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NASA NTRS2004

Analysis of Electrical Characteristics of Thin Film Photovoltaic Cells

Solar energy is the most abundant form of energy in many terrestrial and extraterrestrial environments. Often in extraterrestrial environments sunlight is the only readily available form of energy. Thus the ability to efficiently harness solar energy is one of the ultimate goals in the design of space power systems. The essential component that converts solar energy into electrical energy in a solar energy based power system is the photovoltaic cell. Traditionally, photovoltaic cells are based on a single crystal silicon absorber. While silicon is a well understood technology and yields high efficiency, there are inherent disadvantages to using single crystal materials. The requirements of weight, large planar surfaces, and high manufacturing costs make large silicon cells prohibitively expensive for use in certain applications. Because of silicon s disadvantages, there is considerable ongoing research into alternative photovoltaic technologies. In particular, thin film photovoltaic technologies exhibit a promising future in space power systems. While they are less mature than silicon, the better radiation hardness, reduced weight, ease of manufacturing, low material cost, and the ability to use virtually any exposed surface as a substrate makes thin film technologies very attractive for space applications. The research group lead by Dr. Hepp has spent several years researching copper indium disulfide as an absorber material for use in thin film photovoltaic cells. While the group has succeeded in developing a single source precursor for CuInS2 as well as a unique method of aerosol assisted chemical vapor deposition, the resulting cells have not achieved adequate efficiencies. While efficiencies of 11 % have been demonstrated with CuInS2 based cells, the cells produced by this group have shown efficiencies of approximately 1 %. Thus, current research efforts are turning towards the analysis of the individual layers of these cells, as well as the junctions between them, to determine the cause of the poor yields. As a student of electrical engineering with some material science background, my role in this research is to develop techniques for analyzing the electrical characteristics of the CuInS2 cells. My first task was to design a shadow mask to be used to place molybdenum contacts under a layer of CuInS;! in order to analyze the contact resistance between the materials. In addition, I have also analyzed evaporated aluminum top contacts and have tested various methods of increasing their thicknesses in order to decrease series resistance. More recently I have worked with other members of the research group in reviving a vertical cold-wall reactor for experimentation with CuInS2 quantum dots. As part of that project, I have improved the design for a variable frequency and pulse width square wave generator to be used in driving the precursor injection process. My task throughout the remainder of my tenure is to continue to analyze and develop tools for the analysis of electrical properties of the CuInS2 cells with the ultimate goal of discovering ways to improve the efficiency of our photovoltaic cells. Traditionally, photovoltaic cells are based on a single crystal silicon absorber. While The research group lead by Dr. Hepp has spent several years researching copper indium

Kasick, Michael P.↗
NASA NTRSDate not supplied

The Stratospheric Aerosol and Gas Experiment (SAGE) IV

Atmospheric composition continues to be a key Earth science focus for international space agencies and research organizations. The target constituent varies depending upon the topic area of interest such as monitoring the ozone layer and the efficacy of the Montreal Protocol, assessing the influence of volcanic eruptions and large wildfires on the atmosphere, improving our knowledge of transport processes, or evaluating the impacts of greenhouse gases on climate. Additionally, there is a growing need for measurement continuity of many trace gases both from an inability to properly assess long-term trends without it as well as the reliance of reanalyses and chemistry climate models on observational input. To this end, proper continuity requires continuous and overlapping multi-instrument data records of which spaceborne measurements are a critical component. However, most current satellite systems are well beyond their expected lifetimes, and so we look toward the future to develop the Stratospheric Aerosol and Gas Experiment (SAGE) IV as a future mission concept. Enabled by the NASA Earth Science Technology Office’s (ESTO) Instrument Incubator Program, the SAGE IV Pathfinder project developed and validated a prototype demonstration that paves the way for a future SAGE IV spaceflight mission. Utilizing solar occultation imaging, SAGE IV will be capable of measuring key species in the stratosphere and upper troposphere with the same quality as previous SAGE instruments but with greatly improved pointing knowledge and extensibility to new science targets. The precision and stability of a well-designed occultation instrument can act as a calibration reference standard for other spaceborne instruments, ensuring proper continuity of the desired science targets. Furthermore, current technological advancements allow SAGE IV to fit within a CubeSat framework and make use of commercial hardware, significantly reducing the size and cost when compared with traditional missions and enabling sustainability of future measurements.

Robert Damadeo↗
DOE OSTI2025

Turbo‐charging crop improvement: harnessing multiplex editing for polygenic trait engineering and beyond

Multiplex CRISPR editing has emerged as a transformative platform for plant genome engineering, enabling the simultaneous targeting of multiple genes, regulatory elements, or chromosomal regions. This approach is effective for dissecting gene family functions, addressing genetic redundancy, engineering polygenic traits, and accelerating trait stacking and de novo domestication. Its applications now extend beyond standard gene knockouts to include epigenetic and transcriptional regulation, chromosomal engineering, and transgene‐free editing. These capabilities are advancing crop improvement not only in annual species but also in more complex systems such as polyploids, undomesticated wild relatives, and species with long generation times. At the same time, multiplex editing presents technical challenges, including complex construct design and the need for robust, scalable mutation detection. We discuss current toolkits and recent innovations in vector architecture, such as promoter and scaffold engineering, that streamline workflows and enhance editing efficiency. High‐throughput sequencing technologies, including long‐read platforms, are improving the resolution of complex editing outcomes such as structural rearrangements—often missed by standard genotyping—when targeting repetitive or tandemly spaced loci. To fully realize the potential of multiplex genome engineering, there is growing demand for user‐friendly, synthetic biology‐compatible, and scalable computational workflows for gRNA design, construct assembly, and mutation analysis. Experimentally validated inducible or tissue‐specific promoters are also highly desirable for achieving spatiotemporal control. As these tools continue to evolve, multiplex CRISPR editing is poised to become a foundational technology of next‐generation crop improvement to address challenges in agriculture, sustainability, and climate resilience.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Influence of Powder Characteristics and Processing Methods on Creep Performance of Powder Metallurgy Hot Isostatic Pressed SS316

The U.S. nuclear energy expansion goals are driving the demand for manufacturing routes that can rapidly produce large, complex, near net shape components. Powder metallurgy hot isostatic pressing (PM HIP) is an advanced manufacturing technique that can be economically scaled-up, while alleviating the supply chain challenges that forging and casting face in terms of cost and lead time constraints. This makes PM-HIP a viable technology to aid and accelerate large-scale part manufacturing for nuclear applications. However, large-scale qualification and deployment of this technology require a thorough understanding of the influence of powder feedstock quality, powder handling history, hot isostatic pressing (HIP) parameters, and subsequent heat treatment on microstructural evolution and elevated temperature mechanical performance. The present work focusses on 316 austenitic stainless steel (SS316) which is most commonly used in high temperature environments for nuclear applications Results from this study show that PM HIPed SS316 meets ASME tensile requirements at room temperature and at elevated temperature. However, creep performance of PM-HIPed SS316 remains inferior to its wrought counterpart, demonstrating that tensile performance alone is not a reliable metric for long duration high temperature integrity. Further, this report delineates powder derived microstructural features that govern creep damage, with key evidences pointing to “microstructural inheritance” from gas atomized powder feedstocks. Commercial SS316 powders of varying chemical compositions and recycling histories were studies, and the results showed large differences in elemental segregation, oxide surface layers and secondary phase distributions. Multi-scale characterization revealed segregation of chromium, molybdenum, manganese and silicon at the boundaries and the precipitation of manganese-, silicon-, and molybdenum-oxides. During HIP consolidation, these surface oxides transform into decorated prior particle boundaries (PPBs) and grain boundary inclusions that persist through conventional post-HIP solution annealing treatment. The retained oxides in post-HIP microstructures were found to influence grain growth, precipitation behavior, and ultimately creep cavitation and fracture. Such post-HIP heat treatments are therefore limited by a complex trade-off between grain growth, and oxide coarsening which aggravate creep damage by acting as nucleation sites for cavities. The objective of this work is to establish an integrated processing–structure–property framework for PM-HIP 316 stainless steel by investigating the influence of powder feedstock characteristics in pre- and post-HIP processing as well as to understand the significance of post-HIP heat treatment on microstructural evolution and creep properties. The results from this report emphasize the significance of powder feedstock integrity in improving creep performance of PM-HIPed SS316, by highlight the effect of rapid solidification, elemental segregation, oxide formation and powder recycling on microstructural defect inheritance following HIP consolidation. Rather than considering HIP processing, solution annealing, and mechanical performance independently, this report treats powder production, HIP consolidation, post-HIP thermal processing, and creep deformation as interconnected stages within a continuous metallurgical process. The resulting framework will provide a scientific basis for developing feedstock engineering strategies capable of improving long-term reliability of PM-HIP stainless steels and accelerating their qualification for advanced nuclear applications.

Ajjarapu, Pavan [Oak Ridge National Laboratory (OR↗
NASA NTRSDate not supplied

Continuing Global SO2 Data Record from OMI and SNPP/OMPS to JPSS-1/NOAA-20/OMPS

Since 2004, the Ozone Monitoring Instrument (OMI) aboard NASA's Earth Observing System (EOS) Aura spacecraft has been providing global observations that help to constrain the sources, transport, and environmental impacts of anthropogonic and volcanic SO2. The OMI SO2 data record is now being continued with the NASA/NOAA Suomi National Polar-orbiting Partnership (SNPP)/Ozone Mapping and Profiler Suite (OMPS) launched in 2011. Both OMI and SNPP/OMPS SO2 products are produced with the Goddard principal component analysis (PCA) based spectral fitting algorithm. This data-driven technique inherently accounts for various instrumental factors and geophysical interferences, leading to high-quality, consistent SO2 retrievals between OMI and SNPP/OMPS, despite coarser spectral (~0.5 nm vs. ~1 nm) and spatial (13  24 km2 vs. 50  50 km2 at nadir) resolution for the latter. In this presentation, we describe our effort to continue the long-term SO2 climate data record using measurements from the Joint Polar Satellite System (JPSS)-1/NOAA-20 (N20)/OMPS. Launched in 2017, the N20/OMPS is a follow-on for SNPP/OMPS but features a spatial resolution (17  13 km2) that is comparable with OMI. We will discuss our progress implementing the PCA SO2 algorithm with N20/OMPS, especially algorithmic improvements to further reduce retrieval noise and bias for large volcanic eruptions. We will present examples for both continuously emitting sources (e.g., power plants in India and oil/gas fields in the Middle East) and volcanic eruptions (e.g., Raikoke in 2019). We will also compare N20/OMPS SO2 retrievals with OMI and SNPP/OMPS, as well as other instruments such as the ESA Copernicus Sentinel-5 Precursor (S5P)/TROPOspheric Monitoring Instrument (TROPOMI). To assess the ability of N20/OMPS to monitor and quantify SO2 sources, we will run the level 2 retrievals through a top-down emission algorithm to estimate the SO2 emission strengths for a number of point sources. Finally, we will outline our plan for further algorithm refinement and public data release.

SO2↗
NASA NTRS2009

Potential for Monitoring Snow Cover in Boreal Forests by Combining MODIS Snow Cover and AMSR-E SWE Maps

Monitoring of snow cover extent and snow water equivalent (SWE) in boreal forests is important for determining the amount of potential runoff and beginning date of snowmelt. The great expanse of the boreal forest necessitates the use of satellite measurements to monitor snow cover. Snow cover in the boreal forest can be mapped with either the Moderate Resolution Imaging Spectroradiometer (MODIS) or the Advanced Microwave Scanning Radiometer for EOS (AMSR-E) microwave instrument. The extent of snow cover is estimated from the MODIS data and SWE is estimated from the AMSR-E. Environmental limitations affect both sensors in different ways to limit their ability to detect snow in some situations. Forest density, snow wetness, and snow depth are factors that limit the effectiveness of both sensors for snow detection. Cloud cover is a significant hindrance to monitoring snow cover extent Using MODIS but is not a hindrance to the use of the AMSR-E. These limitations could be mitigated by combining MODIS and AMSR-E data to allow for improved interpretation of snow cover extent and SWE on a daily basis and provide temporal continuity of snow mapping across the boreal forest regions in Canada. The purpose of this study is to investigate if temporal monitoring of snow cover using a combination of MODIS and AMSR-E data could yield a better interpretation of changing snow cover conditions. The MODIS snow mapping algorithm is based on snow detection using the Normalized Difference Snow Index (NDSI) and the Normalized Difference Vegetation Index (NDVI) to enhance snow detection in dense vegetation. (Other spectral threshold tests are also used to map snow using MODIS.) Snow cover under a forest canopy may have an effect on the NDVI thus we use the NDVI in snow detection. A MODIS snow fraction product is also generated but not used in this study. In this study the NDSI and NDVI components of the snow mapping algorithm were calculated and analyzed to determine how they changed through the seasons. A blended snow product, the Air Force Weather Agency and NASA (ANSA) snow algorithm and product has recently been developed. The ANSA algorithm blends the MODIS snow cover and AMSR-E SWE products into a single snow product that has been shown to improve the performance of snow cover mapping. In this study components of the ANSA snow algorithm are used along with additional MODIS data to monitor daily changes in snow cover over the period of 1 February to 30 June 2008.

Riggs, George A.↗
NASA NTRS2008

Ares I and Ares V First Stage - Powering Exploration

The National Aeronautics and Space Administration (NASA) Ares Projects Office (APO) is continuing to make progress toward the final design of the first stage propulsion system for the Ares I crew launch vehicle and the Ares V cargo launch vehicle. Ares I and Ares V will provide the space launch capabilities needed to fulfill NASA' s exploration strategy of sending human beings to the Moon, Mars, and beyond. As primary propulsion for the Ares launch vehicles, the Space Shuttle-derived Reusable Solid Rocket Motor (RSRM) is one of the first and most important components to be tested. The first flight of Ares I, called Ares I-X, will occur in April 2009, with booster integration to begin at Kennedy Space Center (KSC) by autumn 2008. The Ares I-X flight will use a combination of flight and simulation hardware to obtain data on controlling the long and narrow crew launch vehicle configuration. The test will use a four-segment RSRM from the Shuttle inventory and a fifth spacer segment to simulate the size and weight of the operational five-segment motor to be used on later flights. The upper stage, Orion crew exploration vehicle, and launch abort system will all be replaced with simulator hardware. Manufacturing work has begun on the spacer segment, as well as the new forward hardware for the booster. Atlas V avionics will be adapted to control Ares I-X' s first stage. That hardware is undergoing hardware-in-the-loop testing in a contractor-provided systems integration laboratory (SIL); a critical design review (CDR) was completed in December 2007. Drogue and main parachute drop tests have also been conducted successfully at Yuma Proving Grounds, allowing the First Stage team to begin fabricating parachutes for Ares I-X. The Ares I-X flight test will be the first flight test for the parachutes. A series of preliminary design technical interchange meetings is being conducted prior to the Ares I-X CDR in January 2007 to ensure readiness for the flight. Much of the First Stage activity in 2007 has focused on a series of preliminary design (PDR) activities associated with each booster subsystem. These events will culminate in a formal preliminary design review in 2008, where subsystems and component specifications will be developed and associated analyses and drawings will be evaluated for technical adequacy. The first stage also has been undergoing a series of trade studies to determine means for upgrading booster performance and reducing operational costs. Performance improvement studies have included changing from polybutadiene acrylonitrile (PBAN) propellant to hydroxyl-terminated polybutadiene (HTPB); replacing aluminum with composite motor casings; and optimizing or upgrading the propellant grain and nozzle structures. Some or all of these changes could result in a block upgrade to the Ares I first stage, after becoming the standard configuration for the Ares V. The cost reduction studies included a change from reusable or recoverable boosters to completely expendable boosters; changing from hydrazine-powered to more environmentally friendly electrohydrostatic actuators (EHAs) on the thrust vector control (TVC) system; and changing the location of the separation plane to reduce the likelihood of recontact upon booster separation. The expendability trade study resulted in a decision to keep the recoverable boosters, as the new hardware costs significantly outweighed the potential operational cost savings due to reduced ground operations. Likewise, due to cost considerations, the team continued using existing hydrazine-powered actuators for the TVC system. The separation plane location for Ares I is still being studied, with results to be announced in 2008. In short, the Ares launch vehicles' first stage is building upon NASA's close familiarity with this Shuttle-derived system, while continuing to seek out improvements for long-term exploration.

Priskos, Alex S.↗
DOE OSTI2025

Modeling the Air Quality Impacts of Future Energy Scenarios

Many greenhouse gas (GHG) emission reduction measures achieve simultaneous reductions in air pollutants. Human-Earth system models can estimate such emission changes in the energy system but using them in chemistry-transport models (CTMs) to study their air quality impacts involves resource-intensive emissions processing. This is greatly simplified by an emissions scaling approach linking state-level emissions estimated by a human-Earth system model to a CTM. A scenario continuing pre-2022 energy policy in the U.S. to 2050 shows widespread air quality improvements over the 2015 baseline from SO 2 and NO x emission reductions of 50–80% from electricity generation and light-duty vehicles. Scenarios of GHG mitigation and vehicle electrification at the state and national level add further benefits. However, PM 2.5 increases from increased use of wood heating and bioenergy suggest that additional PM 2.5 management may be needed when using biofuels. In conclusion, this approach helps assess multiple future energy scenarios efficiently without sacrificing chemical detail in the air quality simulations.

air quality↗
DOE OSTI2021

Impact of a New Cloud Microphysics Parameterization on the Simulations of Mesoscale Convective Systems in E3SM

Abstract Mesoscale convective systems (MCSs) are one of the most climatically significant forms of convection because of their large role in water and energy cycles. The mesoscale features associated with MCS are difficult to represent in climate models because the relevant dynamics and physics are absent or poorly represented with coarse model resolution (∼100 km). Using a regionally refined model (RRM) with 0.25° grid spacing embedded in the Energy Exascale Earth System Model (E3SM), we explore the impact of cloud microphysics parameterizations on the simulation of precipitation, particularly MCS precipitation over the contiguous United States. The Predicted Particle Properties (P3) cloud microphysics scheme has been modified and implemented into E3SM to overcome the limitations of the default Morrison and Gettelman (MG2) scheme in which rimed precipitating ice particles (graupel/hail) are absent and frozen particles are artificially partitioned into cloud ice and snow. We show that P3 improves the simulation of precipitation statistics including frequency distribution compared with MG2 with a limited effect on the diurnal cycle. P3 predicts higher hourly rain rates, resulting in 20% more MCSs and a higher total MCS precipitation (4.4%) compared to MG2, agreeing better with observations. The improvements with P3 mainly result from improved representations of ice microphysics, which not only produces higher rain rates through melting but also leads to a stronger large‐scale ascending motion by releasing more latent heating. This study suggests that improving microphysics parameterization is important for simulating MCS precipitation as future climate model resolutions continue to increase.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Neutral gas analysis for JET DT operation

Neutral gas analysis, the measurement and evaluation of total and partial pressures, is a key technique to study the impact of neutral gas dynamics on retention, recycling and release processes of fuel or impurity species in fusion devices. At JET, the experiment closest to ITER in terms of operating parameters and size, various detectors and techniques for partial pressure and total pressure measurements are deployed together to characterise neutral gas dynamics during and after plasma operation on various toroidal and poloidal locations. An extensive modification of JET's sub-divertor neutral gas diagnostic system aims at retaining and extending established measurement capabilities in the forthcoming Deuterium-Tritium (DT) experiments (DTE2). To achieve DT compatibility, a separation of radiation-sensitive electronics from the sensor and adequate radiation shielding is required, as well as utilisation of a DT compatible differential pumping system with adjustable throughput to account for the strong pressure variation in the sub-divertor region. Finally, the sub-divertor neutral gas diagnostic will be equipped with multiple Residual Gas Analysers (RGAs), utilising quadrupole mass spectrometry and electrostatic ion-trap-principles, all operating with remote electronics located behind the biological radiation shield. These RGAs will record data in a fast selected discrete mass mode during plasma pulses (cycle ~2 s) and will automatically switch back to continuous data recording (cycle ~100 s) afterwards. They will be complemented by a newly improved Penning gauge spectroscopy configuration in particular supporting the He and D 2 separation relevant for DT operation. The distance between these devices and their associated control unit is typically 15m . A newly developed RGA with a cable length of 80 m, compatible with the ITER environment, will also be employed for the first time. Finally, this set-up and its operation in DTE2 will provide vital input to the development of the ITER divertor RGA in the most relevant environment currently available.

47 OTHER INSTRUMENTATION↗
DOE OSTI2023

Coordinate-Based Seismic Interpolation in Irregular Land Survey: A Deep Internal Learning Approach

Physical and budget constraints often result in irregular sampling, which complicates accurate subsurface imaging. Preprocessing approaches, such as missing trace or shot interpolation, are typically employed to enhance seismic data in such cases. Recently, deep learning has been used to address the trace interpolation problem at the expense of large amounts of training data to adequately represent typical seismic events. Nonetheless, most research in this area has focused on trace reconstruction, with little attention having been devoted to shot interpolation. Furthermore, existing methods assume regularly spaced receivers/sources failing in approximating seismic data from real (irregular) surveys. This work presents a novel shot gather interpolation approach which uses a continuous coordinate-based representation of the acquired seismic wavefield parameterized by a neural network. The proposed unsupervised approach, which we call coordinate-based seismic interpolation (CoBSI), enables the prediction of specific seismic characteristics in irregular land surveys without using external data during neural network training. Importantly, experimental results on real and synthetic 3-D data validate the ability of the proposed method to estimate continuous smooth seismic events in the time-space and frequency-wavenumber domains, improving sparsity or low-rank-based interpolation methods.

58 GEOSCIENCES↗
DOE OSTI2022

“Gray Zone” Simulations Using a Three-Dimensional Planetary Boundary Layer Parameterization in the Weather Research and Forecasting Model

Abstract Generating accurate weather forecasts of planetary boundary layer (PBL) properties is challenging in many geographical regions, oftentimes due to complex topography or horizontal variability in, for example, land characteristics. While recent advances in high-performance computing platforms have led to an increase in the spatial resolution of numerical weather prediction (NWP) models, the horizontal gridcell spacing (Δ x ) of many regional-scale NWP models currently fall within or are beginning to approach the gray zone (i.e., Δ x ≈ 100–1000 m). At these gridcell spacings, three-dimensional (3D) effects are important, as the most energetic turbulent eddies are neither fully parameterized (as in traditional mesoscale simulations) nor fully resolved [as in traditional large-eddy simulations (LES)]. In light of this modeling challenge, we have implemented a 3D PBL parameterization for high-resolution mesoscale simulations using the Weather Research and Forecasting Model. The PBL scheme, which is based on the algebraic model developed by Mellor and Yamada, accounts for the 3D effects of turbulence by calculating explicitly the momentum, heat, and moisture flux divergences in addition to the turbulent kinetic energy. In this study, we present results from idealized simulations in the gray zone that illustrate the benefit of using a fully consistent turbulence closure framework under convective conditions. While the 3D PBL scheme reproduces the evolution of convective features more appropriately than the traditional 1D PBL scheme, we highlight the need to improve the turbulent length scale formulation. Significance Statement The spatial resolution of weather models continues to increase at a rapid rate in accordance with the enhancement of computing power. As a result, smaller-scale atmospheric features become more explicitly resolved. However, most numerical models still ignore the impact of horizontal weather variations on boundary layer flows, which becomes more important at these smaller spatial scales. To address this issue, we have implemented a new modeling approach, using fundamental principles, which accounts for horizontal variability. Our results show that including three-dimensional effects of turbulence is necessary to achieve realistic boundary layer characteristics. This novel technique may be useful for many applications including complex terrain flows, pollutant dispersion, and surface–atmosphere interaction studies.

Juliano, Timothy W.↗
DOE OSTI2024

AMReX and pyAMReX: Looking beyond the exascale computing project

AMReX is a software framework for the development of block-structured mesh applications with adaptive mesh refinement (AMR). AMReX was initially developed and supported by the AMReX Co-Design Center as part of the U.S. DOE Exascale Computing Project (ECP), and is continuing to grow post-ECP. In addition to adding new functionality and performance improvements to the core AMReX framework, we have also developed a Python binding, pyAMReX, that provides a bridge between AMReX-based application codes and the data science ecosystem. pyAMReX provides zero-copy application GPU data access for AI/ML, in situ analysis and application coupling, and enables rapid, massively parallel prototyping. In this paper we review the overall functionality of AMReX and pyAMReX, focusing on new developments, new functionality, and optimizations of key operations. We also summarize capabilities of ECP projects that used AMReX and provide an overview of new, non-ECP applications.

Myers, Andrew↗
DOE OSTI2026

Constructing a High‐Resolution Aftershock Catalog for the 2017 Mw 8.2 Tehuantepec Earthquake Sequence Using a Machine Learning–Based Workflow

The 8 September 2017 Mw 8.2 Tehuantepec earthquake was the largest instrumentally recorded normal‐faulting earthquake in Mexico. The mainshock occurred offshore within the Tehuantepec seismic gap, generating >30,000 aftershocks in the following year. We applied an open‐source, machine learning (ML)–assisted workflow to construct a high‐resolution aftershock catalog using data from temporary and permanent seismic networks in southern Mexico. The workflow integrates PhaseNet for phase detection; GaMMA for phase association; and VELEST, HypoInverse, and HypoDD for velocity modeling and relocation. We processed seven months of continuous waveform data from 29 broadband stations, including a temporary rapid‐response deployment that improved station coverage of the offshore rupture zone. To evaluate performance, we compared our results against analyst‐reviewed picks and event locations from the Servicio Sismológico Nacional catalog. The resulting catalog contains 11,374 relocated earthquakes and represents the most comprehensive published dataset for this sequence, incorporating the first full use of the temporary network. Relocated hypocenters show improved depth control and align well with the Slab2.0 subduction geometry, revealing clearer separation between offshore slab events and onshore crustal seismicity. This study demonstrates that combining ML‐based detection with established methods provides a scalable and reproducible approach for constructing high‐quality earthquake catalogs in tectonically complex environments and offers practical guidance for adapting similar workflows to other earthquake sequences.

Garcia, Marc [The University of Texas at El Paso, ↗
DOE OSTI2021

Modeling, Design, and Testing of a Novel Biphasic Solvent-Enabled Absorption System for Post-Combustion Carbon Capture

A novel absorption process enabled by a new class of biphasic solvents for post-combustion carbon capture (BiCAP) is presently being developed at the University of Illinois at Urbana-Champaign. The new biphasic solvents are water-lean solvent blends that develop dual liquid phases with the absorbed CO2 highly enriched in one of the phases. These solvents are superior in CO2 capacity and have high thermal and oxidative stability compared to MEA. The BiCAP technology features a unique process configuration of multi-stage CO2 absorption and liquid–liquid phase separation during CO2 absorption, allowing continual separation and removal of the CO2-enriched liquid phase for maintaining low solvent viscosity and a fast absorption rate. A 40 kWe-scale skid has recently been constructed at the Abbott Power Plant in Champaign, IL, to test the BiCAP solvent in continuous operation with a slipstream of real post-combustion flue gas. Along with improvements in the stripping configuration including cold feed bypass, the process is expected to achieve a minimum reboiler duty of 2,210 kJ/kg CO2 captured at a stripper operating pressure of 6 bar.

20 FOSSIL-FUELED POWER PLANTS↗
DOE OSTI2021

Analysis of Microbial Communities as Indicators of Microbially Induced Corrosion Potential in Stainless Steel Piping

The 200 West Area Pump-and-Treat (200W P&T) facility is part of the final remedy decision for the 200 ZP-1 Operable Unit (OU) and is the interim remedial action for 200-UP-1 OU at the Hanford Site. The facility also treats water from other sources across the site, including 200-DV-1 OU perched water, groundwater from the 200-BP-5 OU, and leachate from the Environmental Restoration Disposal Facility. The facility originally operated a biological treatment process for nitrate that resulted in systemic biofouling and loss of injection well capacity, requiring frequent and costly well rehabilitation. Biofouling has also contributed to corrosion of welds and pitting of stainless-steel piping at the facility. The biological treatment for nitrate was suspended at the end of 2019 calendar year to eliminate negative impacts from biological treatments which improve facility operations and treatment of carbon tetrachloride. Evaluation of the 200W P&T facility’s response to operational improvements, specifically the suspension of the biological treatment, provides information on the potential for continued microbially induced corrosion (MIC). To this end, historical data sets (2015-2017) were compared with molecular biological analyses of 200W P&T water samples from 2018-2020 to quantify trends in total microbial biomass and specific microbial indicator species (iron, sulfate, nitrate, and manganese reducing bacteria) known to cause and enhance metal corrosion. Results showed unequivocal reductions (in excess of 90%) in all microbiological indicators measured following the suspension of the biological treatment. Although abundance measurements for all corrosive MIC populations initially declined after removal of the treatment system, nitrate-reducing bacteria (nirK) and sulfate-reducing bacteria remain highly abundant in the system, an order of magnitude higher than the number of cells present prior to the removal of the biological treatment system [5.14e (1.4) cells/L, vs. 1.14e (0.07) cells/L, reported as average (±SD) for n=3]. In addition, there was an unexpected inflection point in 2019 where all microbial indicators increased unexpectedly in abundance in 2020 by as much as two orders of magnitude compared to 2019 samples. Given the limited number of water samples available for this analysis, the cause of the increase in microbial indicators post-biological treatment has not yet been determined. Systematic sampling of the facility for continued monitoring is recommended so that the responses of MIC populations can be evaluated to ensure that further corrosion is avoided, allowing the 200W P&T facility to operate safely and efficiently at design capacity.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2022

SAMMY Modernization Efforts [Slides]

This presentation discusses SAMMY releases and states that all of its dependencies are freely available. It also discusses version control and confirms that versions will be tagged as needed. Additionally, SAMMY program flow is discussed and states that the program flow was improved to allow for easier external access to SAMMY routines. Modernization of SAMMY is continued with the Doppler and Resolution broadening modules. The presentation concludes by stating that documentation updates are in progress.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2023

PRIMED for the Future: Purposing Raw Intake for Machine Learning-Enabled Detection (Final Report)

The COVID-19 pandemic demonstrated how a novel, elusive, and diffuse biological threat can engender uncertainty and misinformation, and it underscored the need for flexible analytical modalities agnostic to the identity of biological material. Yet even before the pandemic, recognition of the limitations of the current, list-based approach, which focuses on known pathogens and biotoxins, and of the importance of agent-agnostic biodetection was growing within the biosecurity community. In a 2018 report on “Biodefense in the Age of Synthetic Biology,” for example, the National Academy of Sciences stated that “an overreliance on the Select Agent List is a systematic weakness affecting many aspects of the United States’ current biodefense mitigation capability”. More recently, a group of biodefense researchers proposed the identification and adoption of “bioagent-agnostic signatures (BASs)” as a way of detecting and characterizing not only existing agents but also novel ones, an approach they believe will “enable a more flexible and resilient biodefense posture”. Indeed, the future of biodetection requires us to begin developing novel analytics that can identify anomalies and/or characteristics that indicate a potential threat, whether known or unknown, without looking for a specific signature that has been identified previously. To assess potential threats more rapidly, it is critical to develop agnostic artificial intelligence (AI)/machine learning (ML) systems that can be employed for real-time assessment of the nature and source of a perturbation. Such systems should be multi scale and multi-dimensional, integrating sensor data from a range of biological, chemical, and physical application spaces. Emerging deep learning (DL) models demonstrate exceptional promise for identification of discriminatory features within multi-dimensional datasets. DL models have the capacity to recognize and encode highly complex patterns in a wide range of input data modalities, including images, text, and biological/chemical/physical spectra. As such, they can execute a wide range of assessments and determinations that have traditionally required a human operator. The promise of advances in DL is apparent in the realm of human health and medicine. DL models have been validated for evaluating a variety of clinical threats to human health in a range of contexts, including infection and cancer, and they demonstrated improved performance in predicting stroke relative to human neurologists in some categories of data. Continuously evolving advances in AI/ML are expected to support more efficient evaluation of raw sequence, spectroscopy, and spectrometry data. For instance, recent advances and deployment of large language models (LLM) such as Generative Pre training Transformer (GPT) and Bidirectional Encoder Representations from Transformers (BERT) have already motivated application of these models for biological function prediction. As frameworks such as LLMs become larger and more complex in their representations, their capacity to serve as pre-trained models that can be fine-tuned for biological/biodetection purposes will similarly be amplified. While existing and emerging AI/ML have found broad applicability and use cases in the clinical sciences, development for environmental evaluation and biodetection has been limited. Functionalizing such capabilities for this purpose requires an understanding of the existing technical landscape and how the respective tools and algorithms are currently being employed. This landscape awareness then allows an assessment of the current practical capabilities of existing models and the anticipated requirements and development efforts that will be needed to adapt available algorithms for biodetection applications relevant to DHS. Leveraging expertise in biodetection, ML, and operational biodetection, the effort described in this report is comprised of a systematic landscape assessment (Subtask 2.1), comparative evaluation (Subtask 2.2), and formulation of a value proposition (Subtask 2.3) for the prospect of ML-enabled, agnostic biodetection from raw, or minimally-processed, datasets.

59 BASIC BIOLOGICAL SCIENCES↗

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