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At least 613 records · Page 34

2001 National Household Travel Survey - Wisconsin Add-On

The Wisconsin add-on survey supplements the 2001 National Household Travel Survey (NHTS) with additional household samples and detailed travel behavior for an assigned travel day. The Federal Highway Administration and Wisconsin Department of Transportation conducted the survey. The NHTS ran from March 2001 through May 2002, with data collection for the Wisconsin add-on occurring May 1, 2001, to May 6, 2002. The NHTS collected detailed information on travel behavior nationwide, as well as data on the demographic and socioeconomic composition of households. State transportation departments and metropolitan planning agencies had the opportunity to purchase extra household samples as part of the NHTS add-on program. These additional samples, along with national samples collected in the add-on areas, are compiled for use in transportation planning, forecasting, and research. The Wisconsin Department of Transportation participated in the NHTS add-on program and received a total of 17,610 household samples in Wisconsin. (The total number of household samples nationwide is 26,038.)

1Hz data↗

2009 National Household Travel Survey - Wisconsin Add-On

The Wisconsin add-on survey supplements the 2009 National Household Travel Survey (NHTS) with additional household samples and detailed travel behavior for an assigned travel day. The Federal Highway Administration and Wisconsin Department of Transportation conducted the survey from March 2008 through May 2009. It collected detailed information on travel behavior nationwide, as well as data on the demographic and socioeconomic composition of households. State transportation departments and metropolitan planning agencies had the opportunity to purchase extra household samples as part of the NHTS add-on program. These additional samples, along with national samples collected in the add-on areas, are compiled for use in transportation planning, forecasting, and research. The Wisconsin Department of Transportation participated in the NHTS add-on program and received a total of 1,707 household samples in Wisconsin. (The total number of household samples nationwide is 26,000.)

1Hz data↗

2017 National Household Travel Survey - Wisconsin Add-On

The Wisconsin add-on survey supplements the 2017 National Household Travel Survey (NHTS) with additional household samples and detailed travel behavior for an assigned travel day. The Federal Highway Administration and Wisconsin Department of Transportation conducted the survey. The NHTS ran from April 19, 2016, through April 25, 2017. It collected detailed information on travel behavior nationwide, as well as data on the demographic and socioeconomic composition of households. State transportation departments and metropolitan planning agencies had the opportunity to purchase extra household samples as part of the NHTS add-on program. These additional samples, along with national samples collected in the add-on areas, are compiled for use in transportation planning, forecasting, and research. The Wisconsin Department of Transportation participated in the NHTS add-on program and received a total of 11,675 household samples in Wisconsin. (The total number of household samples nationwide is 129,112.)

1Hz data↗

PubChemLite Plus Collision Cross Section (CCS) Values for Enhanced Interpretation of Nontarget Environmental Data

Finding relevant chemicals in the vast (known) chemical space is a major challenge for environmental and exposomics studies leveraging nontarget high resolution mass spectrometry (NT-HRMS) methods. Chemical databases now contain hundreds of millions of chemicals, yet many are not relevant. This article details an extensive collaborative, open science effort to provide a dynamic collection of chemicals for environmental, metabolomics, and exposomics research, along with supporting information about their relevance to assist researchers in the interpretation of candidate hits. The PubChemLite for Exposomics collection is compiled from ten annotation categories within PubChem, enhanced with patent, literature and annotation counts, predicted partition coefficient (logP) values, as well as predicted collision cross section (CCS) values using CCSbase. Monthly versions are archived on Zenodo under a CC-BY license, supporting reproducible research, and a new interface has been developed, including historical trends of patent and literature data, for researchers to browse the collection. This article details how PubChemLite can support researchers in environmental and exposomics studies, describes efforts to increase the availability of experimental CCS values, and explores known limitations and potential for future developments. The data and code behind these efforts are openly available.

PubChem↗

Achieving Stable Molybdenum Oxide Cathodes for Aqueous Zinc-Ion Batteries in Water-in-Salt Electrolyte

A layered MoO 3 material with large interlayer spacing represents a promising cathode for aqueous rechargeable Zn-ion batteries (ARZIBs), but the implementation of this material is limited due to the intrinsically low conductivity and poor structural stability. A 30 m ZnCl 2 water-in-salt electrolyte (WISE) was introduced to a MoO 3 nanobelt cathode for the first time, significantly increasing the stability of MoO 3 cathodes compared to those in 3 M ZnSO 4 and 3 M ZnCl2 electrolyte. The Zn/MoO 3 cell in WISE unambiguously demonstrated significantly improved rate performance delivering 349, 253, and 222 mAh/g at 100, 500, and 1000 mA/g, denoting a 2×, and 12× capacity increase of those achieved in 3 M electrolytes at 500 and 1000 mA/g, respectively. A capacity retention rate of 73% was achieved after (dis)charging at 100 mA/g for 100 cycles, and no obvious capacity fading was observed at higher current densities of 500 mA/g and 2A/g. A compilation of structural and morphological characterization was systematically performed to provide insight into the mechanisms of the improved performance in ZnCl 2 WISE for the MoO3 cathode materials. Specifically, our data collectively suggested that the drastic fading in 3M electrolytes can be attributed to the parasitic surface deposits on Zn originated from Mo dissolution and H 2 formation due to Zn corrosion and hydrogen evolution reaction (HER), which were significantly suppressed in the ZnCl 2 WISE. The direct visualization of these side reactions was achieved for the first time in the Zn-MoO 3 system, using an in situ optoelectrochemical measurement.

25 ENERGY STORAGE↗

Spatial biases of information influence global estimates of soil respiration: how can we improve global predictions?

Soil respiration (Rs), the efflux of CO2 from soils to the atmosphere, is a major component of the terrestrial carbon cycle, but is poorly constrained from regional to global scales. The global soil respiration database (SRDB) is a compilation of in-situ Rs observations from around the globe that has been consistently updated with new measurements over the past decade. It is unclear whether the addition of data to new versions of the SRDB has produced better-constrained global Rs estimates. We compared two versions of the SRDB (v3.0 n=5173 and v5.0 n=10366) to determine how additional data influenced the global sum and uncertainty of Rs as predicted by a quantile regression forest model. A model parameterized using SRDBv3 yielded a global Rs sum of 88.6 Pg C yr-1, and associated uncertainty of 29.9 (mean absolute error) and 57.9 (standard deviation) Pg C yr-1, whereas parameterization using SRDBv5 yielded 96.5 Pg C yr-1 and associated uncertainty of 30.2 (mean average error) and 73.4 (standard deviation) Pg C yr-1. Empirically estimated global heterotrophic respiration (Rh) from v3 and v5 were 49.9-50.2 (mean 50.1) and 53.3-53.5 (mean 53.4) Pg C yr-1, respectively. Interestingly, SRDBv5’s inclusion of new data from underrepresented regions (e.g., Asia, Africa, South America) thus resulted in overall higher model uncertainty. The SRDBv5 is still biased towards northern latitudes and temperate zones, so we tested that an optimized global distribution of Rs measurements resulted in a global sum of 96.4 ± 21.4 Pg C yr-1 and lower model uncertainty. These results support current global estimates of Rs but highlight spatial inconsistencies that influence model parameterization and interpretation that should be addressed to improve regional- to global-scale predictions.

Stell, Emma↗

Verification of Triso Fuel Burnup Using Machine Learning Algorithms

Pebble Bed Reactors are fueled with fuel pebbles that are circulated multiple times through the reactor vessel before discharge. During the normal operation of a PBR, ejected pebbles are returned to the reactor or discharged depending on the fuel burnup and physical condition of the pebbles. The burnup measurement is usually based on detected radiation signatures of fission products accumulated in the pebble fuel over burnup. Previous research has shown that height of photopeaks of fission products, such as 134 Cs, 137 Cs, 154 Eu, etc., can be used independently or in combination to infer or predict the level of burnup in the fuel. However, it remains challenging to measure such complex sources due to self-shielding effects, strong radiation background and intervening materials. Another operational challenge is the required high throughput of burnup measurement, which necessitates limited measurement time and thus impacts quality of measured gamma-ray spectra. Hence, advanced spectral analysis methods are needed to analyze the noisy gamma spectra and predict the burnup values. We propose to use machine learning (ML) method to interpret gamma-ray spectra and predict the burnup values of the pebbles. ML has achieved widespread success and adoption across a few domains that require pattern recognition and analysis in varied data types. In this work, we apply three proven ML approaches - multilayer perceptrons, convolutional neural networks, and transformers - to the task of predicting fuel burnup from measured gamma spectra, and compile a dataset of simulated spectra for training and validation of the ML models. In this paper, we will discuss the network architecture of these three ML approaches and compare the performance of the simplest of these (MLP) to a standard linear regression.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

Wabash CarbonSAFE. Geologic Analysis of the Potosi Dolomite Reservoir Interval and Potential Confining Units, at the Wabash #1 Well, Vigo Co., Indiana, Subtask 7.2, Technical Report

The Wabash CarbonSAFE project drilled the Wabash #1 stratigraphic test well (ID# 168045) at the Wabash Valley Resources (WVR) IGCC facility in Vigo County, Indiana, to evaluate the feasibility of commercial-scale CO 2 storage near the site. The Mt. Simon Sandstone was the initial target for storage evaluation and was found to have generally poor reservoir qualities in the Wabash #1 well. Focus was then placed on the secondary target, the Potosi Dolomite. For storage and containment evaluation an extensive suite of geophysical logs was collected throughout the borehole, and whole cores were obtained from the Mt. Simon Sandstone, Eau Claire Formation, and Maquoketa Group. The Maquoketa Group is identified as a regionally extensive secondary sealing interval above the Mt. Simon Sandstone and as a primary seal for the Potosi Dolomite storage complex. Due to concerns over potential lost circulation zones in the Potosi Dolomite during drilling, no core samples or Formation Micro Imager (FMI) logs were acquired in the Potosi Dolomite. However, cased hole well testing was performed over a 20 feet (6 m) interval within the Potosi Dolomite during which a fluid swab sample was obtained. Approximately 35 miles of 2D seismic information was acquired in the project area near Wabash #1 to aid in reservoir and caprock characterization. The highly porous and permeable zones in the Potosi Dolomite and lower Oneota Dolomite observed within the Wabash #1 well are, for the purpose of this report, considered the same reservoir package and referred to as the Potosi reservoir interval. This report presents a regional depositional context for the Potosi reservoir interval, the overlying confining zones in the Knox Group, and Maquoketa Group primary seal. This context is developed through detailed information obtained from the Wabash #1 well such as lithologic data collected from cuttings and core, geophysical logging, geomechanical analysis of core samples, and well testing and fluid sampling within the Potosi reservoir interval. Information discussed in this report was compiled as a basis for the development of static and dynamic geologic models of the Potosi Dolomite storage complex. The static and dynamic modeling of CO 2 injection in the Potosi reservoir interval are discussed in a separate report (Dessenberger et al., 2022) under the Wabash CarbonSAFE project.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

Hawai‘i Play Fairway (Final Report)

Most of Hawai'i's geothermal resources are blind—their manifestations, such as hot springs and steam vents, do not appear on the ground surface because the heated water flows far below. With the exception of $K\bar{i}lauea East Rift Zone$, in most areas of Hawai'i, high lateral permeability in the first kilometer below ground surface prevents surface thermal features from developing. As a methodology for discovering these blind resources, Play Fairway Analysis (PFA) involves finding potential locations of blind hydrothermal systems and describing potential geothermal sources in rift-zone settings. Using the PFA to find Hawai'i's geothermal resources, the University of Hawai'i (UH) conducted the Hawai‘i Play Fairway Project, Hawai'i's first statewide geothermal resource assessment since 1985. Sponsored by the U.S. Department of Energy, the Hawai'i Play Fairway Project provided an updated resource assessment, a roadmap for additional exploration activities, and the identification of areas for further exploration. Benefitting from UH's core competency in earth sciences and experienced geothermal researchers, the project comprised three phases. During the first phase, the team identified, compiled, and ranked existing geologic, groundwater, and geophysical datasets relevant to subsurface heat, fluid and permeability. Using a Bayesian statistical approach, the team developed a statistical methodology to integrate these data into a resource probability map. The team evaluated the confidence in the probability value and considered development viability of areas with geothermal resources. With these analyses, the team identified 10 locations in the Hawaiian Islands for exploration activities. For the second phase, the team collected new groundwater data in 10 locations across the state and new geophysical data on $L\bar{a}na‘i, Maui$, and central Hawai'i Island and modeled topographically induced stress to better characterize subsurface permeability. Analyzing the subsurface stresses, the team evaluated the potential for fracture-induced permeability. The team inverted the MT and gravity data to produce 3D models of resistivity and density, respectively, on $L\bar{a}na‘i$, across $Haleakal\bar{a}'s$ SW rift (Maui), and surrounding Mauna Kea (Hawai‘i Island). The team developed and applied a new method for incorporating depth information about resistivity, density, and potential for fracture-induced permeability into the statistical method for computing resource probability in these three focus areas. The team incorporated the new groundwater results with the new geophysical results and the calculations of potential for fracture-induced permeability to produce updated maps of resource probability and confidence. Through combining data from the first and second phases, the team determined locations for further exploration during the third phase. For MT and gravity surveys, the team recommended $Kaua'i's$ $L\bar{i}hu'e$ $Basin$, the east rift of $Maui's$ $Haleakal\bar{a}$ volcano, and the southwest rift of Hawai'i Island's Mauna Loa volcano. The MT and gravity surveys aimed to enable improved confidence in the resource potential in these locations. For drilling deep groundwater well(s), the team recommended Southeast Mauna Kea and $L\bar{a}na's$ $P\bar{a}l\bar{a}wai$ $Basin$. During the third phase, further exploration involved drilling a groundwater well in $L\bar{a}na's$ $P\bar{a}l\bar{a}wai$ $Basin$ and performing more geophysical surveys. We deepened an existing water well proximal to our target area on $L\bar{a}na'i$ due to funding constraints that precluded us from spudding a new well that would exceed 1km depth. Drilling was preceded by a number of substantial elements including: writing an Environmental Assessment and the subsequent legal process, performance of deviation logging, lowering a camera down the well, coordinating site preparation with $P\bar{u}lama$ $L\bar{a}na'i$, shipping the UH-owned rig interisland, procuring supplies, and leading 3 community meetings on $L\bar{a}na'i$. Drilling occurred 24/7 the entire month of June 2019 over which time $L\bar{a}na'i$ $Well$ 10 was deepened from 427 m to 1057 m, with continuous core collected. We measured a roughly linear temperature gradient averaging 42°C/km and a maximum bottom hole temperature of 66°C. This gradient is more than twice the background for Hawai'i and within a range of gradients measured in this depth range for some exploration wells within KERZ. We consider these results encouraging for $L\bar{a}na'i's$ resource potential and recommend following with a slim hole within $L\bar{a}na'i's$ caldera (our target zone) to ~ 2 km. Further, the positive implications such results have for the island of O‘ahu are substantial - the shield stage of O'ahu's volcanoes ended 1-2 My earlier. However, O'ahu uses more electricity than the rest of the islands combined, and the utility recently called for 500-700MW of firm, dispatchable renewable electricity on O'ahu by 2033. In Phase 3, we also collected limited new encouraging groundwater data, and updated our thoughts on the probabilities of fluid and permeability at resource depths (Pr F = 1; Pr P = mostly unconstrained). Ultimately, we advocate for using our final probability of heat, and confidence in this probability, to drive the next phase of exploration. We contend further development of geothermal in Hawai‘i will enable the state to achieve its 100% renewable policy objective and Hawai'i to transition off of fossil fuels through geothermal discovery and development. The project not only produced a large amount of data and expanded the existing knowledge of Hawai'i's geothermal resources, but also produced publications, theses, presentations, core photos, datasets, media reports, television interviews, community events, and a blog. Students and new professionals benefitted from the project's hands-on research experiences and educational opportunities and earned awards and recognition.

15 GEOTHERMAL ENERGY↗

VERIFICATION OF TRISO FUEL BURNUP USING MACHINE LEARNING ALGORITHMS

Pebble Bed Reactors are fueled with fuel pebbles that are circulated multiple times through the reactor vessel before discharge. During the normal operation of a PBR, ejected pebbles are returned to the reactor or discharged depending on the fuel burnup and physical condition of the pebbles. The burnup measurement is usually based on detected radiation signatures of fission products accumulated in the pebble fuel over burnup. Previous research has shown that height of photopeaks of fission products, such as 134Cs, 137Cs, 154Eu, etc., can be used independently or in combination to infer or predict the level of burnup in the fuel. However, it remains challenging to measure such complex sources due to self-shielding effects, strong radiation background and intervening materials. Another operational challenge is the required high throughput of burnup measurement, which necessitates limited measurement time and thus impacts quality of measured gamma-ray spectra. Hence, advanced spectral analysis methods are needed to analyze the noisy gamma spectra and predict the burnup values. We propose to use machine learning (ML) method to interpret gamma-ray spectra and predict the burnup values of the pebbles. ML has achieved widespread success and adoption across a few domains that require pattern recognition and analysis in varied data types. In this work, we apply three proven ML approaches - multilayer perceptrons, convolutional neural networks, and transformers - to the task of predicting fuel burnup from measured gamma spectra, and compile a dataset of simulated spectra for training and validation of the ML models. In this paper, we will discuss the network architecture of these three ML approaches and compare the performance of the simplest of these (MLP) to a standard linear regression.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗

U.S. Hydropower Relicensing and License Surrender Data and Metadata, 2026

The U.S. Hydropower Relicensing and License Surrender Database (2026) provides a comprehensive, nationwide snapshot (as of December 31, 2025) of hydropower projects across the United States that are undergoing Federal Energy Regulatory Commission (FERC) relicensing or license surrender processes. Compiled by Oak Ridge National Laboratory, the dataset includes detailed project-level attributes such as geographic location, ownership type, waterway, project type (hydropower or pumped storage), regulatory milestones (e.g., Notice of Intent, application dates, FERC issuance dates), licensing process type (ILP, TLP, ALP), operational characteristics, capacity changes, settlement agreements, construction or turbine‑generator investments, and project status categories spanning relicensing, surrenders, exemptions, and terminations. Together, the relicensing and surrender records offer a detailed view of regulatory trends, infrastructure transitions, dam removals, and economic drivers influencing the evolution of the U.S. hydropower fleet.

Johnson, Megan [ORNL] (ORCID:0000000290141741)↗

GIS Resource Compilation Map Package - Applications of Machine Learning Techniques to Geothermal Play Fairway Analysis in the Great Basin Region, Nevada

This submission contains an ESRI map package (.mpk) with an embedded geodatabase for GIS resources used or derived in the Nevada Machine Learning project, meant to accompany the final report. The package includes layer descriptions, layer grouping, and symbology. Layer groups include: new/revised datasets (paleo-geothermal features, geochemistry, geophysics, heat flow, slip and dilation, potential structures, geothermal power plants, positive and negative test sites), machine learning model input grids, machine learning models (Artificial Neural Network (ANN), Extreme Learning Machine (ELM), Bayesian Neural Network (BNN), Principal Component Analysis (PCA/PCAk), Non-negative Matrix Factorization (NMF/NMFk) - supervised and unsupervised), original NV Play Fairway data and models, and NV cultural/reference data. See layer descriptions for additional metadata. Smaller GIS resource packages (by category) can be found in the related datasets section of this submission. A submission linking the full codebase for generating machine learning output models is available through the "Related Datasets" link on this page, and contains results beyond the top picks present in this compilation.

15 GEOTHERMAL ENERGY↗

Oak Ridge National Laboratory FY 2023 Site Sustainability Plan With FY 2022 Performance Data

At the close of each fiscal year, the US Department of Energy (DOE) Sustainability Performance Division (SPD) issues guidance documents and technical resource aids/tools necessary for DOE sites and national laboratories to complete sustainability reporting requirements. SPD is part of the DOE Office of Asset Management. As required by DOE Order 436.1, Departmental Sustainability, “each site will develop and commit to an annual Site Sustainability Plan (SSP) that identifies its respective contribution toward meeting the DOE’s sustainability goals.” SPD collects and compiles information reported by each site to develop an agency-wide Sustainability Report and Implementation Plan, which is used to report DOE sustainability progress to the federal government as required by all major federal agencies. DOE launched a formal Sustainability Office and annual SSP process in 2011. Each year, Oak Ridge National Laboratory (ORNL), in concert with the Office of Science (SC), provides the resources essential to fulfill its commitment to deliver a complete and accurate SSP report and quality performance data for entry into the DOE Sustainability Dashboard as managed by SPD. The performance data entered by each DOE site are then combined to disclose the progress of each DOE Program Office and are further combined to show comprehensive progress for the agency. The Office of Asset Management provides assistance to program offices in sustaining their missions, freeing up resources by reducing waste, avoiding excess expenditure on utilities, maximizing productivity, and improving the efficiency of facilities and processes. By focusing on mission needs, programs and associated DOE sites can help the agency meet its sustainability goals, as outlined in federal statutory and regulatory requirements. In FY 2022, the SSP guidance was updated to capture requirements from Executive Order (EO) 14008, Tackling the Climate Crisis at Home and Abroad, the Energy Act of 2020 (EAct 20), actions outlined in DOE’s Climate Adaptation & Resilience Plan and Sustainability Plan, and EO 14057, Catalyzing Clean Energy Industries and Jobs Through Federal Sustainability. Updates in SSP guidance help to minimize and streamline reporting while simultaneously addressing updated federal requirements. Per DOE, each SSP report should provide an overview of the site’s planned actions, as well as an overview of efforts and accomplishments during the reporting period. SPD collects and compiles information reported by each site to develop DOE’s Annual Sustainability Report, Climate Adaptation & Resilience Plan, and Annual Energy Management Report to Congress. The agency goal has been to lower the reporting burden for sites and increase and improve the consistency of information available to decision makers, allowing them to better identify projects and potential for increased efficiency, as well as to reduce waste, lower emissions, and enhance operational resilience. Sites may elect to produce a more polished publication for their leadership and stakeholders, but this step is no longer required. The ORNL SSP narrative report (this document) and the reporting of DOE SPD Sustainability Dashboard performance data is a collaborative effort of approximately 30 subject matter experts (SMEs) from ORNL facility management and research divisions. Annually, these associates come together to provide a report that can be used by DOE to demonstrate continued agency progress in energy efficiency and sustainable federal operations.

99 GENERAL AND MISCELLANEOUS↗

DATASET RELEASE AND QUALITY CONTROL REVIEW OF LIVERMORE NEVADA NETWORK (LNN) RECORDINGS OF A SUBSET OF NEVADA NUCLEAR SECURITY SITE NUCLEAR EXPLOSIONS FROM 1979 TO 1992.

Geophysical research on historical nuclear tests is an important aspect of future monitoring capabilities in seismic research. This research is challenging due to the limited number of digital seismic recordings during the peak of nuclear testing (1945-1992). These limited records are unique and non-reproducible data with potential high research impact. Releasing available nuclear explosion seismic records to the explosion monitoring community is thus of high value and is the motivation for this dataset release. The target of this effort was on compilation and quality control of regional seismic records of nuclear explosions recorded on Lawrence Livermore National Laboratory stations ELK, KNB, LAC, and MNV, known collectively as the Livermore National Network (LNN) (Figure 1). LNN was established in the early 1960s for the primary purpose of monitoring underground nuclear testing at the former Nevada Test Site (NTS), now known as the Nevada Nuclear Security Site (NNSS) following the signing of the Limited Test Ban Treaty (LTBT). LNN consisted initially of short-period vertical component Benioff’s recorded on film located at Mina, NV (MNV) and Kanab, Utah (KNB). LNN added two additional stations at Landers, CA (LAC) and Elko, NV (ELK) in 1967 and upgraded equipment to broadband seismometers recorded on frequency modulation (FM) tapes from 1967-1979, followed by digital recordings after 1979 (Jarpe, 1989). The digital recordings were on a variety of now obsolete media, including 9-track, Exabyte, and DAT tapes. Jarpe (1989) describes the seismic station instrumentation details over the period of deployment. LNN recorded valuable non-repeatable unique data of several hundreds of nuclear explosions at NNSS, as well as earthquakes and chemical and mining explosions (Walter, 2020). The details of these nuclear tests are provided in the Department of Energy Report NV-209 Rev 16 (DOE, 2015).

58 GEOSCIENCES↗

Chemical-damage MINE: A database of curated and predicted spontaneous metabolic reactions

Spontaneous reactions between metabolites are often neglected in favor of emphasizing enzyme-catalyzed chemistry because spontaneous reaction rates are assumed to be insignificant under physiological conditions. However, synthetic biology and engineering efforts can raise natural metabolites' levels or introduce unnatural ones, so that previously innocuous or nonexistent spontaneous reactions become an issue. Problems arise when spontaneous reaction rates exceed the capacity of a platform organism to dispose of toxic or chemically active reaction products. While various reliable sources list competing or toxic enzymatic pathways' side-reactions, no corresponding compilation of spontaneous side-reactions exists, nor is it possible to predict their occurrence. We addressed this deficiency by creating the Chemical Damage (CD)-MINE resource. First, we used literature data to construct a comprehensive database of metabolite reactions that occur spontaneously in physiological conditions. We then leveraged this data to construct 148 reaction rules describing the known spontaneous chemistry in a substrate-generic way. We applied these rules to all compounds in the ModelSEED database, predicting 180,891 spontaneous reactions. The resulting (CD)-MINE is available at https://minedatabase.mcs.anl. gov/cdmine/#/home and through developer tools. We also demonstrate how damage-prone intermediates and end products are widely distributed among metabolic pathways, and how predicting spontaneous chemical damage helps rationalize toxicity and carbon loss using examples from published pathways to commercial products. We explain how analyzing damage-prone areas in metabolism helps design effective engineering strategies. Finally, we use the CD-MINE toolset to predict the formation of the novel damage product N-carbamoyl proline, and present mass spectrometric evidence for its presence in Escherichia coli.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

2009 National Household Travel Survey - California Add-On

The California add-on survey supplements the 2009 National Household Travel Survey (NHTS) with additional household samples and detailed travel behavior for an assigned travel day. The NHTS was conducted by the Federal Highway Administration, which collected data on the demographic and socioeconomic composition of households, as well as detailed information on travel behavior nationwide. State transportation departments and metropolitan planning agencies had the opportunity to purchase extra household samples as part of the NHTS add-on program. These additional samples, along with national samples collected in the add-on areas, are compiled for use in transportation planning, forecasting, and research. The California Department of Transportation participated in the NHTS add-on program and received a total of 21,225 household samples in California, from March 2008 through May 2009.

1Hz data↗

2017 National Household Travel Survey - California Add-On

The California add-on survey supplements the 2017 National Household Travel Survey (NHTS) with additional household samples and detailed travel behavior for an assigned travel day. The NHTS was conducted by the Federal Highway Administration, which collected data on the demographic and socioeconomic composition of households, as well as detailed information on travel behavior nationwide. State transportation departments and metropolitan planning agencies had the opportunity to purchase extra household samples as part of the NHTS add-on program. These additional samples, along with national samples collected in the add-on areas, are compiled for use in transportation planning, forecasting, and research. The California Department of Transportation participated in the NHTS add-on program and received a total of 26,095 household samples in California, from April 2016 through April 2017.

1Hz data↗

Predictions for the sPHENIX physics program

sPHENIX is a next-generation detector experiment at the Relativistic Heavy Ion Collider, designed for a broad set of jet and heavy-flavor probes of the Quark-Gluon Plasma created in heavy ion collisions. In anticipation of the commissioning and first data-taking of the detector in 2023, a RIKEN-BNL Research Center (RBRC) workshop was organized to collect theoretical input and identify compelling aspects of the physics program. Here this paper compiles theoretical predictions from the workshop participants for jet quenching, heavy flavor and quarkonia, cold QCD, and bulk physics measurements at sPHENIX.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗