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UMap: An application-oriented user level memory mapping library

Exploiting the prominent role of complex memories in exascale node architecture, the UMap page fault handler offers new capabilities to access large memory-mapped data sets directly. UMap provides flexible configuration options to customize page handling to each application, including analysis of massive observational and simulation data sets. The high-performance design features I/O decoupling, dynamic load balancing, and application-level controls. Page faults triggered by application threads and processes accessing data mapped to a UMapp’ed region are handled via the Linux userfaultfd protocol, an asynchronous message-oriented kernel-user communication mechanism that avoids the context switch penalty of traditional signal fault handlers. UMap is fully open source. In this paper, we give an overview of the UMap library architecture, its extensible plugin architecture, and the use/performance of UMap in emerging heterogeneous memory hierarchies such as near-node Non-volatile Memory (NVM) and network attached memories. We highlight new capabilities in two pagefault management plugins, the NetworkStore and SparseStore. We demonstrate the integration between UMap and multiple ECP products including Caliper, Metall, ZFP, Mochi, and Ripples.

97 MATHEMATICS AND COMPUTING↗

Data Summary Report for Soil and Slab Sampling at Former Building 175

Lawrence Livermore National Security (LLNS) and the U.S. Department of Energy (DOE) are in the process of returning the area where Building 175 existed to beneficial use for future site development. The completed assessment will help LLNS and DOE to outline project scope and costs associated with the removal, characterization, and disposal of materials generated when the concrete slab for the former building and any associated remaining subsurface structures are demolished. Slab removal under the Transition & Disposition process requires full screening for potential site contamination to determine appropriate future land use. Borehole locations and depths were designed to correctly assess DOE’s future liability for characterizing residual vadose zone contamination. If a residual source area is identified at depth, DOE will need to evaluate the additional cost of future subsurface cleanup (with limited access) due to planned development. This is necessary to leave the location in a “ready-to-build” status. As a result, Phase II sampling requirements may in some cases extend beyond the construction zone required for slab removal to ensure the necessary due diligence. To avoid the potential spread of contamination and/or the creation of an environmental release by impacting the integrity of known contamination areas (e.g., pit within room 102 and the east to west boundary seam), these locations were not sampled for this project. During demolition of the slab, these locations and their underlying soils should be further evaluated. The assessment was conducted in accord with the May 2022 Lawrence Livermore National Laboratory Former Building 175 Assessment Soil Sampling and Analysis Plan / Quality Assurance Plan. Thirty-six, direct-push borings were advanced, along with collecting concrete (where present) and soil samples for laboratory analyses. Thirty-two of the borings were advanced to a depth of 25 feet below ground (bgs), and four borings were advanced to a depth of 55 feet bgs. The concrete slab for former Building 175 ranged from approximately 8 inches to over two feet in thickness. Soils encountered during the investigation consisted primarily of clayey silt, with interbeds of sandy gravel and silty sand to the total depth explored of 55 feet bgs. Field photoionization detector readings – checking for volatile organic compound (VOC) vapors in soils, ranged from zero (0) to a peak of 33 parts per million at 25 feet bgs in boring PC-B175- 028. The cause for the peak reading is unknown, however, no visually discolored or odorous soils were encountered during the assessment and all VOC results were below Lawrence Livermore National Laboratory’s (LLNL’s) Soil Screening and Management Plan (SSMP) soil screening levels (SSL). Groundwater was not encountered during the assessment and is expected to occur at roughly 65 feet bgs in the project area. No metals were detected at concentrations of concern in the concrete core samples collected and analyzed. Low gross alpha and beta activity concentrations were detected in the concrete core samples collected and analyzed. The detected activity concentrations would appear to be from naturally occurring radioactive isotopes, i.e., potassium 40, present in the raw materials used to make concrete and not artificially added. Tritium was not detected above the testing laboratory's Method Detection Limit (MDL) in any of the concrete core samples collected and analyzed. The radioactive isotopes - actinium 228, bismuth 214, lead 212, lead 214, potassium 40, radium 226, radium 228, and thallium 208, were detected at low activity concentrations in the concrete core samples analyzed. Based on the detected activity concentrations, the isotopes would appear to be naturally occurring in the raw materials used to make concrete and not artificially added. Acetone – a common laboratory contaminant, was detected in nine of the soil samples collected and analyzed. Concentrations of two other volatile organic compounds (benzene and tetrachloroethene) were detected in five of the soil samples analyzed. All concentrations were below SSLs. Total petroleum hydrocarbons as diesel range organics were detected at 43.9 milligrams per kilogram (mg/kg) in the 20-foot bgs duplicate sample from boring PC-B175-007. Total petroleum hydrocarbons as motor oil were detected at 7.20 mg/kg in the 15-foot bgs routine sample from boring PC-B175-001, and at 20.7 mg/kg in the 20-foot bgs duplicate sample from boring PC-B175-007. The SSL for diesel-range TPHs is 260 mg/kg. LLNL does not have an SSL for motor oil range TPHs. No indications of a release, e.g., visibly stained, or odorous soil, were present at the boring locations. No samples collected contained polychlorinated biphenyls (PCBs) in concentrations above the laboratory reporting limit. Arsenic was detected above its SSL of 8.51 mg/kg in the 25-foot bgs routine sample collected from boring PC-B175-023. Nickel was detected above its SSL of 86.0 mg/kg in the 10-foot bgs routine sample from boing PC-B175-005, the 20-foot bgs routine sample from boring PC-B175-025, and the 25-foot bgs routine sample from boring PC-B175-034. The detected concentrations, however, were well below ten times (10x) their respective STLCs. Gross alpha and/or gross beta were detected above their respective SSL activity concentrations in three routine soil samples. Retesting (two per sample) of the samples showed that the initial reported activity concentrations were anomalous. Tritium was not detected in any of the routine or duplicate soil samples collected and analyzed during the investigation. The radioactive isotopes - actinium 228, bismuth 212 and 214, lead 212 and 214, potassium 40, radium 224, 226 and 228, thallium 208, thorium 234 and uranium, were detected at low activity concentrations in the soil samples analyzed for radioactive constituents. The detected isotopes and their associated activity concentrations are typical of those naturally occurring in the marine-type sedimentary deposits underlying the Livermore Valley. No radiological controls are necessary for the soil evaluated.

54 ENVIRONMENTAL SCIENCES↗

Model bias in simulating major chemical components of PM 2.5 in China

High concentrations of PM 2.5 (particulate matter with an aerodynamic diameter less than 2.5 µm) in China have caused severe visibility degradation. Accurate simulations of PM 2.5 and its chemical components are essential for evaluating the effectiveness of pollution control strategies and the health and climate impacts of air pollution. In this study, we compared the GEOS-Chem model simulations with comprehensive datasets for organic aerosol (OA), sulfate, nitrate, and ammonium in China. Model results are evaluated spatially and temporally against observations. The new OA scheme with a simplified secondary organic aerosol (SOA) parameterization significantly improves the OA simulations in polluted urban areas, highlighting the important contributions of anthropogenic SOA from semivolatile and intermediate-volatility organic compounds. The model underestimates sulfate and overestimates nitrate for most of the sites throughout the year. More significant underestimation of sulfate occurs in winter, while the overestimation of nitrate is extremely large in summer. The model is unable to capture some of the main features in the diurnal pattern of the PM 2.5 chemical components, suggesting inaccuracies in the presented processes. Potential model adjustments that may lead to a better representation of the boundary layer height, the precursor emissions, hydroxyl radical concentrations, the heterogeneous formation of sulfate and nitrate, and the wet deposition of nitric acid and nitrate have been tested in the sensitivity analysis. The results show that uncertainties in chemistry perhaps dominate the model biases. The proper implementation of heterogeneous sulfate formation and the good estimates of the concentrations of sulfur dioxide, hydroxyl radical, and aerosol liquid water are essential for the improvement of the sulfate simulation. The update of the heterogeneous uptake coefficient of nitrogen dioxide significantly reduces the modeled concentrations of nitrate. However, the large overestimation of nitrate concentrations remains in summer for all tested cases. The possible bias in the chemical production and the wet deposition of nitrate cannot fully explain the model overestimation of nitrate, suggesting issues related to the atmospheric removal of nitric acid and nitrate. A better understanding of the atmospheric nitrogen budget, in particular, the role of the photolysis of particulate nitrate, is needed for future model developments. Moreover, the results suggest that the remaining underestimation of OA in the model is associated with the underrepresented production of SOA.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Generative AI in Supply Chain Management: Applications, Challenges, and Future Directions

Supply chain management (SCM) is undergoing rapid transformation due to increasing global complexity, demand volatility, and operational disruptions. Generative Artificial Intelligence (GenAI) has emerged as a powerful paradigm capable of synthesizing data, simulating operational scenarios, and enabling adaptive decision-making across supply chain networks. This paper presents a survey of GenAI’s role in SCM, focusing on its applications in predictive analytics, autonomous logistics, and fraud detection. Unlike traditional AI systems that rely primarily on predictive analytics, GenAI models, including large language models, generative adversarial networks, and diffusion-based architectures, enable the creation of synthetic supply chain scenarios and autonomous optimization strategies. This survey provides (1) a taxonomy of GenAI techniques for supply chain applications, (2) a comparative analysis of generative AI approaches with traditional machine learning, reinforcement learning, and blockchain-based methods, and (3) a discussion of key challenges such as data privacy, interpretability, and integration with legacy enterprise systems. Furthermore, we outline open research problems and propose directions for future research toward autonomous, resilient, and sustainable AI-driven supply chains.

15 - GEOTHERMAL ENERGY↗

Field-Aged Glass/Backsheet and Glass/Glass PV Modules: Encapsulant Degradation Comparison

Ethylene vinyl acetate (EVA) is the predominant encapsulant in crystalline-silicon photovoltaic (PV) modules; however, its degradation is a subject of major concern, which causes significant power loss under field conditions. This article presents a comparison of EVA degradation in field-aged PV modules with glass/backsheet (G/B) and glass/glass (G/G) architectures. Modulelevel characterization included UV fluorescence imaging and I-V measurements. Material analytical techniques, including colorimetry, differential scanning calorimetry, thermogravimetric analysis, Fourier transform infrared spectroscopy, and Raman spectroscopy, were performed to correlate the module performance parameters with EVA material properties. An intense EVA discoloration in G/G modules was observed, which was corroborated by higher module Isc and Pmax degradation rates compared with its counterpart G/B modules. Higher power degradation was accompanied by a significant increase in EVA crosslinking, vinyl acetate content, yellowness index, and presence of functional groups containing unsaturated moieties that are linked to degradation products of photothermal reaction, and a higher decrease in the degree of crystallinity. The absence of a polymeric backsheet in hermetically sealed G/G modules, which restricts photobleaching and enhances the entrapment of volatile acetic acid and other degradation by-products, plays a major role in causing higher EVA degradation in G/G modules. This article concludes that EVA might have been a good choice of an encapsulant for the G/B modules over the decades, but it may prove to be an inappropriate choice for the G/G modules because of potential degassing, corrosion, and/or discoloration issues. Ionomers or polyester-based encapsulants like polyolefins could be best suited for G/G modules as it appears to be a current trend in the industry.

14 SOLAR ENERGY↗

Sustainable Aviation Fuel from High-Strength Wastewater via Membrane-Assisted Volatile Fatty Acid Production: Experimental Evaluation, Techno-economic, and Life-Cycle Analyses

To reduce emissions from combustion of fossil fuels, sustainable aviation fuels (SAFs) have the potential to decarbonize the aviation sector. Redirecting wastes from conventional waste management practices and using them as cost-effective feedstocks for low-carbon fuels can reduce emissions from both waste disposal and fuel combustion. One approach is to upgrade wet wastes to SAF precursors, such as volatile fatty acids (VFAs). Here, in this study, novel membrane-assisted arrested methanogenesis was developed to convert high-strength wastewater to VFAs. Based on experimental results of VFA production, techno-economic and life-cycle analyses were conducted to estimate the potential economic and environmental benefits of SAF production from high-strength wastewater via VFAs. By evaluating three proposed scenarios for VFA production, a minimum production cost of VFA is achieved at $\$$0.60/kg VFA at a wastewater flow rate of 1100 MT/d. For the corresponding VFA-derived SAF, the estimated minimum fuel selling price is $\$$4.64/gasoline gallon equivalent. The life-cycle analysis shows that up to a 71% reduction in greenhouse gas emissions can be achieved relative to its fossil-counterpart along with lower water and fossil-fuel consumption.

09 BIOMASS FUELS↗

Integration of Pumped Heat Energy Storage with Fossil-Fired Power Plant (Final Report)

The project team of Southwest Research Institute ® (SwRI ® ), Malta Inc. (Malta), and Luminant Generation Company LLC (Luminant) completed a feasibility study for the integration of a 100-MW, 10-hour (1000-MWh) Malta Pumped Heat Energy Storage (MPHES) system with multiple full-sized fossil-fired electricity generation units (EGU) in Luminant portfolio. MPHES is a long-duration, molten-salt-energy storage technology that uses turbomachinery and heat exchangers to transfer energy to a thermal storage media when charging, and removes the heat in a similar fashion when discharging. With high round trip efficiency (60-65%) and long lifespan (30+ years), MPHES provides economic benefits to the fossil-asset owners that can be scaled to integrate with assets across their portfolio. This technology uses hardware components, workforce personnel, and skillsets similar to those used by fossil EGUs, allowing for synergy when co-locating the two technologies. Luminant has approximately 39,000 megawatts of generation across 12 states, operating in six of the seven competitive markets in the U.S. and powered by a diverse portfolio of natural gas, nuclear, coal, and solar facilities. The DeCordova plant in Granbury, Texas, a simple cycle natural gas peaker power plant, was used as the fossil-fired asset in this project. The local market in Granbury, Texas has many influences, including several nearby power plants, a Luminant-owned nuclear plant (Comanche Peak), and substantial wind energy, which causes both negative pricing at night and high market volatility. Reducing false starts of the DeCordova plant and better responding to market volatility would be an economic advantage. Luminant is currently integrating battery storage plant on site to begin addressing these challenges. Integrating long-duration storage, like MPHES, would expand this capability beyond one hour of storage and have the potential to greatly reduce the total number of gas turbine starts. The MPHES charging requirement could help offset the overnight operating costs of Comanche Peak, which cannot load follow, and the nearby Luminant-owned Wise County combined cycle plant that cycles too often. Following the assessment of Luminant’s ERCOT-based portfolio for integration compatibility with Malta’s PHES system and the project tasks of conceptual study, technoeconomic analysis, technology gap assessment, and commercialization plan, the project team effort has resulted in several key outcomes: (1) Identification of market trends in a high-wind penetration market outside a major metropolitan area; (2) Creation of a dispatching model for the MPHES system and the pairing of Li-ion battery with a gas turbine in a real time market; (3) Revenue and cost estimations for operating MPHES alongside a gas peaker plant with real dispatching considerations and comparison with variations in the Malta implementation, including doubling the storage capacity and using two discharge drivetrains; (4) Potential carbon emission reductions possible by replacing gas turbine operation with Malta PHES operation; and (5) Summary of literature-based future market predictions for Texas.

20 FOSSIL-FUELED POWER PLANTS↗

Volatility-resolved chemical characterization of airborne particles

A method and apparatus for evaluating the chemical composition of airborne particles by sequentially collecting and analyzing airborne particles in-situ. The method includes: collecting particles; enlarging the particles through water condensation; accelerating the enlarged particles onto a surface to collect enlarged particles; and analyzing the enlarged particles by: isolating the surface; passing a carrier gas over the surface; heating the surface to thermally desorb collected particles into the carrier gas; transporting this evolved vapor into detectors; and assaying the evolved vapor as a function of a desorption temperature. The apparatus includes: a sample flow inlet; a condensational growth tube; a collection and thermal desorption (CTD) cell; a carrier gas source; a heater coupled to the CTD; one or more gas detectors; and a controller configured to operate valves, the heater, the growth tube, and the CTD cell in at least an in-situ sequential collection mode and analysis mode.

Isaacman-VanWertz, Gabriel↗

TRACER-MAP Field Campaign Report

During July-August 2022, the U.S. Department of Energy (DOE) Atmospheric System Research (ASR)-funded Tracking Aerosol Convection Interactions Experiment (TRACER)-MAP campaign completed an Atmospheric Radiation Measurement (ARM) user facility field study to measure and map aerosol, volatile organic compounds (VOC), trace gas, and select meteorological observations across Houston, Texas. That ASR project and ARM field campaign, TRACER-MAP, was designed to complement measurements made at the first ARM Mobile Facility (AMF1) by the Aerosol Observing System (AOS) during the TRACER campaign. In this manner, TRACER-MAP effectively extended aerosol measurements from the TRACER AMF deployment, increasing the spatial coverage across the Houston metropolitan area, characterizing a greater diversity of source mixtures (e.g., industrial, traffic, residential, and biogenic) and capturing air masses with differential aging of urban source emissions (e.g., downtown versus downwind). That rich data set now offers many possibilities for in-depth analysis that align with the ASR mission to “research aerosol processes that affect Earth’s radiative balance and hydrological cycle.”

54 ENVIRONMENTAL SCIENCES↗

Krypton storage capacity of the Earth's lower mantle

Noble gases are important geochemical tracers allowing reconstructing global volatile cycles in Earth's reservoirs. To constrain these fundamental processes, precise data on their partitioning behavior at deep Earth conditions are needed. Such data are only available at moderate pressures up to 25 GPa due to experimental challenges. We have investigated the possibility of noble gas storage in the Earth's lower mantle up to 115 GPa. We studied the incorporation of krypton in the second most abundant lower mantle mineral (Mg 1-x ,Fe x )O (ferropericlase) as well as in liquid metal-alloys by performing experiments up to 115 GPa and 3700 K using the laser-heated diamond anvil cell coupled to post-mortem EMPA analysis and X-ray absorption spectroscopy. The results reveal that, at these extreme conditions, up to 3 wt.% of krypton can be stored in (Mg 1-x ,Fe x )O and 3000 ppm in the Fe-rich liquid metal. For both phases the storage capacities increase with pressure (between 40 GPa and 60 GPa) at a constant high temperature of 2300 K. Fpc has never been considered as a NG host, despite being the second most abundant mineral in the Earth's LM. Using recent accurate compressibility data, we demonstrate that a substitution of krypton into the anion site of (Mg 1-x ,Fe x )O in form of neutral oxygen Schottky defects at diluted lower mantle conditions is possible. This noble gas incorporation mechanism is in agreement with a previous study on bridgmanite. We show that (Mg 1-x ,Fe x )O exhibits higher noble gas storage capacities than bridgmanite through the lower mantle using lattice strain modeling and including experimental solubility and thermoelastic data for neon, argon, krypton and xenon. We also demonstrate that both phases exhibit the highest solubilities for argon and krypton. We used the solubility data from lattice strain modeling to predict noble gas abundances stored in the solid lower mantle after magma ocean crystallization. The modeled abundances show apparent similarities with estimates for the deep noble gas reservoir that are based on either 3He abundances in ocean island basalts or radiogenic 40Ar abundances in the bulk Earth. This strongly indicates that the crystalline lower mantle may play an important role as deep noble gas storage reservoir. We propose, based on considerations on noble gas replenishment from the lower mantle to the atmosphere, that the lower mantle can only contribute to a small fraction of the present-day atmospheric noble gases. This suggests that the lower mantle is an un-degassed reservoir.

58 GEOSCIENCES↗

Opportunities for wave energy in bulk power system operations

Wave energy resources have high, yet largely untapped potential as candidate generation technology. In this paper, we perform a data-driven analysis to characterize the impact of wave energy integration on bulk-scale power systems and market operations. Through data-driven sensitivity studies centered on an optimization-based production cost modeling formulation, our work characterizes the inflection point beyond which wave integration starts impacting power system operations, considering present day transmission infrastructure. Furthermore, our analysis also considers the joint effects of wave energy integration and system-wide transmission expansion. Finally, potential resilience scenarios such as wildfire-driven transmission contingencies and heat wave events are investigated, whereby the contributions of grid-integrated wave energy in alleviating the effects of the resilience events are analyzed. As our demonstration test bed, we consider a reduced-order network topology for the U.S. Western Interconnection with wave energy generation integrated at carefully selected sites across the coastal areas of Washington, Oregon, and northern California. Our results indicate that over a representative year of operations, wave energy integration systematically reduces locational marginal prices (LMPs) of energy and price volatility, especially during periods of high wave resource availability (winter months for the U.S. west coast). Average, maximum, and minimum of hourly LMPs over a typical year of operation was reduced by 2.95, 51.28, and 1.13 $\$$/MWh respectively (over a baseline scenario with no wave energy integration), when the selected network model had a total of 5000 MW wave power installed capacity during the representative year of study. The effects of wave energy integration can remain localized with existing transmission infrastructure (identified to be most pronounced in the Pacific Northwest region in the example we studied). However, with concurrent transmission expansion, the impacts of wave energy integration are likely to have a higher geographical spread. Our results also indicate that wave energy may be able to assist power system operations during resilience events such as major transmission contingencies and heat wave events, although such benefits might be dependent on factors such as proximity of affected area to wave resources, availability of adequate resource potential and adequate transmission capacity.

16 TIDAL AND WAVE POWER↗

Comprehensive assessment of deep reinforcement learning approaches for economic dispatch in nuclear-driven microgrids

As the electrical grid integrates more variable renewable energy sources such as wind and solar, the demand for distributed and flexible systems to address this increased variability becomes critical. Nuclear-driven microgrids provide a promising solution by offering stable generation to complement intermittent renewables, ensuring grid reliability and operating efficiency. This paper proposes a recurrent deep reinforcement learning framework for optimal economic dispatch in a nuclear-powered microgrid integrating renewable energy sources, small modular reactors, battery storage systems, and balance-of-plant dynamics. A three-agent control architecture is developed, where demand and renewable energy agents act as forecasters, and a reinforcement learning-based dispatch agent performs real-time energy allocation. A nonlinear programming formulation is first used to generate an optimal baseline for benchmarking. The proposed dispatch controller, based on Proximal Policy Optimization enhanced with Long Short-Term Memory networks, exploits temporal correlations in system dynamics by taking advantage of the time series used as inputs to improve policy robustness under uncertainty. Comparative analysis against established deep reinforcement learning methods, including Proximal Policy Optimization with a feedforward architecture, Soft Actor-Critic, and Twin Delayed Deep Deterministic Policy Gradient, demonstrates superior performance. Numerical results indicate that the proposed controller achieves a 0.39% cost reduction relative to the nonlinear programming benchmark and outperforms other learning-based methods by generating additional revenue of up to 0.35%. All reinforcement learning controllers compute dispatch actions in less than 0.3 s, resulting in a computational speedup of more than three orders of magnitude over the nonlinear programming baseline. The findings of this paper highlight their applicability for real-time operation and control in nuclear-integrated microgrids under volatile operating conditions.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Numerical Investigation of the characteristics of pressurized biomass-oxy-combustion

As a second generation of oxy-fuel combustion technologies, pressurized oxy-combustion has a potential to improve the process efficiency and economy. With the modern decarbonization needs, biomass combustion is considered to be nearly CO2 neutral, and thus co-firing biomass with coal in an oxy-combustion process can achieve a negative CO2 balance. The size of the biomass fuel particles is a key parameter in an entrained flow combustor because large biomass particles, exceeding some threshold size, may only partially be burnt due to the restrictions on the heat transport, thereby diminishing the combustion efficiency. While such a threshold particle size has been scrutinized at an atmospheric pressure, the present study extends the analysis to elevated pressures. Specifically, two scenarios were set up, for experimental-scale and full-scale pressurized oxy-combustors, with the threshold sizes of biomass particles appearing 0.5 mm and 5 mm, respectively, without temperature gradient in biomass particles. With temperature gradient, the parametric study included the particles in the range from 0.15 mm to 5 mm. It is shown that the thermal-thin model is applicable to spherical biomass particles not exceeding 0.15 mm. The impact of the heating rate on biomass particle devolatilization was also investigated and the biomass devolatilization could happen in less than 1 second; the same amount of heat energy could induce different volatile release rates.

Li, Lei↗

Large-Grained Cylindrical Block Copolymer Morphologies by One-Step Room-Temperature Casting

We report a facile method of ordering block copolymer (BCP) morphologies in which the conventional two-step casting and annealing steps are replaced by a single-step process where microphase separation and grain coarsening are seamlessly integrated within the casting protocol. This is achieved by slowing down solvent evaporation during casting by introducing a non-volatile solvent into the BCP casting solution what effectively prolongs the duration of the grain-growth phase. We demonstrate the utility of this solvent evaporation annealing (SEA) method by producing well-ordered large molecular-weight BCP thin films in a total processing time shorter than 3 minutes without resorting to any extra laboratory equipment other than a basic casting-device i.e., spin- or blade-coater. By analyzing the morphologies of the quenched samples, we identify a relatively narrow range of polymer concentration in the wet film, just above the order-disorder concentration, to be critical for obtaining large-grained morphologies. This finding is corroborated by the analysis of the grain-growth kinetics of horizontally-oriented cylindrical domains where relatively large growth exponents (1/2) are observed, indicative of a more rapid defect-annihilation mechanism in the concentrated BCP solution than in thermally-annealed BCP melts. Furthermore, the analysis of temperature-resolved kinetics data allows us to calculate the Arrhenius activation energy of the grain-coarsening in this one-step BCP ordering process.

36 MATERIALS SCIENCE↗

Overview of the Alaskan Layered Pollution and Chemical Analysis (ALPACA) Field Experiment

The Alaskan Layered Pollution And Chemical Analysis (ALPACA) field experiment was a collaborative study designed to improve understanding of pollution sources and chemical processes during winter (cold climate and low-photochemical activity), to investigate indoor pollution, and to study dispersion of pollution as affected by frequent temperature inversions. A number of the research goals were motivated by questions raised by residents of Fairbanks, Alaska, where the study was held. This paper describes the measurement strategies and the conditions encountered during the January and February 2022 field experiment, and reports early examples of how the measurements addressed research goals, particularly those of interest to the residents. Outdoor air measurements showed high concentrations of particulate matter and pollutant gases including volatile organic carbon species. During pollution events, low winds and extremely stable atmospheric conditions trapped pollution below 73 m, an extremely shallow vertical scale. Tethered-balloon-based measurements intercepted plumes aloft, which were associated with power plant point sources through transport modeling. Because cold climate residents spend much of their time indoors, the study included an indoor air quality component, where measurements were made inside and outside a house to study infiltration and indoor sources. In the absence of indoor activities such as cooking and/or heating with a pellet stove, indoor particulate matter concentrations were lower than outdoors; however, cooking and pellet stove burns often caused higher indoor particulate matter concentrations than outdoors. The mass-normalized particulate matter oxidative potential, a health-relevant property measured here by the reactivity with dithiothreiol, of indoor particles varied by source, with cooking particles having less oxidative potential per mass than pellet stove particles.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Bioindicator “fingerprints” of methane-emitting thermokarst features in Alaskan soils

Permafrost thaw increases the bioavailability of ancient organic matter, facilitating microbial metabolism of volatile organic compounds (VOCs), carbon dioxide, and methane (CH 4 ). The formation of thermokarst (thaw) lakes in icy, organic-rich Yedoma permafrost leads to high CH 4 emissions, and subsurface microbes that have the potential to be biogeochemical drivers of organic carbon turnover in these systems. However, to better characterize and quantify rates of permafrost changes, methods that further clarify the relationship between subsurface biogeochemical processes and microbial dynamics are needed. In this study, we investigated four sites (two well-drained thermokarst mounds, a drained thermokarst lake, and the terrestrial margin of a recently formed thermokarst lake) to determine whether biogenic VOCs (1) can be effectively collected during winter, and (2) whether winter sampling provides more biologically significant VOCs correlated with subsurface microbial metabolic potential. During the cold season (March 2023), we drilled boreholes at the four sites and collected cores to simultaneously characterize microbial populations and captured VOCs. VOC analysis of these sites revealed “fingerprints” that were distinct and unique to each site. Total VOCs from the boreholes included > 400 unique VOC features, including > 40 potentially biogenic VOCs related to microbial metabolism. Subsurface microbial community composition was distinct across sites; for example, methanogenic archaea were far more abundant at the thermokarst site characterized by high annual CH 4 emissions. The results obtained from this method strongly suggest that ∼10% of VOCs are potentially biogenic, and that biogenic VOCs can be mapped to subsurface microbial metabolisms. By better revealing the relationship between subsurface biogeochemical processes and microbial dynamics, this work advances our ability to monitor and predict subsurface carbon turnover in Arctic soils.

anaerobic degradation↗

Characterization of the Sulfur Saturated Versions of the High-Chromium Matrix Glasses

In this report, the Savannah River National Laboratory provides chemical analysis of a series of sulfur saturated melt versions of simulated nuclear waste glasses, and chemical analysis of the wash solutions resulting from the preparation of these glasses. The glasses were selected and fabricated by the Pacific Northwest National Laboratory as part of a broader study of the influence of glass composition on chemical durability, sulfur retention, and other properties. The resulting data will be used in the development of improved property/composition models to support operation of the Hanford Tank Waste Treatment and Immobilization Plant. Some degree of scatter among the Al₂O₃, B₂O₃, CaO, K₂O, Na₂O, and SiO₂ measurements was noted for the study glasses. The measured concentrations of B₂O₃, chlorine, and fluorine were below the targeted values for most of the study glasses, likely because of volatility during the multiple melting steps. The measured concentrations of Cr₂O₃ and K₂O were generally low relative to the targeted values. These components were identified in the wash solutions, indicating partitioning to the excess sulfate phase that was washed from the glasses prior to analyses. As expected, the measured concentrations of SO₃ in the glasses were higher than targeted due to the use of the sulfur saturation method in fabricating these glasses. Minor scatter among the triplicate measurements of some of the analytes of the wash solutions was noted. The wash solutions contained mainly sulfur and sodium, with moderate concentrations of chromium and potassium. Revision 1 of this report corrects the omission of Al₂O₃ values from Table A-4 in Appendix A.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

Design Space Exploration of Emerging Memory Technologies for Machine Learning Applications

Memory design space exploration methods study memory systems’ performances and limitations before implementation. The computer memory design space has grown exponentially because of the enormous growth of memory types, memory controllers, and application software. Computer simulators are commonly used for memory design space exploration. However, complex memory simulations take an enormous amount of time. Hence, in this paper, we proposed a machine learning-based design space exploration method for dynamic random-access memory and non-volatile memory systems. We applied our method to the CosmoGAN and LeNet applications to predict the following six memory response parameters: (i) bandwidth, (ii) power, (iii) average latency, (iv) average total latency, (v) memory reads, and (vi) memory writes. Our experimental results show that machine learning models can predict memory response parameter values faster than simulations. We used support vector machine, random forest, and gradient boosting machine learning models. We observed that the support vector machine provides better performance for bandwidth, average latency, and average total latency. The random forest model works better for memory reads and writes. The gradient boosting model provides superior prediction performance for power. We provide a detailed discussion on learning curve characteristics, error analysis, and memory type recommendation.

Hasan, S M Shamimul↗