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All sources NASA NTRS DOE OSTI

DOE OSTI2025

Data for Discovery, Characterization, and Application of Chromosomal Integration Sites for Stable Heterologous Gene Expression in Rhodotorula toruloides

Rhodotorula toruloides is a non-model, oleaginous yeast uniquely suited to produce acetyl-CoA-derived chemicals. However, the lack of well-characterized genomic integration sites has impeded the metabolic engineering of this organism. Here we report a set of computationally predicted and experimentally validated chromosomal integration sites in R. toruloides . We first implemented an in silico platform by integrating essential gene information and transcriptomic data to identify candidate sites that meet stringent criteria. We then conducted a full experimental characterization of these sites, assessing integration efficiency, gene expression levels, impact on cell growth, and long-term expression stability. Among the identified sites, 12 exhibited integration efficiencies of 50% or higher, making them sufficient for most metabolic engineering applications. Using selected high-efficiency sites, we achieved simultaneous double and triple integrations and efficiently integrated long functional pathways (up to 14.7 kb). Additionally, we developed a new inducible marker recycling system that allows multiple rounds of integration at our characterized sites. We validated this system by performing five sequential rounds of GFP integration and three sequential rounds of MaFAR integration for fatty alcohol production, demonstrating, for the first time, precise gene copy number tuning in R. toruloides . These characterized integration sites should significantly advance metabolic engineering efforts and future genetic tool development in R. toruloides .

Conversion↗

DOE OSTI2012

Python Materials Genomics (pymatgen): A robust, open-source python library for materials analysis

We present the Python Materials Genomics (pymatgen) library, a robust, open-source Python library for materials analysis. A key enabler in high-throughput computational materials science efforts is a robust set of software tools to perform initial setup for the calculations (e.g., generation of structures and necessary input files) and post-calculation analysis to derive useful material properties from raw calculated data. The pymatgen library aims to meet these needs by (1) defining core Python objects for materials data representation, (2) providing a well-tested set of structure and thermodynamic analyses relevant to many applications, and (3) establishing an open platform for researchers to collaboratively develop sophisticated analyses of materials data obtained both from first principles calculations and experiments. The pymatgen library also provides convenient tools to obtain useful materials data via the Materials Project's REpresentational State Transfer (REST) Application Programming Interface (API). As an example, using pymatgen's interface to the Materials Project's RESTful API and phasediagram package, we demonstrate how the phase and electrochemical stability of a recently synthesized material, Li 4 SnS 4 , can be analyzed using a minimum of computing resources. Here, we find that Li 4 SnS 4 is a stable phase in the Li-Sn-S phase diagram (consistent with the fact that it can be synthesized), but the narrow range of lithium chemical potentials for which it is predicted to be stable would suggest that it is not intrinsically stable against typical electrodes used in lithium-ion batteries.

36 MATERIALS SCIENCE↗

DOE OSTI2024

Time-resolved X-ray diffraction diagnostic development for the National Ignition Facility

Here we present the development of an experimental platform that can collect four frames of x-ray diffraction data along a single line of sight during laser-driven, dynamic-compression experiments at the National Ignition Facility. The platform is comprised of a diagnostic imager built around ultrafast sensors with a 2-ns integration time, a custom target assembly that serves also to shield the imager, and a 10-ns duration, quasi-monochromatic x-ray source produced by laser-generated plasma. We demonstrate the performance with diffraction data for Pb ramp compressed to 150 GPa and illuminated by a Ge x-ray source that produces ∼7 × 10 11 , 10.25-keV photons/ns at the 400 μm diameter sample.

36 MATERIALS SCIENCE↗

DOE OSTI2024

Multi-Scale Integrated Monitoring System for Enhancing Methane Emission Detection, Quantification & Prediction

This report details the progress and findings of a comprehensive study on reviewing existing solutions, identifying technology gaps, and formulating an “all-in-one” integrated strategy for developing the next-generation multiscale methane monitoring and modeling platform, conducted under grant number DE-FE0032292. Co-led by Dr. David Ebert, Dr. Binbin Weng, and Dr. Chenghao Wang at the University of Oklahoma, the project’s goal was to develop an integrated approach for building this engineering platform to detect, quantify, and mitigate methane emissions across various temporal scale, spatial scales, and sectors. The planning grant study began with an extensive review of various methane sensing and monitoring technologies and systems, surveying over 100 technology providers globally. This review revealed the prevalence of optical methods over chemical methods in commercially available sensors, with Non-Dispersive Infrared (NDIR), Tunable Diode Laser Absorption Spectroscopy (TDLAS), and Optical Gas Imaging (OGI) cameras being the most prevalent options. A trend towards more advanced optical techniques was observed, driven by increased regulatory focus and technological advancements. The technical evaluation of these sensing technologies provided crucial insights into their capabilities and limitations. The study examined emerging technologies such as Differential Absorption LiDAR (DIAL), which show promise for high-precision and long-range detection. The team then investigated the features and application bandwidth of various sensing platforms, including handheld, fixed/stationary, mobile, aerials, and spaceborne monitors. Pilot field studies were conducted to assess the capabilities of solutions for different emission scenarios. Field work with sensor deployments was conducted at three distinct site types: an oil & gas industry site, a cattle ranching operation, and a waste processing facility. The team also conducted a thorough review of methane flux inverse modeling approaches, focused on physically based methods. These approaches were categorized into simple, intermediate, and advanced methods. A realtime WRF-GHG (Weather Research and Forecasting-Greenhouse Gas) modeling system was developed and applied, incorporating multiple data sources to guide field experiments and inform methane plume detection. The project identified and analyzed numerous categories of methane data sources, including satellite measurements, ground-based sensors, and inventory databases. Key platforms examined include EDGAR, EPA GHGI, NASA TROPOMI, Carbon Mapper, and Climate TRACE, among others. The team proposed an architecture for a comprehensive methane monitoring platform. This system incorporates multi-source data acquisition, advanced data processing and assimilation, interactive visualization tools, and analytical capabilities for emissions forecasting and scenario analysis. The proposed platform aims to provide a user-friendly interface catering to various stakeholders, from researchers to policymakers. The architecture includes sophisticated data ingestion methods, a centralized data warehouse, and advanced analytical tools for data fusion and interpretation. To ensure the relevance and effectiveness of the proposed system, a comprehensive survey was conducted to gather stakeholder input on system requirements. Key findings include a strong need for integrating various data types and formats, a preference for real-time data updates and advanced visualization tools, and a demand for user-friendly interfaces catering to different expertise levels.

03 NATURAL GAS↗

DOE OSTI2018

Attribution of gamma-ray background collected by a mobile detector system to its surroundings using panoramic video

Mobile radiation detector systems are important tools for detecting radiological and nuclear sources outside of regulatory control, but due to their mobility, they are subject to complex and varying backgrounds in most realistic operational scenarios. Recent work has found correlations between non-radiological contextual information and gamma-ray spectral features that can be used to decrease false alarm rates, however a more complete understanding of background source terms has been elusive. A measurement campaign developed a full radiological characterization of a controlled facility that roughly corresponded to two city blocks. As part of the campaign, the Radiological Multi-sensor Analysis Platform (RadMAP) collected extensive multi-sensor data. In this work, RadMAP’s panoramic video data were used to visually identify several different materials and quantify the detector response to each material. A linear model was fit between the material responses and radiological features, and the results demonstrate reasonable agreement with ground truth flux measurements for each material. This paper will describe the data collection, processing, and analysis of the gamma-ray and video data from RadMAP. The paper will conclude with perspectives on the applicability of such a method to less controlled environments both with respect to achieving better understanding the sources of variability of background radiation in urban environments and whether such methods could be leveraged in operational scenarios.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2022

Validation of CFD simulations of the moored DeepCwind offshore wind semisubmersible in irregular waves

This article examines the use of computational fluid dynamics (CFD) simulations to predict the response of a floating wind platform to irregular-wave excitation. This work was conducted as part of the Reproducible CFD Joint Industry Project for Floating Offshore Wind Applications and involved verification across several participants and modeling tools and validation against experiments. The authors pay special attention to the uncertainties in both CFD results and experimental measurements. In this work, we perform detailed comparisons of the incident waves and the motion of a moored structure. The nonlinear, low-frequency resonance motion is of particular interest because it potentially drives the mooring and tower-base loads. The verification and validation study is partially successful in that the CFD simulations capture the low-frequency slow-drift motion well but underpredict the low-frequency pitch resonance. This underprediction can be attributed in part to the CFD incident waves, which showed some discrepancies with the experimental waves, especially around extreme events. The effects of the wave discrepancies are also estimated and investigated using a mid-fidelity OpenFAST model. Overall, the present study increases our confidence in using CFD simulations to predict the global performance of offshore wind platforms in irregular waves and to produce data for the calibration of lower-fidelity models.

17 WIND ENERGY↗

DOE OSTI2023

Mapping foliar photosynthetic capacity in sub-tropical and tropical forests with UAS-based imaging spectroscopy: Scaling from leaf to canopy

Accurate understanding of the variability in foliar physiological traits across landscapes is critical to improve parameterization and evaluation of terrestrial biosphere models (TBMs) that seek to represent the response of terrestrial ecosystems to a changing climate. Numerous studies suggest imaging spectroscopy can characterize foliar biochemical and morphological traits at the canopy scale, but there is only limited evidence for retrieving canopy photosynthetic capacity (e.g., maximum carboxylation rate, V c,max and maximum electron transport rate, J max ). Moreover, the effect of canopy structure within forest communities on scaling up spectra-trait relationships from leaf to canopy level is not well known. To advance the spectra-trait approach and enable the estimation of key traits using remote sensing, we collected imaging spectroscopy data from an Unoccupied Aerial System (UAS) platform over two forest sites in China (a subtropical forest in Mt. Dinghu and a tropical rainforest in Xishuangbanna). At these sites, we also collected ground measurements of leaf spectra and traits, including biochemical (leaf nitrogen, phosphorus, chlorophyll, and water content), morphological (leaf mass per area, LMA) and physiological (V c,max25 and J max25 ) traits (n=135 tree-crowns from 42 species across two sites). Using a partial least-squares regression (PLSR) approach, we built and tested spectra-trait models with repeated cross-validation. The spectral models developed with leaf spectra were directly transferred to canopy spectra to evaluate the effect of canopy structure. Here we further applied canopy spectral models to map these traits at individual tree-crown scale. The results demonstrate that (1) UAS-based canopy spectra can be used to estimate V c,max (R 2 =0.55, nRMSE=11.79%), Jmax (R 2 =0.54, nRMSE=12.34%), and five additional foliar traits (R 2 =0.38-0.60, nRMSE=10.11-13.56%) at the tree-crown scale with demonstrated generalizability across two sites; (2) canopy structure strongly affects the spectratrait relationships from leaf to canopy level, but the effects vary considerably across foliar traits and cannot be well captured by the 4SAIL canopy radiative transfer model. UAS-based imaging spectroscopy maps large variability in all foliar traits (including physiological traits) with spatially explicit information, reproducing the field-observed inter- and intra-specific variations. These results demonstrate the capability of using UAS-based imaging spectroscopy for characterizing the variability of foliar physiological traits at individual tree-crown scale over forest landscapes and highlight the similar generalizability but different biophysical mechanisms underlying spectra-trait relationships at leaf and canopy levels.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2017

Application of Surface Tension Model for Prediction of Interfacial Speciation of CO 2 -Loaded Aqueous Solutions of Monoethanolamine

A thermodynamic platform in combination with experimental surface tension data is used to predict the surface phase speciation of CO 2 -loaded aqueous solutions of 30 weight percent (%) monoethanolamine (MEA) at 20, 40, and 60 °C from their corresponding bulk phase speciation. The osmotic coefficient dependency of the surface tension of an electrolyte solution has been employed to estimate the surface tension. In order to calculate the osmotic coefficients, the Pitzer equation for the excess Gibbs energy was applied. According to the results of this study, molecular MEA, and to some extent protonated MEA and the MEA-carbamate have surface propensity and will accumulate at the surface phase. The carbonate ion shows nearly even distribution between bulk and surface phases, and bicarbonate displays a tendency to remain in the bulk phase that increases with CO 2 loading. The differences in the behaviors of protonated MEA, MEA-carbamate, carbonate and bicarbonate are due to differences among their sizes and polarizabilities. Using first principles calculations, the static isotropic dipole polarizabilities for bicarbonate, carbonate, MEA carbamate and protonated MEA were calculated. The bulk and surface phases’ ionic strengths reasonably represent this behavior of ionic species at the surface phase.

03 NATURAL GAS↗

DOE OSTI2025

Predicting Pulsed-Laser Deposition SrTiO 3 Homoepitaxy Growth Dynamics Using High-Speed Reflection High-Energy Electron Diffraction

Pulsed-laser deposition (PLD) is a powerful technique for growing complex oxides with controlled stoichiometry. To understand growth dynamics therein, it is common to leverage in situ spectroscopies, such as reflection high-energy electron diffraction (RHEED), to monitor surface crystallinity. Most commercial systems rely on video-rate cameras operating at 60-120 Hz that lack sufficient temporal resolution to capture growth dynamics at practical deposition frequencies. Here, a high-speed platform to record in situ dynamics via RHEED at >500 Hz is implemented. An open-source analysis package is designed to fit diffraction spots to 2D Gaussians, allowing single-pulse surface reconstruction kinetics extraction. Using homoepitaxially deposited (001)-oriented SrTiO 3 as a model system, we demonstrate how high-speed RHEED can provide real-time insight into growth processes obscured by slower acquisition systems. By fitting the single-pulse intensity to a set of exponential functions, we observe changes in the characteristic decay time and mechanism correlated to the substrate step width and surface termination. We observe distinct surface effects, with diffraction intensity decaying on lower-energy TiO 2 -terminated surfaces and stabilizing on SrO- or mixed-terminated surfaces. Similarly, using an exponential model, the extracted characteristic time of adatom deposition decreases with increased density of bonding sites associated with mixed termination and narrower step widths. Ultimately, this work shows how increasing RHEED temporal resolution can uncover new insights into growth processes, with practical implications for the design and control of PLD processes. This experimental platform provides new capabilities to enable data-driven machine learning analysis and autonomous control systems to enhance the complexity and fecundity of PLD.

(SrO)↗

DOE OSTI2020

4D imaging of lithium-batteries using correlative neutron and X-ray tomography with a virtual unrolling technique

The temporally and spatially resolved tracking of lithium intercalation and electrode degradation processes are crucial for detecting and understanding performance losses during the operation of lithium-batteries. Here, high-throughput X-ray computed tomography has enabled the identification of mechanical degradation processes in a commercial Li/MnO 2 primary battery and the indirect tracking of lithium diffusion; furthermore, complementary neutron computed tomography has identified the direct lithium diffusion process and the electrode wetting by the electrolyte. Virtual electrode unrolling techniques provide a deeper view inside the electrode layers and are used to detect minor fluctuations which are difficult to observe using conventional three dimensional rendering tools. Moreover, the 'unrolling' provides a platform for correlating multi-modal image data which is expected to find wider application in battery science and engineering to study diverse effects e.g. electrode degradation or lithium diffusion blocking during battery cycling.

25 ENERGY STORAGE↗

DOE OSTI2017

Three-Dimensional Design Simulations of a High-Energy Density Reshock Experiment at the National Ignition Facility

We present here simulations of a new experimental platform at the National Ignition Facility (NIF) for studying the hydrodynamic instability growth of a high-energy density (HED) fluid interface that undergoes multiple shocks, i.e., is “reshocked.” In these experiments, indirect-drive laser cavities drive strong shocks through an initially solid, planar interface between a high-density plastic and low-density foam, in either one or both directions. The first shock turns the system into an unstable fluid interface with the premachined initial condition that then grows via the Richtmyer–Meshkov and Rayleigh–Taylor instabilities. Backlit X-ray imaging is used to visualize the instability growth at different times. Our main result is that this new HED reshock platform is established and that the initial data confirm the experiment operates in a hydrodynamic regime similar to what simulations predict. The simulations also reveal new types of edge effects that can disturb the experiment at late times and suggest ways to mitigate them.

42 ENGINEERING↗

DOE OSTI2015

SU-E-T-170: Characterization of the Location, Extent, and Proximity to Critical Structures of Target Volumes Provides Detail for Improved Outcome Predictions Among Pancreatic Cancer Patients

Purpose: In radiotherapy, size, location and proximity of the target to critical structures influence treatment decisions. It has been shown that proximity of the target predicts dosimetric sparing of critical structures. In addition to dosimetry, precise location of disease has further implications such as tumor invasion, or proximity to major arteries that inhibit surgery. Knowledge of which patients can be converted to surgical candidates by radiation may have high impact on future treat/no-treat decisions. We propose a method to improve our characterization of the location of pancreatic cancer and treatment volume extent with respect to nearby arteries with the goal of developing features to improve clinical predictions and decisions. Methods: Oncospace is a local learning health system that systematically captures clinical outcomes and all aspects of radiotherapy treatment plans, including overlap volume histograms (OVH) – a measure of spatial relationships between two structures. Minimum and maximum distances of PTV and OARs based on OVH, PTV volume, anatomic location by ICD-9 code, and surgical outcome were queried. Normalized distance to center from the left and right kidney was calculated to indicate tumor location and laterality. Distance to critical arteries (celiac, superior mesenteric, common hepatic) is validated by surgical status (borderline resectable, locally advanced converted to resectable). Results: There were 205 pancreas stereotactic body radiotherapy patients treated from 2009–2015 queried. Location/laterality of tumor based on kidney OVH show strong trends between location by OVH and by ICD-9. Compared to the locally advanced group, the borderline resectable group showed larger geometrical distance from critical arteries (p=0.03). Conclusion: Our platform enabled analysis of shape/size-location relationships. These data suggest that PTV volume and attention to distance between PTVs and surrounding OARs and major arteries may be promising for improving characterization of treatment anatomy that can refine our ability for outcome predictions and decision making. Elekta, Toshiba.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2019

Neutron Gamma LAMP Software version 1

The Localization and Mapping Platform (LAMP) software fuses radiation and contextual data to produce 3-D maps that locate and identify gamma-ray emitting sources in complex environments. It enables data collection, fusion, and processing onboard the LAMP system in handheld configurations, and enables near-real-time 3-D mapping from onboard an unmanned aerial system (UAS). This software enables a real-time 3-D gamma-ray mapping capability that is not available commercially. This software disclosure is for a new simultaneous gamma-ray and neutron mapping software update to the original LAMP software.

Cates, Joshua↗

DOE OSTI2018

Localization and Mapping Platform Software (LAMP) v3

Joshi, Tenzing↗

DOE OSTI2018

Localization and Mapping Platform Software (LAMP Software) v1

Haefner, Andrew↗

DOE OSTI2021

pnnl/fieryfuture

Deep learning approaches which incorporate geophysical data and readings from multiple remote sensing platforms for predictive mapping of vegetation over very large, ecologically diverse regions.

Larson, Kyle↗

DOE OSTI2014

A Study of Successive Over-relaxation Method Parallelization over Modern HPC Languages

Successive over-relaxation (SOR) is a computationally intensive, yet extremely important iterative solver for solving linear systems. Due to recent trends of exponential growth in amount of data generated and increasing problem sizes, serial platforms have proved to be insucient in providing the required computational power. In this paper, we present parallel implementations of red-black SOR method using three modern programming languages namely Chapel, D and Go. We employ SOR method for solving 2D steady-state heat conduction problem. We discuss the optimizations incorporated and the features of these languages which are crucial for improving the program performance. Experiments have been performed using 2, 4, and 8 threads and performance results are compared with serial execution. The analysis of results provides important insights into working of SOR method.

Mittal, Sparsh↗

DOE OSTI2021

Aspen ploidy level and cover maps from NEON Airborne Observation Platform, 2018, Crested Butte, Colorado

Maps of aspen ploidy (diploid or triploid) developed using National Ecological Observatory Network (NEON) Airborne Observation Platform visible to shortwave infrared imaging spectroscopy data that were acquired over the Upper East River, Colorado in 2018. Maps of aspen distribution, as well as other land cover types that were used to screen for aspen dominated pixels are also provided. Each image file is provided as a GeoTiff with internal tiling and LZW compression. For use of ploidy maps, it is necessary to mask out non-aspen and shaded pixels. Shaded pixels are provided in an associated data package. For full details, please see associated publication and methods description. This dataset is associated with the publication in Ecological Applications.

54 ENVIRONMENTAL SCIENCES↗