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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Product-specific kinetics reveal effect of epoxy crystallization on thermoset thermal degradation

Crystallization is a common problem for epoxy resins, which are ubiquitous in industrial and commercial use. Integration of crystallized epoxy monomers into cured thermosets has been shown to alter the thermosets’ final mechanical properties. However, no studies have investigated the impact of these crystals on the thermal stability of the thermoset. Here we investigate the degradation kinetics of a bisphenol F–based epoxy thermoset with and without crystallized monomers using Product-Specific Kinetic (PSK) analysis coupled with Evolved Gas Analysis-Mass Spectrometry (EGA-MS). PSK analysis revealed significant differences in evolved product ion kinetics, suggesting changes in the degradation kinetics between thermoset configurations. Further, it was concluded that early stages of degradation are influenced most by crystal presence due to the high concentration of unreacted epoxy monomers and lower cross-linking density of the cured network. After post-cure annealing, significant changes are observed in the degradation kinetics of thermosets without crystal inclusions. Conversely, post-cure annealing procedures of crystal integrated thermosets showed little change in the thermoset degradation kinetics across all conversion extents. These findings suggest that post-cure annealing of thermosets with crystals present at the onset does not alter the cross-linking density of the polymer network enough to significantly change the degradation kinetics. We hypothesize this is because the excess monomers from the melted crystals are unable to find suitable reaction sites for complete binding into the polymer network, which has direct implications for the material properties and final thermal stability of the thermoset.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Mechanistic Insight into Lignin Slow Pyrolysis by Linking Pyrolysis Chemistry and Carbon Material Properties

As a highly abundant renewable carbon source, lignin can be converted to a variety of advanced carbon materials with tailorable properties through slow pyrolysis. In this study, slow pyrolysis of kraft lignin, for the first time, was investigated with a commercial pyrolysis–gas chromatography–mass spectrometry (Py–GC–MS) system through evolved gas analysis-MS (EGA-MS) and heart-cutting-GC–MS (HC-GC–MS) analyses. These analyses allow recovery and examination of the multiphased gas products generated from thermal decomposition of lignin during slow pyrolysis at a controlled heating rate over a long time course, thus making it possible to link operation conditions, pyrolysis chemistry, and carbon material properties. The overall product distributions, including volatiles and solid products, were quantitatively tracked at different heating rates (2, 20, and 40 °C/min) and different temperature regions (100–200, 200–300, and 300–600 °C). Solid residues were further characterized using a suite of analytical tools, in correlation with the investigation of formation mechanisms of volatiles to reveal the reaction chemistry of lignin during slow pyrolysis and to determine the morphology, pore structure, and interfacial chemical properties. This study provides critical insights into the slow pyrolysis chemistry of lignin and the properties of the resulting carbon material. These results will facilitate a better design and control of the lignin slow pyrolysis process for synthesizing functional carbon materials.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Interaction of Concentrated (68%) Aqueous Nitric Acid Solutions with Cheesecloth: A TGA-DSC-MS Study

The exposure of cotton cheesecloth to concentrated aqueous solutions of nitric acid reduced the onset of combustion by 30-40°C (from ~350°C to ~310°C) relative to that of both unexposed cheesecloth and cheesecloth that was rinsed immediately following exposure to the same solution. Cheesecloth exposed to concentrated (68%) nitric acid underwent three exothermic pre-combustion reactions which onset at 125-135°C, 160- 170°C and 200-210°C. These reactions give off mostly water vapor, with lesser amounts of NO and carbon dioxide. Exposure to concentrated nitric acid damages the cellulose polymers, oxidizing them, reducing them in length, and increasing their reactivity in response to thermal stress. Cheesecloth which was rinsed after exposure behaved similarly to unexposed cheesecloth, indicating that NC was not formed on exposure to the concentrated aqueous nitric acid solution. The addition of a 239Pu – 241Am spike in an aqueous nitric – hydrofluoric solution to the cheesecloth reduced the onset temperature of the first exothermic reaction to 126°C and lowered the onset temperature of combustion from ~310°C to ~275°C. When dried on to cellulose substrates in the presence of excess nitrate, metal ions may act as oxidizers, or may catalyze the oxidation of the carbonaceous material, lowering ignition temperatures. No evidence for the formation of nitrocellulose following the exposure of cheesecloth to nitric acid was observed during this study

36 MATERIALS SCIENCE↗
DOE OSTI2026

Formation of Non-Doped Cubic Lithium Lanthanum Zirconium Oxide Nanofibers: Insights from In Situ Synchrotron X-Ray Scattering

This study investigates the formation mechanism of non-doped cubic lithium lanthanum zirconium oxide (c-LLZO) nanofibers using in situ synchrotron X-ray scattering techniques. Electrospun polymer precursor nanofibers were annealed at temperatures up to 800 °C, enabling real-time tracking of phase transitions via simultaneous small-angle X-ray scattering (SAXS), wide-angle X-ray scattering (WAXS), and evolved CO 2 gas analysis. The results reveal a three-step transformation pathway: polymer decomposition, formation of La 2 Zr 2 O 7 (LZO), and direct conversion of LZO to c-LLZO without intermediate tetragonal phases detected within the sensitivity of our in situ WAXS measurement. Cryo-electron energy loss spectroscopy (EELS) further elucidates the role of lithium diffusion, showing Li enrichment at fiber surfaces and Li deficiency in the interior, which stabilizes the cubic phase. This Li segregation effect in nanostructured LLZO materials extends beyond the previously reported size effect. This work advances the understanding of c-LLZO formation mechanisms and provides practical insights for optimizing synthesis routes to achieve phase-pure c-LLZO for solid-state battery applications.

LLZO phase stability↗
DOE OSTI2026

Optimizing Cryo-Focused Pyrolysis GC/MS for Tracing Soil Organic Matter Across Diverse Ecosystems

The cycling of organic matter in terrestrial soils and sediments is central to a range of biogeochemical processes that regulate nutrient cycling, crop productivity, trace gas emissions, and contaminant transport. Pyrolysis-gas chromatography/mass spectrometry (py-GC/MS) is a powerful tool for characterizing bulk soil organic matter (SOM) at the molecular level. In this study, we used a cryo-focused py-GC/MS system to analyze soil samples from seven diverse ecosystems: vernal pool, prairie pothole, temperate forest, tropical forest, tundra, wildfire-affected boreal forest, and grassland. We addressed a key bottleneck in molecular-level SOM characterization by developing an automated data analysis pipeline to optimize py-GC/MS and complementary evolved gas analysis/mass spectrometry (EGA/MS) methods, incorporating advanced tools for peak deconvolution, developing a custom compound class library, and implementing fragmentation spectrum-based molecular networking for the first time. This improved workflow was applied to soil samples from all seven ecosystems, including multiple depths and density fractions. Our findings demonstrate that ecosystem type plays a dominant role in shaping compositional differences in SOM. We also identified trends in the source of SOM compounds (e.g., microbial vs plantderived) across soil depth and density fractions, which are critical for understanding persistence and turnover of SOM. Our molecular networking analysis indicated that although many compounds are widespread across ecosystems, others are restricted to specific environments, such as wetlands. This underscores the utility of molecular-level data in elucidating the complexity of SOM composition and the environmental drivers that shape it. Such molecular-level insights can deepen our knowledge of biogeochemical SOM cycles.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Development of gas chromatographic pattern recognition and classification tools for compliance and forensic analyses of fuels: A review

Gas chromatography (GC) is undoubtedly the analytical technique of choice for analyzing the composition of petroleum-based fuels. Over the past twenty years, as comprehensive two-dimensional gas chromatography (GC×GC) has evolved, fuel analysis has often been highlighted in scientific reports, as their complexity allows for illustration of the impressive peak capacity gains afforded by GC×GC. Indeed, several research groups in recent years have applied GC×GC and chemometrics to demonstrate the potential of these analytical tools to address important compliance (tax evasion, tax credits, physical quality standards) and forensic (arson investigations, oil spills) applications involving fuels. None the less, routine use of GC×GC in forensic laboratories has been limited largely by (1) legal and regulatory guidelines, (2) lack of chemometrics training, and (3) concerns about the reproducibility of GC×GC. The goal of this review is to highlight recent advances in one-dimensional GC (1D-GC) and GC×GC analyses of fuels for compliance and forensic applications, in an effort to assist scientists in overcoming the aforementioned hindrances. An introduction to 1D-GC principles, GC×GC technology (column stationary phases and modulators) and several chemometric techniques will be provided. More specifically, chemometric techniques will be broken down into (1) signal pre-processing, (2) peak decomposition, identification and quantification, and (3) classification and pattern recognition. Examples of compliance and forensic applications will be discussed, with particular emphasis on the demonstrated success of the employed chemometric techniques. Overall, this review will hopefully make 1D-GC and GC×GC coupled with chemometric data analysis tools more accessible to the larger scientific community, and aid in eventual widespread standardization.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Energetics of oxidation and formation of uranium monocarbide

To enable better implementation of uranium monocarbide (UC) as an advanced nuclear fuel for future high-temperature reactors, it is essential to have a thorough knowledge of its thermal and thermodynamic properties under reactor operational conditions. In this work, we studied thermal bulk oxidation of UC by simultaneous thermal analysis consisting of thermogravimetric analysis – differential scanning calorimetry coupled with evolved gas mass spectrometry (TGA-DSC-MS), and we examined the thermodynamic stability of UC using high temperature oxide melt drop solution calorimetry. Further, in air, our studied UC sample (which contains ~5 mol% UO 2 ) was found to undergo a step-wise thermal oxidation process consisting of consecutive oxidations and thermal decomposition reactions: 0.95UC·0.05UO 2 → UO 3 ·0.29(C x O y ) + 0.66CO 2 → UO 3 ·0.20(C x O y ) + 0.09CO 2 → UO 3 ·0.03(C x O y ) + 0.17CO 2 → U 3 O 8 + 0.03CO 2 + 0.166O 2 . DSC was further used to determine the enthalpies of reactions associated with this series of oxidation reactions. Synchrotron X-ray diffraction (XRD) and extended X-ray absorption spectroscopy (EXAFS) were performed to characterize both the long- and short-range structures of UC. The standard enthalpy of formation (ΔH° f ) of UC was determined to be –50.7 ± 10.8 kJ/mol·atom, in good agreement with previous values measured by bomb calorimetry. Lastly, the enthalpic landscape of U-C compounds, including UC, U 2 C 3 , and α-UC 1.94 , were established based on the enthalpy normalized per mole atom, which suggests that U-C phases are thermodynamically stable at lower C/U ratios.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2025

Analysis of anion exchange membrane water electrolyzer performance and its evolution over time

Understanding water, evolved gas, and ionic transport in membrane-electrode-assemblies (MEAs) is essential for the development of high performance and durable anion exchange membrane water electrolyzers (AEMWEs). This study evaluates the MEA conditioning process, operating conditions, and short-term stability in a 1 M potassium hydroxide (KOH) electrolyte, focusing on the underlying transport phenomena. We observe a significant initial voltage loss in continuous cell operation, which could be associated with gas bubble accumulation, transport layer or flow field passivation, and changes in the catalyst oxidation state. Further, we investigate the effects of materials and operational configurations, including the membrane type and thickness, and the electrolyte flow rate, including KOH being fed to both electrodes as well as to the anode only. Furthermore, the effect of membrane drying temperature on ex situ as well as in situ electrochemical performance is evaluated. Finally, we discuss 700 h of AEMWE operation at 1 A/cm 2 , highlighting the underlying degradation phenomena.

25 ENERGY STORAGE↗
DOE OSTI2025

Intrinsic Kinetics of Polyethylene Terephthalate Pyrolysis via Micropyrolysis and Multivariate Chromatographic Analysis

This study provides an in-depth investigation of the primary decomposition of polyethylene terephthalate (PET) via pyrolysis, employing an experimental-analytic workflow that integrates design of experiments (DoE), micropyrolysis coupled with comprehensive two-dimensional gas chromatography (GC×GC), and multivariate data analysis to verify intrinsic kinetic conditions and elucidate evolving product distributions for mapping key reaction pathways. Peaks that could not be identified using commercial spectral libraries were assigned using Mass Frontier simulations, enabling the identification of divinyl terephthalate, ethyl vinyl terephthalate, and 2-(benzoyloxy)ethyl vinyl terephthalate. A polar×polar (non-orthogonal) column set tailored for the detection of carboxylic acids enhanced the quantification of benzoic acid, 4-vinylbenzoic acid, 4-ethylbenzoic acid, and methylbenzoic acid by up to 6-fold relative to an orthogonal column combination (non-polar×mid-polar). Moreover, pyrolysis variables were systematically evaluated using a Box- Behnken design (BBD), encompassing pyrolysis temperature (500−600 °C), sample weight (50−150 μg), and carrier gas flow rate (100−300 mL min −1 ). Among these, pyrolysis temperature was the only statistically significant factor influencing product yields, ranging from 58.78 to 84.26 wt %. In contrast, neither the sample weight nor the carrier gas flow rate had a significant effect on product yields within the evaluated experimental space. At 600 °C, the major pyrolysis products were benzoic acid (up to 20.20 ± 1.46 wt %) and CO 2 (up to 21.28 ± 1.46 wt %), which can be produced through decarboxylation reactions. These findings underscore the critical importance of selecting appropriate analytical columns for the accurate quantification of heteroatomcontaining products such as carboxylic acids, which may otherwise be underestimated or undetected due to their reactivity with the stationary phase of non-polar and mid-polar columns, as well as other GC components. They also highlight the importance of selecting pyrolysis conditions for investigating the primary decomposition of PET under an isothermal kinetically limited regime.

aromatic compounds↗
DOE OSTI2021

Individual Wave Detection and Tracking within a Rotating Detonation Engine through Computer Vision Object Detection applied to High-Speed Images

Known for their simplistic design and continuous detonation, rotating detonation engines (RDEs) constitute a majority of current pressure gain combustion (PGC) research efforts. Experimental RDE operation times have been continuously extended through the use of rig cooling techniques. As the window of observable behavior is expanded, and as the technology matures toward eventual integration within gas turbines, monitoring techniques must evolve to better match industrial diagnostics. High-speed image analysis techniques prove useful to capture and evaluate the unsteady detonation behavior within the RDE. Traditional image analysis techniques, however, require extensive processing times which prohibit simultaneous monitoring. To better address this problem, a computer vision object detection methodology is proposed to quickly detect individual detonation waves within a single down-axis image. Detonation waves are detected in individual images by the implemented computer vision method You Only Look Once (YOLO) object detection network. In order to detect detonation waves, the network must first be trained using RDE images of interest, for which each required phase of network development is outlined. Detection of waves is improved through proper treatment of the collected image set, variation of Intersection over Union (IoU) and confidence thresholding, and through a parametric study of annotation dimensions. Each detected wave is described by its location and rotational direction, and locations are tracked to calculate wave velocity across each frame, leading to a timestep resolution of 20 µs. Wave velocities are also calculated through a series of frames, leading to a suitable average velocity estimation using as few as 10 frames. Uncertainty analysis accounting for variation in camera framerate, pixel width and annotation centroid locations estimates a total uncertainty of ±4.3% for velocity calculations, using the smallest annotation boxes. This method offers great reductions in processing times, as a step toward real-time monitoring of detonation waves within an RDE. Improving on previous studies, this technique is impartial to wave modes not included in the original training set and calculates wave velocities independent of high-speed pressure data. The ability to isolate waves within predicted bounding boxes will likely facilitate analysis of pixel intensity variation as an estimation of wave strength in future work.

Johnson, Kristyn↗
DOE OSTI2021

Individual Wave Detection and Tracking within a Rotating Detonation Engine through Computer Vision Object Detection applied to High-Speed Images

Known for their simplistic design and continuous detonation, rotating detonation engines (RDEs) constitute a majority of current pressure gain combustion (PGC) research efforts. Experimental RDE operation times have been continuously extended through the use of rig cooling techniques. As the window of observable behavior is expanded, and as the technology matures toward eventual integration within gas turbines, monitoring techniques must evolve to better match industrial diagnostics. High-speed image analysis techniques prove useful to capture and evaluate the unsteady detonation behavior within the RDE. Traditional image analysis techniques, however, require extensive processing times which prohibit simultaneous monitoring. To better address this problem, a computer vision object detection methodology is proposed to quickly detect individual detonation waves within a single down-axis image. Detonation waves are detected in individual images by the implemented computer vision method You Only Look Once (YOLO) object detection network. In order to detect detonation waves, the network must first be trained using RDE images of interest, for which each required phase of network development is outlined. Detection of waves is improved through proper treatment of the collected image set, variation of Intersection over Union (IoU) and confidence thresholding, and through a parametric study of annotation dimensions. Each detected wave is described by its location and rotational direction, and locations are tracked to calculate wave velocity across each frame, leading to a timestep resolution of 20 µs. Wave velocities are also calculated through a series of frames, leading to a suitable average velocity estimation using as few as 10 frames. Uncertainty analysis accounting for variation in camera framerate, pixel width and annotation centroid locations estimates a total uncertainty of ±4.3% for velocity calculations, using the smallest annotation boxes. This method offers great reductions in processing times, as a step toward real-time monitoring of detonation waves within an RDE. Improving on previous studies, this technique is impartial to wave modes not included in the original training set and calculates wave velocities independent of high-speed pressure data. The ability to isolate waves within predicted bounding boxes will likely facilitate analysis of pixel intensity variation as an estimation of wave strength in future work.

Johnson, Kristyn↗
DOE OSTI2023

Pyrolysis of Oils from Unconventional Resources

In this study, oils from various sources were subjected to pyrolysis conditions; that is, without oxidizer, as the samples were heated to 500 °C, and held at that temperature. The oils studied included: (1) heavy oil from Grassy Creek, Missouri; (2) oil from tar sands of Asphalt Ridge in Utah; (3) mid-continent oil shales of three formations (two of Chattanooga formation, Pennsylvanian (age) formation, and Woodford formation); and (4) a Colorado Piceance Basin shale. Differential scanning calorimetry (DSC) and thermogravimetric analysis (TGA) with either gas chromatography (GC) or mass spectrometry (MS) were used to quantify the produced gases evolved in the tests. Purge gases of helium, argon, and humid carbon dioxide were utilized. Larger scale pyrolysis tests were conducted in a tube furnace coupled to a MS and a GC. The results consistently showed that pyrolysis occurred between 300 °C and 500 °C, with the majority of gases being mainly hydrogen and light alkanes. This behavior was essentially consistent, regardless of the oil source.

42 ENGINEERING↗
DOE OSTI2022

Evaluating Contributions of Pitch-Carbon Coating to Improved Stability of Si Anodes Through Voltage-Resolved Multi-Phase Characterization

Silicon nanoparticles have emerged as a promising alternative to graphite to improve the energy density of next-generation lithium-ion battery anodes. Nano-sized Si domains facilitate rapid ion transport and minimize particle-scale mechanical degradation, but also exhibit increased (electro)chemical reactivity with Li-ion electrolyte components due to their high surface area. We have previously demonstrated that surface modification of Si nanoparticles with pitch-carbon is an effective strategy to reduce these parasitic reactions. In the present work, we holistically evaluate the mechanistic contribution of pitch-carbon coating to the observed stability improvement over uncoated Si. We utilize coupled in situ and ex situ methods to probe changes to solid-surface, volatile headspace, and gas-phase chemistry occurring during initial cycling. Measurements taken at targeted potentials associated with electrolyte species reduction enables the decoupling of specific reaction pathways tied to interfacial stability. Further, we demonstrate the non-trivial role of gas reconsumption in dictating the nature of the passivating surface layer evolved on both uncoated and pitch-coated Si. This multi-phase analysis offers insights into the mechanism of effective surface passivation, which may be applied to inform future Si material development.

ENERGY STORAGE↗
DOE OSTI2020

Pore-scale evidence of ion exclusion during methane hydrate growth and evolution of hydrate pore-habit in sandy sediments

Hydrate-bearing sediments constitute complex hydrological systems, within which the pore habit of hydrate significantly affects all physical properties. However, there is scarce pore-scale evidence of the evolution of hydrate growth in porous media and the influence of water salinity in the micro-morphology of hydrate. In this paper, we used X-ray computed micro-tomography (CT) to monitor three experiments of methane hydrate growth in sand partially saturated with either NaBr or KI brine under excess-gas conditions. Gas, brine and hydrate are observed to co-exist at the pore-scale during the evolution of hydrate pore-habit towards three-phase equilibrium. The experiments and data analysis reveal that (1) hydrate forms as a porous medium mixed with inclusions of brine and gas, where hydrate and brine evolve gradually into separate phases as hydrate cages exclude salt ions, (2) hydrate growth mobilizes water over fairly long distances resulting in heterogeneous hydrate distribution, and (3) hydrate can exhibit interconnecting pore-habit at local hydrate saturations higher than ~50%, even if grown under excess-gas conditions. The results imply that hydrate micro-morphology and pore habit evolve during hydrate formation and, we suggest, so do the physical properties of hydrate-bearing sediments. Furthermore, our findings provide new insights into the micro-morphology of hydrate and evolution of the properties of hydrate-bearing sediments synthesized in the laboratory and in natural systems.

Clathrate hydrate↗
DOE OSTI2024

A deep decarbonization framework for the United States economy – a sector, sub-sector, and end-use based approach

Achieving the United States' target of net-zero greenhouse gas emissions by 2050 will require technological transformations and energy sector mitigation. To understand the role of dynamically evolving technologies, identify synergies and dissonance and the effect of allocating limited low-carbon biomass resources in decarbonizing the U.S. economy, we developed the Decarbonization Scenario Analysis Model. A Life Cycle Assessment based approach is implemented considering the U.S. economy as the functional unit, to estimate greenhouse gas mitigation potential for projected energy demand based on several sector-level and cross-sectoral decarbonization pathways. Direct and supply-chain emissions are accounted, resulting from changes in patterns of energy generation and consumption, technology breakthroughs, and reductions in fugitive emissions over time at the granularity of economic sectors, sub-sectors, and end-use. Decarbonization strategies are implemented over a reference case developed using Energy Information Administration (EIA AEO) projection of economic activities for 2020–2050. Based on the considered scenarios, 80–90% economy-wide decarbonization relative to the 2020 reference case is projected. Electrification, low-carbon fuels, and reduction of fugitive emissions play the most significant role to decarbonization. The majority of the remaining emissions are accounted to the supply-chain and end-use emissions from natural gas and diesel fossil-based fuels in heavy duty transportation and heavy industries, highlighting the need for developing low-carbon and carbon-negative alternatives to mitigate those fossil-based carbon emissions.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2025

Evolution of Mass Spectrometers for High m / z Biological Ion Formation, Transmission, Analysis and Detection: A Personal Perspective

Mass spectrometry (MS) has become an essential tool in virtually all academic, pharmaceutical, and biopharmaceutical analytical laboratories. The specialized and bespoke area of MS research and application of high m / z ion (> m / z 6000 and high mass, >150 kDa) formation, transmission, analysis, and detection is a relatively new area of focus for MS that has seen dramatic acceleration in interest over the last two decades. Herein we delve into this exciting aspect of MS, discussing how MS instrumentation has been refined and evolved for native-MS analysis. We cover the early groundbreaking experiments showing high m / z ion formation, transmission, and preservation of protein structure in the gas phase. Additionally, we discuss specific instrument optimizations and modifications that have advanced high m / z ion generation, transmission, analysis, and detection, contributing to the research area known as gas-phase structural biology. Native-MS sample introduction methods, emerging technologies, and future perspectives are also examined. Finally, we share personal opinions, observations, and experiences that are new to the community or previously unpublished.

collisions↗
DOE OSTI2025

Scalability analysis of heavy-duty gas turbines using data-driven machine learning

With the increasing integration of variable renewable energy sources into power systems, the role of flexible power generation technologies like gas turbines (GT) in rapid grid balancing remains crucial. This sustained importance underscores the need for scaled and precise modeling of GT to ensure effective integration within evolving energy frameworks. While physics-driven GT models integrate thermodynamics, fluid dynamics, and combustion principles, they often rely on approximate mathematical representations to accommodate scaling that may not capture the actual complex dynamics for GTs and inertial effects associated to GTs with different ratings. In this study, a data-driven model is proposed using machine learning (ML) techniques to conduct GT scalability analysis and performance evaluation with high accuracy. The ML model, trained on data from various operating conditions and performance parameters, aims to uncover intricate relationships and patterns, resembling GT characteristics at different scales (ratings). The model is developed to capture complex system interaction and to adapt to changing operational scenarios at different capacities, providing valuable insights of power system dynamics. In this study, the real-time digital simulator platform was employed to generate training data for the ML model and assess its dynamic characteristics. The ultimate objective was to develop a detailed modeling framework based on governing equations and data-driven ML capable of predicting key performance indicators, in thermal systems such as GTs, including power output, speed, fuel consumption, and exhaust temperature under diverse operating conditions at different scales. The developed ML framework demonstrated high accuracy, with mean relative errors for GT power prediction, reference speed, exhaust temperature, and compressor pressure ratio (CPR) parameters consistently below 0.1% across typical load fluctuation scenarios. Maximum deviations were limited to approximately 0.5 K for exhaust temperature and 0.009 for CPR, underscoring the model’s ability to replicating dynamic GT behavior with high precision. The adaptability of the ML model enables its application across diverse operational conditions and its extension to other thermal systems. By leveraging advanced ML techniques, this study presents a robust and scalable modeling framework that enhances GT simulation precision, facilitating improved integration into evolving power systems.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

Analysis of Organophosphorus-Based Nerve Agent Degradation Products by Gas Chromatography-Mass Spectrometry (GC-MS): Current Derivatization Reactions in the Analytical Chemist’s Toolbox

The field of gas chromatography-mass spectrometry (GC-MS) in the analysis of chemical warfare agents (CWAs), specifically those involving the organophosphorus-based nerve agents (OPNAs), is a continually evolving and dynamic area of research. The ever-present interest in this field within analytical chemistry is driven by the constant threat posed by these lethal CWAs, highlighted by their use during the Tokyo subway attack in 1995, their deliberate use on civilians in Syria in 2013, and their use in the poisoning of Sergei and Yulia Skripal in Great Britain in 2018 and Alexei Navalny in 2020. These events coupled with their potential for mass destruction only serve to stress the importance of developing methods for their rapid and unambiguous detection. Although the direct detection of OPNAs is possible by GC-MS, in most instances, the analytical chemist must rely on the detection of the products arising from their degradation. To this end, derivatization reactions mainly in the form of silylations and alkylations employing a vast array of reagents have played a pivotal role in the efficient detection of these products that can be used retrospectively to identify the original OPNA.

sarin↗