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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 181 records · Page 10

Effect of Sample Mass, Confinement, and Preheating Time on the Thermal Response of LLM‐105: Experiments and Kinetic Analysis

Various small-scale experiments were performed to provide data for developing a model to predict the thermal response of LLM-105 over a wide range of conditions. The thermal decomposition of LLM-105 was studied as a function of sample mass, confinement of volatile products, and preheating time in both isothermal and ramped heating experiments. The thermal decomposition of LLM-105 is a two-step process, as shown by the two exothermic peaks in the heat flow profiles, which were fitted to two nth-order autocatalytic reaction models with a similar activation energy of ∼289 kJ/mol. The magnitude and shape of these peaks varied with sample mass and confinement. Increasing sample mass enhanced the second exotherm with respect to the first one, while increasing the level of confinement promoted a transition from a sublimation-dominated regime towards thermal decomposition. The effect of LLM-105 particle size on the rate of weight loss was evident for open-pan experiments, where bigger particles sublimed at lower temperatures than smaller particles. Thermal response and solid residue composition of LLM-105 samples were analyzed following preheating for different durations. Longer preheating times caused a shift of the second exotherm to lower temperatures and a decrease in the reaction enthalpy, confirming that LLM-105 decay is a consecutive reaction mechanism, probably autocatalytic. In conclusion, the kinetic model derived from ramped experiments was validated against the measured LLM-105 fraction remaining and the enthalpy remaining of the solid residue as a function of preheating times and showed good agreement.

Chemistry - Chemical explosives↗

Automated Programmable Logic Controller Memory Forensics Using RGB Image Analysis and Deep Learning

The introduction of Industry 4.0 and Internet-based technologies has enhanced industrial control system operations but have inadvertently increased their vulnerabilities to cyber attacks. When an industrial control system is compromised, security analysts need to identify the root cause quickly to start the recovery process and develop mitigation strategies. Memory forensics is critical in the incident analysis process to ascertain what occurred. Approaches for analyzing the persistent memory in industrial control devices are limited and almost nonexistent for volatile memory. This chapter proposes an automated methodology for programmable logic controller memory dump analysis using computer vision and deep learning techniques. The methodology converts the sequences of bytes in a programmable logic controller memory dump to red-green-blue pixels and employs a deep learning model that learns the underlying patterns and features of pre-labeled forensic artifacts in images and segments them into distinct regions. The trained model is employed to automatically segment new memory images and identify forensic artifacts. Evaluation of the methodology on a Schneider Electric Modicon M221 programmable logic controller under code injection and code modification attacks demonstrates its ability to detect attack artifacts in memory dumps.

Asmar Awad, Rima [ORNL] (ORCID:0000000233407742)↗

Hydrocoals from waste biomass via catalytic hydrothermal carbonization processing

This study investigates how operating conditions (temperature and residence time) and benzoyl chloride impact hydrochar production from almond shells through hydrothermal carbonization. Carbon content rises while volatile matter decreases with increasing temperature, yielding a hydrochar with an estimated heating value of 28 MJ/kg. Elevated temperatures lower the O/C and H/C atomic ratios of hydrochars. Even at the lowest temperature under scrutiny, catalytic experiments using benzoyl chloride enhance carbonization and fuel ratio across all temperatures. Hydrochars produced through catalytic runs demonstrate elevated fuel ratios relative to those generated through noncatalytic runs, despite operating under the same conditions. These insights reveal the capacity of benzoyl chloride as a catalyst to improve hydrochar properties, suggesting its utility for advancing both the efficiency and effectiveness of hydrothermal carbonization processes.

09 BIOMASS FUELS↗

Application of Atmospheric Gases and Particulate Matter to the Assessment of Urban Heat Island

Background: Urban heat island (UHI), where built areas are warmer compared to non-urban regions, increases human related diseases and mortality. A key challenge in UHI analysis is the designation of sites as urban or suburban/rural; however, the growing complexity of green spaces in urban areas and the predominance of the transportation sector in nonurban areas creates a dilemma for distinct delineation. Objectives: This study aims to utilize the variability of atmospheric components such as particulate matter (PM), inorganic gases, and volatile organic compounds (VOCs) as direct tracers of the degree of urbanization for ground-based measurements to fully comprehend UHI in convoluted regions with indistinct delineation of urban and nonurban environments. Methods: Atmospheric gases and aerosols were used as direct tracers of urbanization for UHI analysis. Inorganic gases and particulate matter were monitored in two sites in a southeastern US city with varying degrees of urbanization. VOCs were analyzed using a proton transfer reaction time-of-flight mass spectrometer. Results: The more-urbanized site exhibited warmer night conditions and elevated total oxidant levels, leading to the formation of nanometer-sized particles. Machine learning analysis revealed similar atmospheric pollutant profiles for both sites, suggesting comparable sources and variability. Biogenic VOCs were enhanced at the less-urbanized site; however, levels of anthropogenic aromatic VOCs were comparable for both sites. A comprehensive mass spectra analysis revealed distinct molecular backbones per site that further affirmed the applicability of VOCs as indicators of urbanization. Conclusion: This study concludes that VOCs provide more direct and accurate information than typical inorganic gases and PM parameters for characterizing the degree of urbanization. Further exploration of VOCs can enhance our understanding of UHI dynamics and its interaction with vegetation in urban green spaces.

Air quality sensor↗

Fragile-to-strong transition in liquid As 2 S 3 under pressure: The effect of melt metallization

The well-known classification of glass-forming melts into fragile and strong liquids has several notable exceptions, including water, silica, and certain phase-change materials (PCMs). These exceptional fluid systems exhibit a fragile-to-strong transition (FST) upon cooling: a transformation from a high-temperature liquid with fast atomic dynamics, low viscosity, and low flow activation energy, to a viscous supercooled melt with high energy barriers near the glass transition temperature T g . This behavior is critically important for non-volatile memories, photonic tensor cores, reconfigurable metamaterials, and other devices, that use PCMs, enabling nanosecond-scale crystallization in the fragile regime and long data retention in the strong regime near or below T g . A significant structural transformation is expected between these two viscosity regimes, along with a semiconductor-metal (SC-M) transition upon heating, driven by high internal pressure and associated density increase. By applying high external pressure to the canonical low-conducting chalcogenide melt As 2 S 3 , we observed both the FST and the SC-M transition, occurring simultaneously within the same domain of the P, T−phase space. These findings suggest that the FST is not limited to a few exceptional liquids but is a common phenomenon, at least in systems that exhibit melt metallization within specific regions of their P, T−phase diagrams.

first-principles molecular dynamics↗

Evaluating grid stress and reliability in future electricity grids across a range of demand, generation mix, and weather trends

The reliability of power grids in the future will depend on how system planners account for the integration of new technologies, extreme weather events, and uncertainties in demand growth from increased electrification and data centers. This study introduces an open-source, multisectoral, multiscale modeling framework that projects grid stress and reliability trends between 2020 and 2055 in the Western Interconnection of the United States. The framework integrates global to national energy-water-land dynamics with power plant siting and hourly grid operations modeling. We analyze future wholesale electricity price shocks and unserved energy events across eight scenarios spanning a range of population growth and economic change, generation mixes, and weather conditions. Our results show future grids with high percentage of non-renewable generation and strong economic growth are characterized by higher reliability and lower wholesale electricity prices than lower growth scenarios because of larger reliance on dispatchable generators and lower fossil fuel extraction costs. Scenarios with high percentage of renewable resources have lower median but more volatile wholesale electricity prices as well as more frequent and severe unserved energy events compared to scenarios relying more on dispatchable generators. These events occur because higher proportion of solar and wind energy causes net demand curves to deepen during midday (duck curves get progressively severe), exacerbating the challenge of meeting demand during summer evening peaks. This study suggests that robust and co-optimized transmission and energy storage planning could help maintain low wholesale electricity prices and high reliability levels in future electricity grids across uncertainties in generation mixes.

Electric grid reliability↗

Burn parameters affect PAH emissions at conditions relevant for prescribed fires

Wildfire smoke is a health hazard as it contains carcinogenic volatile compounds and fine particulate matter. In particular, exposure to polycyclic aromatic hydrocarbons (PAHs) is a major concern, since these compounds have been recognized as important contributors to the overall carcinogenic risk. In this work, gas and particle-phase PAH emissions from combustion of Eastern White Pine (Pinus strobus) were quantified using time-of-flight mass spectrometry over a range of burn conditions representative of wildfires and prescribed fires, including fuel moisture, heat flux, and oxygen concentration. We found that changing the burn environment lead to a variability of up to 77% in phenanthrene/anthracene emissions. This could explain a large part of the variability in PAH emission factors from biomass combustion reported in the literature. Here, we found that optimal conditions for fuel moisture content of 20–30%, sample heat load of 60 - 70 kW m -2 , and oxygen concentrations of 5–15% can significantly reduce the emissions of heavy molar weight PAHs. Our analysis showed that the relative carcinogenic risk from PAH exposure can be reduced by more than 50% under optimal conditions. In light of the increasing use of prescribed fire for forest management, the relationship between emissions and burn conditions that we have established provides a guidance for assessing the expected health impact from prescription burns, and can inform strategies to reduce PAH emissions from prescribed fire activities.

63 RADIATION, THERMAL, AND OTHER ENVIRON. POLLUTAN↗

Preparation of a 73 As source sample for application in an offline ion source

For the generation of beams with the offline ion source at the Facility for Rare Isotope Beams (FRIB), suitable source samples are required. Arsenic-73 is a frequently requested user beam due to its significance in nuclear structure studies and astrophysics. In this work, we outline the process of preparing a 73 As source sample, containing (5.76 ± 0.37)∗10 14 atoms of 73 As, which was successfully used to generate a 73 As beam for a multi-day user experiment. Silver arsenate was chosen as the chemical form, due to its favorable volatility within the designated operating temperature range. We refined the precipitation method using stable arsenic prior to its application with the 73 As sample, resulting in precipitation yields of (99.4 ± 4.5)%.

As-73↗

Source attribution of near-surface ozone pollution in Jiangsu Province of China over 2013–2019

Near-surface ozone (O3) is one of the most severe air pollutants in China, particularly over densely populated Jiangsu Province in the Yangtze River Delta. In this study, an O3 source tagging technique is utilized in a chemistry-climate model to quantify the source contributions of various emission sectors and regions for nitrogen oxides (NOx) and volatile organic compounds (VOCs) to O3 concentrations in Jiangsu Province during 2013–2019. The results show that the near-surface O3 in Jiangsu Province is mainly contributed by surrounding and remote anthropogenic NOx emissions through long-range transport. Local anthropogenic NOx emissions account for only 13 % and 18 % of the annual and summertime mean near-surface O3 in Jiangsu Province, respectively. Anthropogenic NOx emissions from the surface transportation, industry, and energy sectors account for 21 %, 22 % and 20 % of the annual mean near-surface O3 concentration in Jiangsu Province, respectively. Biogenic and anthropogenic VOCs emissions each explains one-third of the annual mean near-surface O3 concentration in Jiangsu, while methane and stratospheric chemical production contribute 21 % and 6 %, respectively. The sources from stratospheric production, aircraft, lightning, and foreign emissions are the primary contributors to O3 in the mid- and high troposphere. During high pollution days in Jiangsu Province, the near-surface O3 concentrations increase with the maximum exceeding 20 ppb, which is attributed to both the enhanced photochemical production and regional transport in favorable meteorological conditions.

ozone pollution↗

A Demand Bidding Model for Multi-Product Industrial Plants

The growing contribution of renewable energy sources has increased volatility and uncertainty in electricity markets, challenging traditional grid operation paradigms. Demand bidding (DB), a market participation model where (large) electricity users communicate their willingness to pay for electricity to the grid operator, was shown in previous work to enhance grid stability and lower generation cost. We present a DB model for multi-product industrial plants, based on an extended optimal power flow problem where the plant dynamics are represented using autoregressive with extra inputs (ARX) models. We compare DB to price-based demand-side management, showing that, under certain assumptions, the two approaches are equivalent, while DB provides more transparency and predictability to the grid operator. A case study based on an industrial air separation unit is discussed.

24 POWER TRANSMISSION AND DISTRIBUTION↗

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↗

Hydrogen underground storage for grid electricity storage: An optimization study on techno-economic analysis

Here, this study performs a techno-economic analysis of hydrogen underground storage systems for grid electricity storage, evaluating their economic viability at the plant scale using dynamic optimization. It explores the feasibility of various system configurations and revenue models in the context of volatile electricity prices and the necessity for multiple revenue streams. The hypothesis tested is that large-scale hydrogen storage, despite its low round-trip efficiency, can be economically viable with the right mix of revenue streams. This study uses scenario-based analysis to assess the impacts of different system configurations, including engaging in time-shifting arbitrage, ancillary service markets and blending hydrogen with natural gas. Results indicate potential annual net cash flows of up to $\$$1.5 million from ancillary services integration and $\$$5.2 million from natural gas blending, contingent on specific system sizes. The study concludes that hydrogen underground storage for grid electricity storage can be profitable, and emphasizes that proper system design and precise electricity price forecasting are crucial for optimizing system performance and economic returns. This research sets the stage for further investigations into the scalability of hydrogen storage systems and their broader implications for grid electricity storage and energy market dynamics.

25 ENERGY STORAGE↗

Spatial and temporal characterization of municipal solid waste based on resource recovery pathways

This study presents a two-year, quarterly assessment of MSW across four source sectors (residential, schools, restaurants, and grocery stores) from sixteen sites across five U.S. states. MSW was manually sorted into 27 categories and aggregated into pathway fractions: high-moisture (HM) organics, low-moisture (LM) organics, recyclable (RC) materials, and residuals for disposal. Organics represented 89 % of the MSW stream. The largest fraction was HM organics consisting of food waste (31 %) and yard waste (3 %), with large coefficient of variations (CV), 79 and 278 %, respectively, reflecting high seasonal and site variability that varied significantly (p < 0.01) across sampling periods. The HM fraction showed properties favorable for anaerobic digestion, with moisture content ranging from 56 to 95 % and volatile solids ranges of 86-95 %. In contrast, the LM and RC fractions remained more stable (plastics CV = 41 %; paper CV = 53 %) with heating values up to 26.9 MJ/kg across sources, reflecting suitability for gasification. Microstructural analysis revealed less porosity in residential waste sampled at the landfill, which can influence preprocessing efficiency and microbial accessibility. Pathway informed allocations showed that 35 % of MSW is suitable for anaerobic digestion, 36 % for gasification, and 18 % for recycling, leaving 11 % requiring landfill disposal. These results provide quantitative evidence to determine feedstock allocation, waste-to-energy system design, and the development of data-driven sustainability and resource recovery strategies within a circular bioeconomy.

09 BIOMASS FUELS↗

Multi-objective Decisions on Integrated Energy Systems Planning and Operation for Industrial Combined Heat and Power Supply

Unlike the power sector—which can transmit electricity over long distances via established grids—the industrial sector poses a unique challenge due to its geographically concentrated large-scale heat processes. Enhancing energy security in such industrial parks provides a dual benefit: reduced exposure to volatile fossil fuel prices and improved economic viability, largely driven by economies of scale in energy supply and distribution. This study presents a comprehensive technoeconomic analysis of a nuclear energy hub, employing a mechanism-focused approach to evaluate uncertainties in operational strategies and capacity optimization. Load profiles from three major energy intesive industries—chemical, refinery, and steelmaking—are examined, each presenting unique challenges and opportunities for nuclear energy integration. We adopt a multi-objective optimization framework, converting multiple objectives into a single objective function through the e-constraint method. The findings highlight clear trade-offs between system conditions and varying levels of energy independence. Overall, this analysis is granular enough to address industry-specific concerns yet sufficiently generalizable to provide actionable insights into the feasibility of nuclear-based clean heat solutions for the industrial sector.

25 - ENERGY STORAGE↗

Hydrogen-rich syngas production from the steam co-gasification of low-density polyethylene and coal refuse

Gasification provides a promising pathway for transforming waste materials into valuable products, such as fuels and chemicals. Here, this study investigates the steam co-gasification of low-density polyethylene (LDPE) and compressed thickener underflow, representative of coal refuse (CR), in a drop tube reactor. The effects of feed blend ratio (0–100 wt% LDPE) and temperature (800–1000 °C) on syngas composition, tar formation, and process efficiency are examined. The high volatility of LDPE makes it more reactive than CR but also promotes the formation of 2–7 ring aromatic tars. Increasing temperature improves carbon conversion efficiency (CCE), cold gas efficiency (CGE), and syngas yield, although the lower heating value (LHV) of syngas decreases. Hydrogen is the dominant gas product, reaching 59 vol% with the H 2 /CO molar ratio ranging from 2.27 to 4.74. Synergistic effects from alkali and alkali earth metals (AAEMs), particularly K and Ca, in CR ash enhance syngas yield by catalyzing char gasification and tar cracking. Hematite (Fe 2 O 3 ) and ash from sub-bituminous/bituminous coals are explored as tar reforming catalysts. Fe 2 O 3 achieves 100 % tar reforming efficiency, while coal ash, with a lower Fe 2 O 3 content (15 wt%), is less effective at cracking polycyclic aromatic hydrocarbons, particularly naphthalene. These findings demonstrate the flexibility of co-gasification, allowing precise tuning of syngas characteristics for specific downstream applications. Further optimization of waste-derived catalysts could enhance the economic viability of gasification in waste-to-energy processes.

01 COAL, LIGNITE, AND PEAT↗

Reassessing the proposed “CY chondrites”: Evidence for multiple meteorite types and parent bodies from Cr-Ti-H-C-N isotopes and bulk elemental compositions

Here, we report a coordinated bulk Cr-Ti-H-C-N isotopic and compositional study of six carbonaceous chondrites from Antarctica that are often considered to be related and termed Yamato-like carbonaceous (CY) chondrites. These meteorites are known to have undergone extensive aqueous alteration followed by different degrees of thermal alteration, to be similar to one another in regard to mineralogy, and share affinities with both the Ivuna-like carbonaceous (CI) and Mighei-like carbonaceous (CM) chondrites. While mineralogically similar, a key difference among these samples is that chondrules have been found in some of these samples, but not in others. The aim of this study is to evaluate the relationship of these meteorites to one another, and investigate how they relate to the CI and CM chondrite groups. We find that with the addition of the isotopic compositions of these ‘CY’ chondrites, there is now a continuum of isotopic compositions among the carbonaceous chondrites. The CI chondrites are no longer separate in O isotopic compositions from the other carbonaceous chondrite groups in plots of Cr-O and Ti-O. We also find that the ‘CY’ chondrites represent two distinct populations, which correlate with their heating stage. However, the peak temperatures experienced by each population can only explain the differences in H and C isotopes and abundances and N abundances between samples, and cannot have caused the differences in N, Cr, and Ti isotopes, or all the volatile element depletions of the sample’s bulk compositions. Instead, we conclude that the compositional and isotopic data of these two populations correlate with their known chondrule abundances, indicating distinct precursors for each population. We find it most likely that these samples originate from two distinct asteroids, implying that among the six samples studied here, there are not five related samples to constitute a new meteorite group. The chondrule-free heating Stage III samples are most likely heated CI chondrites, while the chondrule-bearing heating Stage IV samples could be heated CM chondrites.

58 GEOSCIENCES↗

Comparison of the earliest NC and CC planetesimals: Evidence from ungrouped iron meteorites

Isotope anomalies in meteorites reveal a fundamental dichotomy between Non-Carbonaceous- (NC) and Carbonaceous-type (CC) planetary bodies. Until now, this dichotomy is established for the major meteorite groups, representing about 36 distinct parent bodies. Ungrouped meteorites represent an even larger number of additional parent bodies, but whether they conform to the overall NC-CC dichotomy is unknown. Here, the genetics and chronology of 26 ungrouped iron meteorites are considered through nucleosynthetic Mo and radiogenic W isotopic compositions. Secondary cosmic ray-induced modifications of these isotope compositions are corrected using Pt isotope measurements on the same samples. We find that all of the ungrouped irons have Mo isotope anomalies within the range of the major meteorite groups and confirm the NC-CC dichotomy for Mo, where NC and CC meteorites define two distinct, subparallel s-process mixing lines. All ungrouped NC irons fall on the NC-line, which is now precisely defined for 41 distinct parent bodies. The ungrouped CC irons show scatter around the CC-line indicative of small r-process Mo heterogeneities among these samples. These r-process Mo isotope variations correlate with O isotope anomalies, most likely reflecting mixing of CI chondrite-like matrix, chondrule precursors, and Ca-Al-rich inclusions. This implies that CC iron meteorite parent bodies accreted the same nebular components as the later-formed carbonaceous chondrites. The Hf-W model ages of core formation for the ungrouped irons overlap with those of the iron meteorite groups from each reservoir and reveal a narrow age peak at ∼3.3 Ma after Ca-Al-rich inclusions for the CC irons. By contrast, the NC irons display more variable ages, including younger ages indicative of impact-induced melting events, which seem absent among the CC irons. This is attributed to the more fragile and porous nature of the CC bodies, making impact-induced melting on their surfaces difficult. The chemical characteristics of all iron meteorites together reveal slightly more oxidizing conditions during core formation for CC compared to NC irons. More strikingly, strong depletions in moderately volatile elements, typical of many iron meteorite parent bodies, predominantly occur among CC irons, for reasons that remain unclear at present.

Chondrite components↗

Beta-Amino Carboxylate (BAC) non-aqueous physical solvents for enhanced CO2 separations in pre-combustion carbon capture, industrial CO 2 capture, and biogas upgrading processes

Novel beta-amino carboxylate (BAC) solvents have been synthesized and tested to efficiently capture carbon dioxide (CO 2 ) from process gas streams with CO 2 partial pressure intermediate between pre-combustion and post-combustion capture. The BAC solvents have molecular structures characterized by alkyl-substituted amides or esters containing a secondary amine functional group on the second carbon from the carbonyl carbon (referred to as the beta “β” carbon). The ester or amide functional group combined with optimal steric crowding around the amine nitrogen by proximate alkyl groups are tailored to modify the strength of CO 2 binding in the solvent. The solvents possess high CO 2 solubilities and high gas selectivity including good CO 2 /H 2 O selectivity and can be utilized for CO 2 absorption over a range of partial pressures. Due to low volatility, many of the solvents can be operated at or above ambient temperature which eliminates solvent chilling and allows regeneration using low grade waste heat. These novel solvents offer an opportunity for efficient carbon capture for a range of applications including biogas upgrading, hydrogen production, and pre-combustion carbon capture.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗