includes the 1,453 predicted probability maps from the vein growing algorithm before thresholding. The genomes folder includes the set of SNPs called against the v4 P. trichocarpa genome for 1,419 genotypes with a README file detailing the steps taken. The results folder includes: (i) raw values of the 68 predicted leaf traits in digital_traits.tsv, (ii) manually measured values of petiole length and width in manual_traits.tsv, (iii) thin plate spline (TPS) adjusted values of the vein density trait in vein_density_tps_adj.tsv, (iv) best linear unbiased prediction (BLUP) adjusted values of the vein density trait in vein_density_blups.tsv, and (v) GWAS results for the vein density trait, including chromosome positions and corresponding P values, in gwas_results.csv.

09 BIOMASS FUELS↗

DOE OSTI2025

High resolution characterization of soil dissolved organic matter with FTICR-MS (Fourier-transform ion cyclotron resonance mass spectrometry) from soil samples in control and warming plots in Blodgett Forest, CA (2014 and 2018)

The pathways of carbon transport and loss through and from soils—soil organic matter (SOM) depolymerization to dissolved organic carbon and mineralization to carbon dioxide (CO2)—are fundamentally driven by microbial activity, which is strongly regulated by environmental conditions. As part of Lawrence Berkeley National Laboratory (LBNL) Terrestrial Ecosystem Science (TES) Belowground Biogeochemistry Science Focus Area (SFA), we have established a novel whole-soil long-term warming experiment at the University of California (UC) Blodgett Forest Research Station (Sierra Nevada) in 2014, where we study the role of biogeochemical, microbial and geochemical process interactions in SOM decomposition and stabilization.This package contains Fourier transform ion cyclotron resonance mass spectrometry (21 Tesla FTICR-MS) data measured in negative and positive ionization mode from water and methanol soil extracts. Soil samples were collected in 2014/06/03 and 2018/06/04 from 3 replicated paired plots that had been subjected to experimental warming since June 2014 to simulate a predicted climate change scenario for northern California. The following files are included: (1) fticr_neg_h2oMeoh_data_raw.csv: raw data from combined water (H2O) and methanol (MeOH) extracts in negative ion mode, (2) fticr_neg_h2oMeoh_data_processed.csv: processed data from combined water (H2O) and methanol (MeOH) extracts in negative ion mode, (3) fticr_neg_metadata.csv: metadata for samples/measurements in negative ion mode, (4) fticr_pos_h2oMeoh_data_raw.csv: raw data from combined water (H2O) and methanol (MeOH) extracts in positive ion mode, (5) fticr_pos_h2oMeoh_data_processed.csv: processed data from combined water (H2O) and methanol (MeOH) extracts in positive ion mode, (6) fticr_pos_metadata.csv: metadata for samples/measurements in positive ion mode.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

Metagenome-assembled genomes from soil samples in control and warming plots in Blodgett Forest, CA (2014-2021)

The pathways of carbon transport and loss through and from soils—soil organic matter (SOM) depolymerization to dissolved organic carbon and mineralization to carbon dioxide (CO2)—are fundamentally driven by microbial activity, which is strongly regulated by environmental conditions. As part of LBNL (Lawrence Berkeley National Laboratory) TES (Terrestrial Ecosystem Science) Belowground Biogeochemistry Science Focus Area (SFA), we have established a novel whole-soil long-term warming experiment at the University of California (UC) Blodgett Forest Research Station (Sierra Nevada) in 2014, where we study the role of biogeochemical, microbial and geochemical process interactions in SOM decomposition and stabilization.Here, we present metagenome-assembled genomes (MAGs) for the bacterial and archaeal community from soil depth profiles collected from 2014 to 2021 from three paired control and warming plots. We collected soil samples across a range of depth profiles (spanning surface to 90 cm deep) from three paired control and warming plots from a temperate mixed forest in Northern California. Each paired plot had been subjected to experimental warming since June 2014 to simulate a predicted climate change scenario for northern California. 101 soil metagenomes were sequenced at JGI (Joint Genome Institute) and UCSF (University of California San Francisco) Center for Advanced Technology and can be found under the JGI (Joint Genome Institute) GOLD (Genomes Online Database) Sequencing project Gs0151586 and NCBI (National Center for Biotechnology Information) Projects PRJNA1225762 and PRJEB39497. Metagenomes were assembled using JGI (Joint Genome Institute) Metagenome Workflow (10.1128/mSystems.00804-20). For each metagenome, the assembled contigs were binned into genomes using 3 binning algorithms (cocacola, metabat, and maxbin) and the resulting bins were consolidated using dastool. The consolidated bins from all metagenomes were pooled, filtered by completeness (>50%) and contamination (<25%), and dereplicated at 99% ANI (average nucleotide identity) using dRep (https://github.com/MrOlm/drep).The dataset includes a zip file of 2321 MAG (Metagenome Assembled Genome) fasta files, the accession numbers for the underlying metagenomes, and a csv file with MAG (Metagenome Assembled Genome) quality metrics and taxonomic classification (GTDB -Genome Taxonomy Database-RS220). This dataset also includes a file-level metadata (flmd.csv) file that lists each file contained in the dataset with associated metadata and a data dictionary (dd.csv) file that contains column/row headers used throughout the files along with a definition, units, and data type. A sample metadata file (samples.csv) that contains site information has also been included.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2026

Untargeted, tandem mass spectrometry (LC/MS-MS) metaproteomes from soil samples in control and warming plots in Blodgett Forest, CA (2014-2021)

The pathways of carbon transport and loss through and from soils—soil organic matter (SOM) depolymerization to dissolved organic carbon and mineralization to carbon dioxide (CO2)—are fundamentally driven by microbial activity, which is strongly regulated by environmental conditions. As part of Lawrence Berkeley National Laboratory (LBNL) Terrestrial Ecosystem Science (TES) Belowground Biogeochemistry Science Focus Area (SFA), we have established a novel whole-soil long-term warming experiment at the University of California (UC) Blodgett Forest Research Station (Sierra Nevada) in 2014, where we study the role of biogeochemical, microbial and geochemical process interactions in SOM decomposition and stabilization. This package contains soil metaproteomics data in the context of site specific metagenomes from soil depth profiles in three paired control and warming plots from a temperate mixed forest in Northern California. Each paired plot had been subjected to experimental warming since June 2014 to simulate a predicted climate change scenario for northern California. These metaproteomes were collected in 2018 after 4.5 years of warming from five depth intervals (0-10 cm, 10-30 cm, 30-45 cm, 45-60 cm, 60-80 cm). For protein identification, the collected spectra were searched following a target-decoy search strategy against a database of metagenome predicted proteins (covering 96 samples from 2014 to 2021) representing the complete sequence diversity at the site. Data was searched with mass spectrometry database search tool (MS-GF+) using Pacific Northwest National Laboratory (PNNL)'s Data Management System (DMS) Processing pipeline. The metagenomes are published as part of another data package. Raw metaproteomic data and the data products from MS-GF+ are deposited in the Mass Spectrometry Interactive Virtual Environment (MassIVE) database under accession no. MSV000097826. Here we present a dataset that includes spectral counts for the detected proteins across samples (EMSL50964_BrodieAllMAGs_Globals_SC.txt), the sequences of the detected proteins, and sample metadata file that contains site information for the soil metaproteome samples.

Belowground Biogeochemistry Science Focus Area↗

DOE OSTI2026

1H-NMR characterization of soil dissolved organic matter from soil samples in control and warming plots in Blodgett Forest, CA (2014 and 2018)

The pathways of carbon transport and loss through and from soils—soil organic matter (SOM) depolymerization to dissolved organic carbon and mineralization to carbon dioxide (CO2)—are fundamentally driven by microbial activity, which is strongly regulated by environmental conditions. As part of Lawrence Berkeley National Laboratory Terrestrial Ecosystem Science Belowground Biogeochemistry Science Focus Area (SFA), we have established a novel whole-soil long-term warming experiment at the University of California (UC) Blodgett Forest Research Station (Sierra Nevada) in 2014, where we study the role of biogeochemical, microbial and geochemical process interactions in SOM (soil organic matter) decomposition and stabilization. This package contains metabolite data obtained through 1H nuclear magnetic resonance (NMR) spectroscopy on water-extracted soils. Soil samples were collected in 2014/06/03 and 2018/06/04 from 3 replicated paired plots that had been subjected to experimental warming since June 2014 to simulate a predicted climate change scenario for northern California. The following files are included: (1) nmr_h2o_data_raw.csv: raw data, (2) nmr_h2o_data_processed.csv: computed compound concentrations and metadata, (3) nmr_h2o_compound_metadata.csv: compound metadata, (4) nmr_h2o_sample_metadata.csv: sample metadata

1H-NMR (nucleic magnetic resonance) spectroscopy↗