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DOE OSTI2025

Data from a throughfall exclusion experiment: Fine root dynamics, morphology, chemistry, and AMF colonization across four lowland Panamanian forests

Fine roots regulate forest nutrient, carbon, and water cycling, yet their variation within and among tropical forests remains under-characterized. We quantified root productivity, disappearance, and stocks to 1 m using minirhizotron imaging, and we measured morphology, elemental composition [root carbon (C), root nitrogen (N), root phosphorus (P)], and arbuscular mycorrhizal fungi (AMF) colonization to 20 cm using ingrowth cores and sequential coring. Sampling took place in four distinct lowland Panamanian forests (32 plots; 8 per forest) from 2018 through 2022 under control and throughfall-exclusion (drought) treatments in the Panama Rainforest Changes with Experimental Drying (PARCHED) experiment.The dataset is presented as an Excel workbook with six tabs. The first tab is the data dictionary. Tab S1 contains ingrowth-core production and mortality, morphology and soil moisture. Tab S2 contains sequential-coring standing stocks with associated morphology and soil moisture. Tab S3 contains minirhizotron row data records to 1 m depth, including per-frame root length and diameter, normalized length metrics, and session timing. Tab S4 contains AMF colonization. Tab S5 contains fine-root chemistry at 0–10 cm, reporting %P, %C, %N, and C:N for samples collected via ingrowth cores and sequential-coring standing stocks. CSV mirrors for each tab are provided, and a KML file supplies coordinates for all 32 plots.Key variables span live and dead fine-root biomass (and coarse fractions where applicable), specific root length (SRL) and area (SRA), diameter, root tissue density (RTD), soil moisture, AMF colonization, root %N, %C, %P, and C:N, along with minirhizotron root length and diameter. Depth, season, treatment, and plot/site identifiers are included to support cross-tab integration and analysis from 0–100 cm (minirhizotron) and 0–20 cm (cores).Units are reported in-column and missing values are coded as NA. No special software is required to open or use the files (Excel, CSV, and KML compatible).

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2020

Materials Data on Sr6Sb6S17 by Materials Project

Sr6Sb6S14(S3) crystallizes in the orthorhombic P2_12_12_1 space group. The structure is three-dimensional. there are six inequivalent Sr2+ sites. In the first Sr2+ site, Sr2+ is bonded in a 8-coordinate geometry to eight S2- atoms. There are a spread of Sr–S bond distances ranging from 3.01–3.27 Å. In the second Sr2+ site, Sr2+ is bonded in a 9-coordinate geometry to nine S2- atoms. There are a spread of Sr–S bond distances ranging from 3.05–3.60 Å. In the third Sr2+ site, Sr2+ is bonded to seven S2- atoms to form distorted SrS7 pentagonal bipyramids that share a cornercorner with one SbS5 square pyramid and edges with two equivalent SrS7 pentagonal bipyramids. There are a spread of Sr–S bond distances ranging from 3.02–3.32 Å. In the fourth Sr2+ site, Sr2+ is bonded to seven S2- atoms to form distorted SrS7 pentagonal bipyramids that share a cornercorner with one SbS5 square pyramid and edges with two equivalent SrS7 pentagonal bipyramids. There are a spread of Sr–S bond distances ranging from 3.03–3.19 Å. In the fifth Sr2+ site, Sr2+ is bonded in a 9-coordinate geometry to nine S2- atoms. There are a spread of Sr–S bond distances ranging from 3.04–3.31 Å. In the sixth Sr2+ site, Sr2+ is bonded in a 9-coordinate geometry to nine S2- atoms. There are a spread of Sr–S bond distances ranging from 3.03–3.32 Å. There are six inequivalent Sb+3.67+ sites. In the first Sb+3.67+ site, Sb+3.67+ is bonded in a distorted trigonal non-coplanar geometry to three S2- atoms. There are two shorter (2.47 Å) and one longer (2.53 Å) Sb–S bond lengths. In the second Sb+3.67+ site, Sb+3.67+ is bonded to five S2- atoms to form SbS5 square pyramids that share corners with two SrS7 pentagonal bipyramids. There are a spread of Sb–S bond distances ranging from 2.44–3.08 Å. In the third Sb+3.67+ site, Sb+3.67+ is bonded in a distorted rectangular see-saw-like geometry to four S2- atoms. There are a spread of Sb–S bond distances ranging from 2.47–3.17 Å. In the fourth Sb+3.67+ site, Sb+3.67+ is bonded in a 4-coordinate geometry to three S2- atoms. There are a spread of Sb–S bond distances ranging from 2.46–2.57 Å. In the fifth Sb+3.67+ site, Sb+3.67+ is bonded in a distorted rectangular see-saw-like geometry to four S2- atoms. There are a spread of Sb–S bond distances ranging from 2.47–2.87 Å. In the sixth Sb+3.67+ site, Sb+3.67+ is bonded in a distorted trigonal non-coplanar geometry to three S2- atoms. There are a spread of Sb–S bond distances ranging from 2.46–2.53 Å. There are seventeen inequivalent S2- sites. In the first S2- site, S2- is bonded in a 5-coordinate geometry to four Sr2+ and one S2- atom. The S–S bond length is 2.10 Å. In the second S2- site, S2- is bonded in a 6-coordinate geometry to four Sr2+ and two S2- atoms. The S–S bond length is 2.09 Å. In the third S2- site, S2- is bonded in a 5-coordinate geometry to four Sr2+ and one S2- atom. In the fourth S2- site, S2- is bonded to two Sr2+ and two Sb+3.67+ atoms to form distorted SSr2Sb2 trigonal pyramids that share a cornercorner with one SSr4Sb square pyramid, a cornercorner with one SSr4Sb trigonal bipyramid, corners with two equivalent SSr2Sb2 trigonal pyramids, and edges with three SSr4Sb trigonal bipyramids. In the fifth S2- site, S2- is bonded to four Sr2+ and one Sb+3.67+ atom to form distorted SSr4Sb trigonal bipyramids that share corners with four SSr4Sb trigonal bipyramids, an edgeedge with one SSr4Sb square pyramid, an edgeedge with one SSr4Sb trigonal bipyramid, and edges with two SSr2Sb2 trigonal pyramids. In the sixth S2- site, S2- is bonded to two Sr2+ and two Sb+3.67+ atoms to form SSr2Sb2 trigonal pyramids that share a cornercorner with one SSr4Sb square pyramid, a cornercorner with one SSr4Sb trigonal bipyramid, corners with two equivalent SSr2Sb2 trigonal pyramids, and edges with three SSr4Sb trigonal bipyramids. In the seventh S2- site, S2- is bonded in a 5-coordinate geometry to four Sr2+ and one Sb+3.67+ atom. In the eighth S2- site, S2- is bonded in a 3-coordinate geometry to one Sr2+ and two Sb+3.67+ atoms. In the ninth S2- site, S2- is bonded in a 3-coordinate geometry to one Sr2+ and two Sb+3.67+ atoms. In the tenth S2- site, S2- is bonded to four Sr2+ and one Sb+3.67+ atom to form distorted SSr4Sb trigonal bipyramids that share corners with four SSr4Sb trigonal bipyramids, corners with two SSr2Sb2 trigonal pyramids, edges with two equivalent SSr4Sb square pyramids, and an edgeedge with one SSr4Sb trigonal bipyramid. In the eleventh S2- site, S2- is bonded in a 4-coordinate geometry to one Sr2+ and two Sb+3.67+ atoms. In the twelfth S2- site, S2- is bonded in a distorted rectangular see-saw-like geometry to one Sr2+ and three Sb+3.67+ atoms. In the thirteenth S2- site, S2- is bonded to four Sr2+ and one Sb+3.67+ atom to form SSr4Sb square pyramids that share corners with two equivalent SSr4Sb trigonal bipyramids, corners with two SSr2Sb2 trigonal pyramids, and edges with four SSr4Sb trigonal bipyramids. In the fourteenth S2- site, S2- is bonded in a 3-coordinate geometry to three Sr2+ and one Sb+3.67+ atom. In the fifteenth S2- site, S2- is bonded in a distorted rectangular see-saw-like geometry to two Sr2+ and two Sb+3.67+ atoms. In the sixteenth S2- site, S2- is bonded to four Sr2+ and one Sb+3.67+ atom to form distorted SSr4Sb trigonal bipyramids that share corners with two equivalent SSr4Sb square pyramids, edges with four SSr4Sb trigonal bipyramids, and edges with two SSr2Sb2 trigonal pyramids. In the seventeenth S2- site, S2- is bonded to four Sr2+ and one Sb+3.67+ atom to form distorted SSr4Sb trigonal bipyramids that share corners with four SSr4Sb trigonal bipyramids, an edgeedge with one SSr4Sb square pyramid, edges with two equivalent SSr4Sb trigonal bipyramids, and edges with two SSr2Sb2 trigonal pyramids.

36 MATERIALS SCIENCE↗

DOE OSTI2020

Materials Data on Sb6Pb6S17 by Materials Project

Pb6Sb6S14(S3) crystallizes in the orthorhombic P2_12_12 space group. The structure is three-dimensional. there are three inequivalent Pb2+ sites. In the first Pb2+ site, Pb2+ is bonded in a 7-coordinate geometry to seven S2- atoms. There are a spread of Pb–S bond distances ranging from 2.80–3.49 Å. In the second Pb2+ site, Pb2+ is bonded in a 9-coordinate geometry to nine S2- atoms. There are a spread of Pb–S bond distances ranging from 3.00–3.31 Å. In the third Pb2+ site, Pb2+ is bonded in a 8-coordinate geometry to eight S2- atoms. There are a spread of Pb–S bond distances ranging from 2.90–3.40 Å. There are three inequivalent Sb+3.67+ sites. In the first Sb+3.67+ site, Sb+3.67+ is bonded to five S2- atoms to form a mixture of distorted edge and corner-sharing SbS5 square pyramids. There are a spread of Sb–S bond distances ranging from 2.44–3.16 Å. In the second Sb+3.67+ site, Sb+3.67+ is bonded to five S2- atoms to form a mixture of edge and corner-sharing SbS5 square pyramids. There are a spread of Sb–S bond distances ranging from 2.48–2.91 Å. In the third Sb+3.67+ site, Sb+3.67+ is bonded in a 5-coordinate geometry to five S2- atoms. There are a spread of Sb–S bond distances ranging from 2.47–3.24 Å. There are nine inequivalent S2- sites. In the first S2- site, S2- is bonded in a 1-coordinate geometry to four Pb2+ and one Sb+3.67+ atom. In the second S2- site, S2- is bonded in a 1-coordinate geometry to four Pb2+ and one S2- atom. The S–S bond length is 2.08 Å. In the third S2- site, S2- is bonded in a distorted water-like geometry to two Pb2+ and two equivalent Sb+3.67+ atoms. In the fourth S2- site, S2- is bonded in a distorted water-like geometry to two Pb2+ and two equivalent Sb+3.67+ atoms. In the fifth S2- site, S2- is bonded in a 5-coordinate geometry to one Pb2+ and four Sb+3.67+ atoms. In the sixth S2- site, S2- is bonded to four Pb2+ and one Sb+3.67+ atom to form distorted edge-sharing SSbPb4 square pyramids. In the seventh S2- site, S2- is bonded in a distorted bent 120 degrees geometry to four Pb2+ and two equivalent S2- atoms. In the eighth S2- site, S2- is bonded to one Pb2+ and four Sb+3.67+ atoms to form distorted edge-sharing SSb4Pb square pyramids. In the ninth S2- site, S2- is bonded in a 1-coordinate geometry to four Pb2+ and one Sb+3.67+ atom.

36 MATERIALS SCIENCE↗

DOE OSTI2020

Materials Data on Eu6Sb6S17 by Materials Project

Eu6Sb6S14(S3) crystallizes in the orthorhombic P2_12_12_1 space group. The structure is three-dimensional. there are six inequivalent Eu+2.67+ sites. In the first Eu+2.67+ site, Eu+2.67+ is bonded to seven S2- atoms to form distorted EuS7 pentagonal bipyramids that share a cornercorner with one SbS5 square pyramid and edges with two equivalent EuS7 pentagonal bipyramids. There are a spread of Eu–S bond distances ranging from 3.00–3.12 Å. In the second Eu+2.67+ site, Eu+2.67+ is bonded to seven S2- atoms to form distorted EuS7 pentagonal bipyramids that share a cornercorner with one SbS5 square pyramid and edges with two equivalent EuS7 pentagonal bipyramids. There are a spread of Eu–S bond distances ranging from 2.96–3.25 Å. In the third Eu+2.67+ site, Eu+2.67+ is bonded in a 8-coordinate geometry to eight S2- atoms. There are a spread of Eu–S bond distances ranging from 2.98–3.26 Å. In the fourth Eu+2.67+ site, Eu+2.67+ is bonded in a 9-coordinate geometry to nine S2- atoms. There are a spread of Eu–S bond distances ranging from 3.03–3.41 Å. In the fifth Eu+2.67+ site, Eu+2.67+ is bonded in a 9-coordinate geometry to nine S2- atoms. There are a spread of Eu–S bond distances ranging from 3.00–3.23 Å. In the sixth Eu+2.67+ site, Eu+2.67+ is bonded in a 9-coordinate geometry to nine S2- atoms. There are a spread of Eu–S bond distances ranging from 3.01–3.22 Å. There are six inequivalent Sb3+ sites. In the first Sb3+ site, Sb3+ is bonded in a distorted rectangular see-saw-like geometry to four S2- atoms. There are a spread of Sb–S bond distances ranging from 2.50–2.98 Å. In the second Sb3+ site, Sb3+ is bonded in a 4-coordinate geometry to four S2- atoms. There are a spread of Sb–S bond distances ranging from 2.47–3.13 Å. In the third Sb3+ site, Sb3+ is bonded in a distorted rectangular see-saw-like geometry to four S2- atoms. There are a spread of Sb–S bond distances ranging from 2.47–3.13 Å. In the fourth Sb3+ site, Sb3+ is bonded to five S2- atoms to form SbS5 square pyramids that share corners with two EuS7 pentagonal bipyramids. There are a spread of Sb–S bond distances ranging from 2.46–3.12 Å. In the fifth Sb3+ site, Sb3+ is bonded in a 3-coordinate geometry to four S2- atoms. There are a spread of Sb–S bond distances ranging from 2.47–3.28 Å. In the sixth Sb3+ site, Sb3+ is bonded in a 3-coordinate geometry to three S2- atoms. There are a spread of Sb–S bond distances ranging from 2.47–2.55 Å. There are seventeen inequivalent S2- sites. In the first S2- site, S2- is bonded in a 5-coordinate geometry to four Eu+2.67+ and one S2- atom. The S–S bond length is 2.12 Å. In the second S2- site, S2- is bonded in a 6-coordinate geometry to four Eu+2.67+ and two S2- atoms. The S–S bond length is 2.12 Å. In the third S2- site, S2- is bonded in a 5-coordinate geometry to four Eu+2.67+ and one S2- atom. In the fourth S2- site, S2- is bonded to four Eu+2.67+ and one Sb3+ atom to form distorted SEu4Sb trigonal bipyramids that share corners with four SEu4Sb trigonal bipyramids, edges with three SEu4Sb trigonal bipyramids, and edges with two SEu2Sb2 trigonal pyramids. In the fifth S2- site, S2- is bonded in a 3-coordinate geometry to one Eu+2.67+ and two Sb3+ atoms. In the sixth S2- site, S2- is bonded in a distorted rectangular see-saw-like geometry to one Eu+2.67+ and three Sb3+ atoms. In the seventh S2- site, S2- is bonded to four Eu+2.67+ and one Sb3+ atom to form distorted SEu4Sb trigonal bipyramids that share corners with two equivalent SEu4Sb trigonal bipyramids, corners with two SEu2Sb2 trigonal pyramids, and edges with four SEu4Sb trigonal bipyramids. In the eighth S2- site, S2- is bonded to four Eu+2.67+ and one Sb3+ atom to form distorted SEu4Sb trigonal bipyramids that share corners with four SEu4Sb trigonal bipyramids, corners with two SEu2Sb2 trigonal pyramids, and edges with three SEu4Sb trigonal bipyramids. In the ninth S2- site, S2- is bonded in a 5-coordinate geometry to one Eu+2.67+ and four Sb3+ atoms. In the tenth S2- site, S2- is bonded in a 3-coordinate geometry to one Eu+2.67+ and two Sb3+ atoms. In the eleventh S2- site, S2- is bonded in a 5-coordinate geometry to four Eu+2.67+ and one Sb3+ atom. In the twelfth S2- site, S2- is bonded to four Eu+2.67+ and one Sb3+ atom to form distorted SEu4Sb trigonal bipyramids that share corners with four SEu4Sb trigonal bipyramids, edges with two SEu4Sb trigonal bipyramids, and edges with two SEu2Sb2 trigonal pyramids. In the thirteenth S2- site, S2- is bonded to two Eu+2.67+ and two Sb3+ atoms to form SEu2Sb2 trigonal pyramids that share corners with two SEu4Sb trigonal bipyramids, corners with two equivalent SEu2Sb2 trigonal pyramids, and edges with three SEu4Sb trigonal bipyramids. In the fourteenth S2- site, S2- is bonded to two Eu+2.67+ and two Sb3+ atoms to form distorted SEu2Sb2 trigonal pyramids that share corners with two SEu4Sb trigonal bipyramids, corners with two equivalent SEu2Sb2 trigonal pyramids, and edges with three SEu4Sb trigonal bipyramids. In the fifteenth S2- site, S2- is bonded in a 4-coordinate geometry to two Eu+2.67+ and two Sb3+ atoms. In the sixteenth S2- site, S2- is bonded in a 4-coordinate geometry to three Eu+2.67+ and one Sb3+ atom. In the seventeenth S2- site, S2- is bonded to four Eu+2.67+ and one Sb3+ atom to form distorted SEu4Sb trigonal bipyramids that share corners with two equivalent SEu4Sb trigonal bipyramids, edges with four SEu4Sb trigonal bipyramids, and edges with two SEu2Sb2 trigonal pyramids.

36 MATERIALS SCIENCE↗

DOE OSTI2020

Polarization Imaging for International Safeguards (FY2019)

A polarization imaging camera, Salsa (Bossa Nova Technologies) was evaluated for use in nuclear safeguards inspection with a focus on swipe sampling. The camera and supporting electronics and software determine the full Stokes parameters for the entire image field. The camera can be used for real-time video or still images. Stokes parameters S0, S1, S2, and S3 can be recorded for each pixel and exported for post processing. This study compared color photography to polarization imaging for powders on many surfaces under ambient lighting. False color images can be created from the Stokes parameters directly from the vendor software to highlight the various polarization states. In most cases, linear polarization showed the greatest effects. The polarization images are easy to collect and very sensitive to surfaces and textures. Fine powders are highlighted in the images, and particle size and refractive index effects were noticed. The images are not affected by ambient lighting (angle, or intensity). Lens choice is key and may require expert knowledge. Very small deposits of 1 µm particles were difficult to image using the standard video lens. General dirt on smooth surfaces and handprints are easily documented as well as powders on textured painted surfaces. Scratches on metal surfaces can be readily separated from other image features. The camera may be useful for tamper indication as surface damage is easy to discern and ambient lighting is not a concern.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2023

Plutonium Age Dating Interlaboratory Comparison Overview - Project Design, Sample Preparation, and Results [Slides]

Project Design – How do you validate measured Pu model age? There are no certified reference materials (CRMs) that are certified for Pu model age or purification dates. There are no Am isotopic CRMs - IRMM 0243 243 Am spike has 241 Am impurity that can be used; Community working toward "consensus ages." This project executed and evaluated results from a Pu age dating interlaboratory comparison with six IAEA NWAL participant laboratories. It also ran the first quality control evaluation of single-picogram to sub-picogram scale interlaboratory 241 Am assay measurements from cotton swipe matrices. Results are promising, with a majority of 241 Am/ 241 Pu model age results agree to within 1 to 4 years. The calculated 241 Am assay results show interlaboratory variability, expanded percent uncertainties on calculated consensus 241 Am concentrations were: PuAge-S1 = 3.1 %, PuAge-S2 = 23 %, and PuAge-S3 = 26 %. Data can be used to investigate laboratory methods and improve precision and accuracy of 241 Pu and 241 Am assay for future campaigns.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2024

Hanford Waste Treatment Plant Low Activity Waste Facility Stack Effluent Monitoring: Sampling Probe Location Qualification Evaluation (Rev.1)

The Hanford Tank Waste Treatment and Immobilization Plant Low Activity Waste (LAW) facility stack monitor locations were qualified using scale model stacks to mitigate the risk of discovering that sampling locations do not meet the qualification criteria on the full-scale stacks. As required by the American National Standards Institute/Health Physics Society (ANSI/HPS) N13.1-1999 standard, the scale model and its sampling location were geometrically similar to the actual stack and the Reynolds numbers for both the actual and model stacks were >10,000. An additional criterion is that the product of the hydraulic diameter and mean velocity (DV) of the full-scale stack must be between 1/6 DV and 6 DV of the scale model stack tests. Verification tests of the LAW stacks were performed at normal operating conditions. The minimum 1/6 DV value, along with the maximum 6 DV value from the scale model testing, determines the range of stack flow rates for which the full-scale stack may be operated and remain in compliance with the stack verification criterion. For this analysis, the range of qualified flow rates listed is conservatively based on the average DV through 6 DV for LV-S1, LV-S2, and LV-C2, and 1/3 DV to 3 DV for LV-S3. Table S1 lists the operating flow rates along with the conservative lower and upper qualified stack flow rates for each of the LAW facility stacks. For each stack, the operating flow is below the upper qualified stack.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2026

A Large-Scale Hardware Experiment Demonstration of Operating High Inverter-Based Resource Power Systems With Grid-Forming Inverters

This paper presents experimental hardware results from a microgrid system as the penetration level of grid-forming (GFM) inverters increases. The experiment aims to showcase the advantages of GFM inverters in enhancing system stability and to investigate the operational challenges in systems relying entirely on inverter-based resources (IBRs). Five test scenarios are devised to progressively increase GFM inverter penetration levels: 0% (S1), 19% (S2), 37% (S3), 68% (S4), and 100% (S5). To ensure consistency, identical loading conditions and dynamic events are applied across all scenarios. The conducted tests include load step changes, output variation of grid-following (GFL) inverters during islanded mode, transition operations (such as synchronization to the grid and islanding), and rateof- change-of-frequency (ROCOF) and voltage jump tests during grid-connected mode. Key findings from the tests are summarized as follows: (1) Scenario S1 proved most challenging for load steps, and GFL variations because of insufficient GFM capacity, leaving the diesel generator unable to handle transient ridethrough; (2) Scenario S5 was the most difficult for transition operations, because the lack of a diesel generator to maintain stiff bus voltage resulted in unexpected reactive power flows during synchronization, causing the point of common coupling (PCC) breaker to trip; (3) Scenarios S4 and S5 were particularly challenging for ROCOF tests because of minimal system inertia, leading to large transients that triggered breaker trips; and (4) Scenario S4 exhibited the strongest voltage recovery capability because of the presence of the diesel generator and a higher number of GFM inverters, both of which possess the highest capacity for reactive power injection.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2026

Comparison of Radiosonde Datasets: SondeHub and Integrated Global Radiosonde Archive

SondeHub aggregates radiosonde telemetry data uploaded from community-run radiosonde receiver stations. This radiosonde telemetry dataset is open-source, available to anyone through Amazon S3. There are also other public radiosonde datasets such as National Centers for Environmental Information (NCEI)’s Integrated Global Radiosonde Archive (IGRA). While there are many similarities between the two datasets, there are many differences as well due to the nature of the two datasets: one is community-run, while the other is managed by a government agency. This report presents the result of analyzing and comparing the two datasets.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2026

Graphical User Interface for a Biasing Board for the PSEC6

The PSEC6 is an application-specific integrated circuit (ASIC) designed for a readout system for a large area picosecond photodetector (LAPPD). The PSEC6 is currently in fabrication and pending testing. The testing system for the PSEC5, the previous iteration of the ASIC, required expensive and non-portable equipment, because the ASIC needs twelve adjustable reference voltages. The new testing system consists of an low-cost, open-source, cross-platform graphical user interface (GUI), a digital system, and a biasing board. The digital system is the interface between the GUI and biasing board, and can be implemented on a microcontroller or field-programmable gate array (FPGA). The biasing board contains twelve digital-to-analog converters (DACs) that are configurable via the GUI, which gives users the ability to write voltage values to all or specific DACs. The GUI was developed in C on Linux using the widget library GTK4 and cross-compiled for Windows compatibility. I2C and SPI protocols were implemented on an Adafruit Feather ESP32-S3 microcontroller to write commands to the DACs and PSEC6. A hardware implementation of the I2C protocol is in development on an FPGA. Since LAPPDs will be used by the Accelerator Neutrino Neutron Interaction Experiment (ANNIE) at Fermilab, the PSEC6 testing system in this internship project can potentially benefit future neutrino research. The project is relevant to the Department of Energy’s microelectronics mission, because the PSEC6 is an ASIC that will handle fast time signals arriving from the detector for readout. It also provided experience with building a cross-platform user interface, practicing digital design and implementation in hardware description language (HDL), and using simulations to inform new design iterations.

Guerrero, Sasha Camila [North Central Coll.]↗

DOE OSTI2024

KBase Narrative - Porphyromonadaceae sp. W3.11 genome

Narratives for The phenotype and genotype of fermentative prokaryotes This is the Narrative for Porphyromonadaceae sp. W3.11. A complementary Narrative for Lachnospiraceae sp. C1.1 is available here. This is the Narrative for Lachnospiraceae sp. C1.1. A complementary Narrative for Porphyromonadaceae sp. W3.11 is available here. Background and Isolation This Narrative and its complementary Narrative contain assembly and annotation of two bacterial isolates that were isolated by our laboratory from the rumen of a Holstein heifer. All procedures with animals have been approved by University of California Davis’s Institutional Animal Care and Use Committee. Rumen contents were collected through a rumen fistula and strained through two layers of cheesecloth into a bottle. The bottle was sealed to exclude air and maintained at 39°C. Contents were brought to the laboratory and bubbled under O2-free CO2 within 15 min. At the laboratory, serial dilutions were made with anaerobic dilution solution for Lachnospiraceae sp. C1.1 and propionibacterium diluent for Porphyromonadaceae sp. W3.11 (table S2). Aliquots (0.1 ml) of each dilution were injected into anaerobic bottle plates (1) containing 9 ml of LH medium (table S2). After incubation at 37°C for 7 days, isolated colonies were picked. Lachnospiraceae sp. C1.1 was picked from a bottle inoculated with a 104 dilution of rumen contents, and Porphyromonadaceae sp. W3.11 was picked from a bottle inoculated with a 103 dilution. After initial isolation, these organisms were purified by growing on anaerobic roll tubes (2) and picking isolated colonies. We performed de novo sequencing of Lachnospiraceae sp. C1.1 and Porphyromonadaceae sp. W3.11. Aliquots of liquid culture (9 and 1.5 ml, respectively) were collected by syringe and centrifuged (21,000g for 10 min at 4°C). Cell pellets were submitted to Molecular Research LP for DNA extraction, library preparation, and sequencing. After resuspending pellets in 180 µl of ATL buffer (Qiagen), DNA was extracted using the MagAttract HMW DNA Kit (Qiagen). DNA was eluted in 100 µl of AE buffer (Qiagen) and then cleaned using the DNEasy PowerClean Pro Cleanup Kit (Qiagen). DNA was then sheared using the Covaris g-TUBE (Covaris). Sequencing libraries were prepared using the SMRTbell Express Template Prep Kit 2.0 (Pacific Biosciences) and 1500 ng of the sheared and purified DNA. The SMRTbell libraries were size-selected (>6 Kb) using a BluePippin instrument (Sage Science) and 0.75% agarose gel. Libraries were then sequenced using the PacBio Sequel II (Pacific Biosciences) platform and a 30-hour movie time. Narrative Summary In these Narratives, we filtered low-quality reads using Trimmomatic (v0.36), assembled filtered reads with SPAdes (v3.15.3), and then checked completeness and contamination of the assembled genomes with CheckM (v1.0.18). Statistics for sequencing and assembly are in table S3. Using the assembled contigs (genomes), we called genes and annotated them. Protein-coding genes were called using Prodigal (v2.6.3) (3) locally or using KBase via RASTtk (v1.073), with identical results. Genes were annotated with KO IDs using KAAS (4). They were further annotated with pfam and TIGRFAM IDs using KBase and the Annotate Domains in a Genome app. We classified putative genes for hydrogenases using HydDB. Genes for 16S ribosomal RNA (rRNA) were called using RASTtk (v1.073) in KBase. The contigs (genomes) were analyzed to determine whether they belonged to new species. Taxonomy was assigned using GTDB-Tk (v1.7.0) in KBase. The identity of 16S rRNA genes to other organisms was found using EzBioCloud (5). Values of digital DNA-DNA hybridization (dDDH) were found with Type (Strain) Genome Server (6). These analyses suggest that Lachnospiraceae sp. C1.1 and Porphyromonadaceae sp. W3.11 represent novel species or genera. GTDB-Tk assigned Lachnospiracae sp. C1.1 to family Lachnospiraceae and genus NK4A144, which contains no type strains. It assigned Porphyromonadaceae sp. W3.11 to Porphyromonadaceae and genus Porphyromonas_A. Values of 16S rRNA identity and dDDH with respect to type strains were low (table S4). Although more phenotypic data are needed, available evidence supports assignment of genomes to new species or genera. Related publication Hackmann TJ, Zhang B. The phenotype and genotype of fermentative prokaryotes. Sci Adv. 2023 Sep 29;9(39):eadg8687. doi: 10.1126/sciadv.adg8687. Epub 2023 Sep 27. PMID: 37756392; PMCID: PMC10530074.

Hackmann, Timothy↗

DOE OSTI2024

KBase Narrative - Lachnospiraceae sp. C1.1 genome

Hackmann, Timothy↗

DOE OSTI2020

PV Rooftop Database for Puerto Rico (PVRDB-PR)

The National Renewable Energy Laboratory's (NREL) PV Rooftop Database for Puerto Rico (PVRDB-PR) is a lidar-derived, geospatially-resolved dataset of suitable roof surfaces and their PV technical potential for virtually all buildings in Puerto Rico. The dataset can be downloaded at the AWS S3 explorer page. The GitHub documentation page provides a description of the dataset with methods and assumptions. The Puerto Rico Solar-For-All dataset provides Census Tract level estimates of residential low-to-moderate income (LMI) PV rooftop technical potential as well as solar electric bill savings potential for LMI communities at the municipality level.

Array↗

DOE OSTI2022

Sample IEEE123 Bus system for OEDI SI

Time series load and PV data from an IEEE123 bus system. An example electrical system, named the OEDI SI feeder, is used to test the workflow in a co-simulation. The system used is the IEEE123 test system, which is a well studied test system (see link below to IEEE PES Test Feeder), but some modifications were made to it to add some solar power modules and measurements on the system. The aim of this project is to create an easy-to-use platform where various types of analytics can be performed on a wide range of electrical grid datasets. The aim is to establish an open-source library of algorithms that universities, national labs and other developers can contribute to which can be used on both open-source and proprietary grid data to improve the analysis of electrical distribution systems for the grid modeling community. OEDI Systems Integration (SI) is a grid algorithms and data analytics API created to standardize how data is sent between different modules that are run as part of a co-simulation. The readme file included in the S3 bucket provides information about the directory structure and how to use the algorithms. The sensors.json file is used to define the measurement locations.

123 bus↗

DOE OSTI2020

Roles for Countercharge in the Voltage Sensor Domain of Ion Channels

Voltage-gated ion channels share a common structure typified by peripheral, voltage sensor domains. Their S4 segments respond to alteration in membrane potential with translocation coupled to ion permeation through a central pore domain. The mechanisms of gating in these channels have been intensely studied using pioneering methods such as measurement of charge displacement across a membrane, sequencing of genes coding for voltage-gated ion channels, and the development of all-atom molecular dynamics simulations using structural information from prokaryotic and eukaryotic channel proteins. One aspect of this work has been the description of the role of conserved negative countercharges in S1, S2, and S3 transmembrane segments to promote sequential salt-bridge formation with positively charged residues in S4 segments. These interactions facilitate S4 translocation through the lipid bilayer. In this review, we describe functional and computational work investigating the role of these countercharges in S4 translocation, voltage sensor domain hydration, and in diseases resulting from countercharge mutations.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2021

Phenylpropanoid Biosynthesis Gene Expression Precedes Lignin Accumulation During Shoot Development in Lowland and Upland Switchgrass Genotypes

Efficient conversion of lignocellulosic biomass into biofuels is influenced by biomass composition and structure. Lignin and other cell wall phenylpropanoids, such as para -coumaric acid ( p CA) and ferulic acid (FA), reduce cell wall sugar accessibility and hamper biochemical fuel production. Toward identifying the timing and key parameters of cell wall recalcitrance across different switchgrass genotypes, this study measured cell wall composition and lignin biosynthesis gene expression in three switchgrass genotypes, A4 and AP13, representing the lowland ecotype, and VS16, representing the upland ecotype, at three developmental stages [Vegetative 3 (V3), Elongation 4 (E4), and Reproductive 3 (R3)] and three segments (S1–S3) of the E4 stage under greenhouse conditions. A decrease in cell wall digestibility and an increase in phenylpropanoids occur across development. Compared with AP13 and A4, VS16 has significantly less lignin and greater cell wall digestibility at the V3 and E4 stages; however, differences among genotypes diminish by the R3 stage. Gini correlation analysis across all genotypes revealed that lignin and p CA, but also pectin monosaccharide components, show the greatest negative correlations with digestibility. Lignin and p CA accumulation is delayed compared with expression of phenylpropanoid biosynthesis genes, while FA accumulation coincides with expression of these genes. The different cell wall component accumulation profiles and gene expression correlations may have implications for system biology approaches to identify additional gene products with cell wall component synthesis and regulation functions.

09 BIOMASS FUELS↗

DOE OSTI2022

The Astronomy Commons Platform: A Deployable Cloud-based Analysis Platform for Astronomy

Abstract We present a scalable, cloud-based science platform solution designed to enable next-to-the-data analyses of terabyte-scale astronomical tabular data sets. The presented platform is built on Amazon Web Services (over Kubernetes and S3 abstraction layers), utilizes Apache Spark and the Astronomy eXtensions for Spark for parallel data analysis and manipulation, and provides the familiar JupyterHub web-accessible front end for user access. We outline the architecture of the analysis platform, provide implementation details and rationale for (and against) technology choices, verify scalability through strong and weak scaling tests, and demonstrate usability through an example science analysis of data from the Zwicky Transient Facility’s 1Bn+ light-curve catalog. Furthermore, we show how this system enables an end user to iteratively build analyses (in Python) that transparently scale processing with no need for end-user interaction. The system is designed to be deployable by astronomers with moderate cloud engineering knowledge, or (ideally) IT groups. Over the past 3 yr, it has been utilized to build science platforms for the DiRAC Institute, the ZTF partnership, the LSST Solar System Science Collaboration, and the LSST Interdisciplinary Network for Collaboration and Computing, as well as for numerous short-term events (with over 100 simultaneous users). In a live demo instance, the deployment scripts, source code, and cost calculators are accessible. 4 4 http://hub.astronomycommons.org/

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2023

CORPSE model with litter decomposition parameters derived from the LIDET dataset

This is a version of the CORPSE model (Carbon, Organisms, Rhizosphere and Protection in the Soil Environment, Sulman et al. 2014) that uses litter decomposition parameters derived from a modified Monte Carlo simulation using the LIDET litter decomposition dataset (Long-term Intersite Decomposition Experiment Team, Harmon 2013). The code also includes the Baseline parameters, and the eight other best parameter sets identified in a modified Monte Carlo simulation. Related publication:Juice, S.M., Ridgeway, J.R., Hartman, M.D., Parton, W.J., Berardi, D.M., Sulman, B.N., Allen, K.E., & Brzostek, E.R. Reparameterizing litter decomposition using a simplified Monte Carlo method improves litter decay simulated by a microbial model and alters bioenergy soil carbon estimates. Description of files:The folder "Input Files" contains one folder for each LIDET site with data necessary to run the model. Note that "(site)" in the filenames below indicates where the LIDET site code appears (see Table 1 for site codes). Data streams include: CORPSE_full_spinup_litter.csv, CORPSE_full_spinup_rhizo.csv, CORPSE_full_spinup_bulk.csv, litterbag_init_100g_6spp.csv: initial C and N (kg C or N/m2) pool values for each soil layer, the litterbag_init_100_6spp.csv file is for the litterbag layer and is the same file for all sites. All initial C and N files have the same columns (Column - Description - Units) uFastC - Unprotected fast decomposing carbon - kg carbon/m2 uSlowC - Unprotected slow decomposing carbon - kg carbon/m2 uNecroC - Unprotected necromass carbon - kg carbon/m2 pFastC - Protected fast decomposing carbon - kg carbon/m2 pSlowC - Protected slow decomposing carbon - kg carbon/m2 pNecroC - Protected necromass carbon - kg carbon/m2 livingMicrobeC - Carbon in living microbial biomass - kg carbon/m2 uFastN - Unprotected fast decomposing nitrogen - kg nitrogen/m2 uSlowN - Unprotected slow decomposing nitrogen - kg nitrogen/m2 uNecroN - Unprotected necromass nitrogen - kg nitrogen/m2 pFastN - Protected fast decomposing nitrogen - kg nitrogen/m2 pSlowN - Protected slow decomposing nitrogen - kg nitrogen/m2 pNecroN - Protected necromass nitrogen - kg nitrogen/m2 inorganicN - Inorganic nitrogen - kg nitrogen/m2 CO2 - Carbon in carbon dioxide - kg carbon/m2 livingMicrobeN - Nitrogen in living microbial biomass - kg nitrogen/m2 soilT (site) DOY274start.csv: Average daily soil temperature (oC) interpolated from previously calculated monthly values used in DayCent LIDET simulations (Bonan et al., 2013). soilT (site) DOY274start.csv: Average daily soil volumetric water content (VWC) scalar interpolated from previously calculated monthly values used in DayCent LIDET simulations (Bonan et al., 2013). litter production.csv: Average daily litter production values for each site, data sources listed in Table S3 of related publication. litter (site) CN.csv: C:N ratio for each species from LIDET dataset (Table 2, Harmon 2013). (site).csv: Table indicating number of observations for each species decomposed at each site. Instructions: Save the model code ("CORPSE_LIDET.R") and "Input Files" folder in the same folder. Also make a folder for the model output (e.g., "results_Baseline") in the same folder. Set the working directory (setwd) in the model code to the folder with the files saved in step #1. Select the parameter set to use for the litter and litterbag compartments, comment out all other parameter sets. Run code. Output will be saved in the folder made in step 1. Output destination can be changed as necessary in code section called "Running the model." Table 1 LIDET sites and site codes used in model files. Site Code - Site AND - H.J. Andrews Experimental Forest BNZ - Bonanza Creek Experimental Forest BSF - Blodgett Research Forest CDR - Cedar Creek Natural History Area CPR - Central Plains Experimental Range HBR - Hubbard Brook Experimental Forest HFR - Harvard Forest JUN - Juneau KBS - Kellogg Biological Station KNZ - Konza Prairie Research Natural Area NWT - Niwot Ridge/Green Lakes Valley OLY - Olympic National Park OLY Conifer forest SEV - Sevilleta National Wildlife Refuge SMR - Santa Margarita Ecological Reserve UFL - University of Florida VCR - Virginia Coast Reserve Table 2 LIDET species and species codes used in model files (6 common species). Species - Species Code Sugar maple (Acer saccharum) - ACSA Drypetes (Drypetes glauca) - DRGL Red pine (Pinus resinosa) - PIRE Chestnut oak (Quercus prinus) - QUPR Western redcedar (Thuja plicata) - THPL Wheat (Triticum aestivum) - TRAE References:Bonan, G. B., Hartman, M. D., Parton, W. J., & Wieder, W. R. (2013). Evaluating litter decomposition in earth system models with long-term litterbag experiments: an example using the Community Land Model version 4 (CLM4). Global Change Biology, 19(3), 957-974. https://doi.org/https://doi.org/10.1111/gcb.12031 Harmon, M. (2013). LTER Intersite Fine Litter Decomposition Experiment (LIDET), 1990 to 2002. Long-Term Ecological Research. Forest Science Data Bank, Corvallis, OR. [Data set]. Accessed http://andlter.forestry.oregonstate.edu/data/abstract.aspx?dbcode=TD023. https://doi.org/10.6073/pasta/f35f56bea52d78b6a1ecf1952b4889c5. Sulman, B. N., Phillips, R. P., Oishi, A. C., Shevliakova, E., & Pacala, S. W. (2014). Microbe-driven turnover offsets mineral-mediated storage of soil carbon under elevated CO2. Nature Climate Change, 4, 1099 - 1102. https://doi.org/10.1038/nclimate2436

Juice, Stephanie↗