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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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Steranes and triterpanes identification from Green River shale by capillary gas liquid chromatography and mass spectrometry
Deuterium labeling for mass spectrometry via exchange of enolizable hydrogen in ketones on gas- liquid chromotography column
Acid fraction extraction of Green River shale identified by gas chromatography-mass spectrometry-computer system, noting trimethyl pentadecanoic acid
Quantitative and qualitative analysis of biochemically and radiobiologically important thiols and disulfides via gas-liquid chromatography
A hand-held, portable gas chromatography-ion mobility spectrometer (GC-IMS) device was used to detect the presence of volatile amine compounds in the headspace of decomposing fish. The Food and Drug Administration (FDA) largely relies on olfactory discrimination with respect to fresh and spoiled, frozen and unfrozen fish. The fish are delivered at ship docks on pallets, and each pallet of fish can range from 30-40 thousand dollars in value. Fresh fish were placed in a teflon bag and the direct headspace was interrogated. In the first three days, only low molecular weight volatile amines were detected. On the fourth day, a number of spectral signatures were observed which indicated the presence of 1,5-diaminopentane, cadaverine. Analyses typically took from 0.5-1 minute.
Studies of the amino acid contents and enantiomeric compositions of a single stone from the Murchison meteorite are reported. Water-extracted and 6M HCl-extracted samples from the meteorite interior of meteorite fragments were analyzed by gas chromatography and combined gas chromatography-chemical ionization mass spectrometry. Examination of the D/L ratios of glutamic acid, aspartic acid, proline, leucine and alanine reveals those amino acids extractable by water to be partially racemized, whereas the acid-extracted amino acids were less racemized. The amino acid composition of the stone is similar to those previously reported, including the absence of serine, threonine, tyrosine phenylalanine and methionine and the presence of unusual amino acids including such as isovaline, alpha-aminoisobutyric acid and pseudoleucine. It is concluded that the most likely mechanism accounting for the occurrence of nonracemic amino acid mixtures in the Murchison meteorite is by extraterrestrial stereoselective synthesis or decomposition reactions.
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.
An extraction protocol from silt sediment of fentanyl and three analogs: acetylfentanyl, thiofentanyl and acetylthiofentanyl, spiked at two concentrations each and separately (at ~1 and ~10 µg/g), is described. In addition, the identity of the fentanyls preliminarily identified by electron ionization gas chromatography-mass spectrometry (EI-GC-MS) analysis, can be corroborated by reacting each opioid in the silt’s extract with 2,2,2-trichloroethoxycarbonyl chloride (Troc-Cl). Further, reaction between Troc-Cl and each opioid generates two unique products that can be used to retrospectively identify the original opioid therefore serving as a corroborating tool for known opioids as well as new, unknown fentanyl analogs.
With the increase in electricity supply from clean energy sources, electrochemical reduction of carbon dioxide (CO 2 ) has received increasing attention as an alternative source of carbon-based fuels. As CO 2 reduction is becoming a stronger alternative for the clean production of chemicals, the need to model, optimize and control the electrochemical reduction of the CO 2 process becomes inevitable. However, on one hand, a first-principles model to represent the electrochemical CO 2 reduction has not been fully developed yet because of the complexity of its reaction mechanism, which makes it challenging to define a precise state-space model for the control system. On the other hand, the unavailability of efficient concentration measurement sensors continues to challenge our ability to develop feedback control systems. Gas chromatography (GC) is the most common equipment for monitoring the gas product composition, but it requires a period of time to analyze the sample, which means that GC can provide only delayed measurements during the operation. Moreover, the electrochemical CO 2 reduction process is catalyzed by a fast-deactivating copper catalyst and undergoes a selectivity shift from the product-of-interest at the later stages of experiments, which can pose a challenge for conventional control methods. To this end, machine learning (ML) techniques provide a potential approach to overcome those difficulties due to their demonstrated ability to capture the dynamic behavior of a chemical process from data. Motivated by the above considerations, we propose a machine learning-based modeling methodology that integrates support vector regression and first-principles modeling to capture the dynamic behavior of an experimental electrochemical reactor; this model, together with limited gas chromatography measurements, is employed to predict the evolution of gas-phase ethylene concentration. The model prediction is directly used in a proportional-integral (PI) controller that manipulates the applied potential to regulate the gas-phase ethylene concentration at energy-optimal set-point values computed by a real-time process optimizer (RTO). Specifically, the RTO calculates the operation set-point by solving an optimization problem to maximize the economic benefit of the reactor. Finally, suitable compensation methods are introduced to further account for the experimental uncertainties and handle catalyst deactivation. The proposed modeling, optimization, and control approaches are the first demonstration of active control for a CO 2 electrolyzer and contribute to the automation and scale-up efforts for electrified manufacturing of fuels and chemicals starting from CO 2 .
Bioorganic comparative analysis of desert soils, Precambrian shales and meteorites by automated pyrolysis-gas chromatography-mass spectrometry system for future Mars soil analysis
Gas-liquid chromatographic separation applied in derivatization chemistry of protein amino acids as N-trimethylsilyl /TMS/ esters
The benzylation of three, low-molecular weight N,N-disubstituted ethanolamines related to chemical warfare agents (CWAs) to furnish derivatives with improved Gas Chromatography-Mass Spectrometry (GC-MS) profiles is described. Due to their low molecular weight and polar nature, N,N-disubstituted ethanolamines are notoriously difficult to detect by routine GC-MS analyses during Organisation for the Prohibition of Chemical Weapons (OPCW) proficiency tests (PTs), particularly in scenarios when they are present at low levels (~1-10 ppm) amidst more abundant interferences. Our studies revealed that the optimal derivatization conditions involved the treatment of the ethanolamine with benzyl bromide in the presence of an inorganic base (e.g., Na 2 CO 3 ) in dichloromethane at 55 °C for 2 hours. This optimized set of conditions were then successfully applied to the derivatization of N,N-dimethylethanolamine, N,N-diethylethanolamine and N,N-diisopropylethanolamine present separately at 1 and 10 μg/mL concentrations in a glycerol-rich matrix sample featured in the 48 th OPCW PT. The benzylated derivatives of the three ethanolamines possessed retention times long enough to clear the massive glycerol-containing matrix interferences. In conclusion, the protocol herein is introduced as an alternative method for derivatization of these CWA and pharmaceutically important species and should find broad applicability in laboratories where their routine forensic analysis is carried out.
Developing gas-liquid chromatographic method for quantitative analysis of amino acids in blood plasma and urine
Results are presented on a computerized gas chromatograph-mass spectrometer analysis of methylene chloride and n-heptane extracts of a No. 2 fuel oil accommodated estuarine water sample. The analytical method is briefly described, and the limitations on the identifications are categorized. Some attempt was made to determine major and trace constituents in the water accommodate. Altogether 66 hydrocarbon compounds were identified specifically, and 75 compounds were partially identified. Seven compounds could be recognized as major constituents of the water accommodated oil and ten were present only as traces. The aromatic compounds found were alkyl benzenes, naphthalene, tetralin, indane, biphenyl, fluorene, anthracene, and some of their alkyl substituted isomers in the range of carbon numbers C7 to C15. Four n-alkanes, C10 to C13, were found along with four other assorted hydrocarbons.
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.
Information is given on the type and concentration of sterols, free fatty acids, and total fatty acids in plant tissue samples. All samples were analyzed by gas chromatography and then by gas chromatography-mass spectrometry combination. In each case the mass spectral data was accumulated as a computer printout and plot. Typical gas chromatograms are included as well as tables describing test results.
Mars is an important target for Astrobiology. A key goal of the MSL mission was to determine whether Mars was habitable in the past, a que-tion that has now been definitely determined to be yes. Another key goal for Mars exploration is to understand the origin and distribution of organic material on Mars; this question is being addressed by the SAM instrument on MSL, and will also be informed by two upcoming Mars exploration missions, ExoMars and Mars 2020. These latter two missions have instrumentation capable of detecting and characterize organic molecules. Over the next decade, these missions will analyze organics in surface, near-surface and sub-surface samples. Each mission has the capability to analyze organics by different methods (pyrolysis gas chromatography-mass spectrometry [py-GC-MS]; laser desorption and thermal volatilization GC-MS; and Raman spectroscopy). Plausibly extraterrestrial organics were recently discovered by the Mars Science Laboratory (MSL), providing an important first step towards understanding the organic inventory on Mars [1]. The compounds detected were chlorobenzenes and chloroalkanes, but it was argued that chlorination of these compounds occurred during pyrolysis of samples containing unchlorinated organics in the presence of perchlorate. A recent report analyzed a suite of aromatic (benzene, toluene, benzoic acid, phthalic acid, and mellitic acid) and aliphatic (acetic acid, propane, propanol, and hexane) by pyrolysis under SAM-like conditions in the presence of perchlorate to attempt to constrain possible precursor molecules for the organic molecules detected on Mars. For aromatic compounds, the aromatic acids all readily produced SAM-relevant chlorobenzes, whereas benzene and toluene did not. This observation suggests that the chlorobenzene detected on Mars could have derived from compounds like mellitic acid, consistent with the previous hypothesis by Benner et al. [3]. Among the aliphatic molecules, it was shown that pyrolysis of alkanes and alcohols in the presence of perchlorates produced polychlorine containing chloro-alkanes similar to what was observed on Mars. Surpris-ingly, however, similar treatment of acetic acid pro-duced chloroketones, instead, and no chloroalkanes were reported. This suggests that the chloroalkanes detected in the Sheepbed mudstone were not derived from aliphatic carboxylic acids, but instead were from more reduced alcohols or even alkanes, or perhaps were degradation products of more complicated organic material. Because organics analyses on mars will rely heavily on py-GC-MS of perchlorate-containing samples over the next decade, it is important to understand the fate of organic molecules of biotic and abiotic origin under such conditions. In this work we begin a series of experiments to improve our understanding of products generated during py-GC-MS of increasingly complex organic molecules (esters, amides, peptides, nucleic acids, fatty acids) in the presence of perchlorate.
Lithium–sulfur (Li–S) batteries hold significant promise for electric vehicles and aviation due to their high energy density and cost-effectiveness. However, understanding the root causes of performance degradation remains a formidable challenge, as the interplay of multiple factors obscures key failure mechanisms. A major limitation has been the inability to quantify soluble sulfur species within practical detection limits accurately and to correlate electrochemical processes with associated physical inventory changes. Here, we introduce the high-performance liquid chromatography-ultraviolet spectroscopy and gas chromatography sequential characterization (HUGS) toolkit, capable of precisely quantifying seven distinct sulfur and polysulfide species at concentrations as low as 40 ppb. HUGS has been successfully applied to practical coin and pouch cells without requiring cell modification. Furthermore, our self-developed software, Dr HUGS, enhanced the data analysis speed by over 30 times, enabling multi-source data integration and delivering comprehensive analysis results within minutes. Using HUGS, we identify significant capacity losses from inactive lithium and sulfur during initial cycles and sulfide-rich solid–electrolyte interphase (SEI) formation on the anode during later cycles. Notably, our findings reveal that soluble polysulfides have minimal contributions to capacity loss, challenging long-standing assumptions. Moreover, HUGS demonstrates that constant-pressure setups in Li–S pouch cells improve compositional uniformity compared to constant-gap configurations. For sulfurized polyacrylonitrile (SPAN) cathodes, unique issues such as non-sulfide SEI formation and lithium pulverization are observed, which can be mitigated through localized high-concentration electrolytes to enhance lithium inventory retention. By enabling precise quantification of critical inventory components, HUGS provides transformative insights into failure mechanisms across various electrolytes and cathode chemistries, guiding rational design strategies for next-generation energy storage systems.