CLASSIFICATION OF FUELS- THERMODYNAMIC AND REACTION KINETIC PROPERTIES IN OTTO ENGINES
Chemical composition, heats of combustion & molecular bond energy of fuels - fuel consumption of otto engines
ANALYTIC FUNCTION↗
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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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Simulating water dynamics related to pedogenesis across space and time: Implications for four-dimensional digital soil mapping
Digital soil mapping (DSM) relies on machine-learning and geostatistics to represent soil property observations across space. DSM techniques are powerful but often empirical, being limited to the quality and density of point samples. Water dynamics are closely related to soil variability, and the physics that govern water movement are well known. Hydrological properties can hence be simulated by physical models through space and time, unveiling key characteristics about soils. We propose the use of hydrologic models to map soils across the surface (2D), depth (1D), and time (1D)–which provides a 4D approach to digital soil mapping (4DSM). The Distributed Hydrology Soil Vegetation Model (DHSVM) was applied to a watershed currently under pasture. Moisture sensors and wells were installed at different depths in the watershed on summit, sideslope and toeslope positions to validate the model. DHSVM simulations of soil moisture distribution and depth to saturation were performed during the hydrological year (October 2008-September 2009). Clusters of similar pixels based on soil moisture values were determined using Dynamic Time Warping (DTW) to align temporal data and K-means. Clustering was performed both seasonally and for the entire year. Temporal patterns simulated by DHSVM matched measurements given by moisture sensors and wells. Seasonal clusters differed from the annual cluster. Distinct clusters were observed for each season and with depth, showing that spatiotemporal soil variability is lost when statically assessing soils. Spatiotemporal clusters corroborated field observations of fragipan occurrence not explicitly spatially mapped by Soil Survey Geographic Database (SSURGO). If a connection can be made between water and soils, static and dynamic soil variability can be predicted using physically based hydrologic models. Hydrologic models can benefit soil mapping by enabling reliable 4D simulation of water dynamics, which are fundamental to soil variability and soil classification and directly relate to biological, physical and chemical soil processes not captured by typical soil sampling protocols.
54 ENVIRONMENTAL SCIENCES↗
Chemometrics and visible diffuse reflectance spectroscopy to classify plutonium dioxide
Diffuse reflectance (DR) spectra in the Vis-NIR (∼380–1050 nm) region were acquired for a series of PuO 2 samples with a spot size of about 10 × 10 μm. Two batches of six PuO 2 samples, synthesized approximately 7.5 months apart, were prepared using both Pu(III) and Pu(IV) oxalate precursors at three distinct calcination temperatures (450, 650, and 950 °C). This yielded a total of 12 PuO 2 samples and 433 DR spectra. The DR spectrum of PuO 2 contained numerous peaks in the visible region, and characteristic features were identified with respect to calcination temperature and chemistry. A distinct peak multiplet near 615 nm was observed for samples prepared at low calcination temperatures, and a peak near 660 nm was observed for higher calcination temperatures. A multivariate classification strategy based on principal component analysis (PCA) was developed to distinguish PuO 2 calcination temperatures of 450, 650, and 950 °C with 100 % accuracy. Classification results also indicate the potential to distinguish chemical processing history (i.e., Pu(III) or Pu(IV)) based on the spectra with 72 % accuracy based on k-nearest neighbors applied to the PCA scores. Partial least squares discriminant analysis was used to identify variation among batches with 88 % accuracy and found that peaks near 669, 681, 811, and 970 nm were the most useful for predicting the batch identity. Here, this work demonstrates how micro-diffuse reflectance spectroscopy and chemometrics can be used to classify PuO 2 processing history based on Vis-NIR spectral features. Combining the chemometric approach with mapping sequences could provide a rapid, nondestructive approach to classify Pu oxide materials for environmental, forensics, and nonproliferation applications.
Actinide↗
The soils of polar wastes and the role of B. N. Gorodkov in their study
Chemical and biochemical reactions of soil formation, and classification of Arctic soils
Mikhaylov, I. S.↗
Chemistry and mineralogy of the Lost City meteorite
Lost City /Oklahoma/ meteorite classification as type H5 chondrite based on bulk chemical composition, mineralogy and petrography
Doan, A. S., Jr.↗
A Cuban tektite
Cuban tektite classification based on age, discussing physical properties and chemical composition
Garlick, G. D.↗
Meteorites and the early solar system
Chondrite classification, primordial matter composition and early solar system chemical processes, discussing cosmic gas condensation and refractory element fractionation
Anders, E.↗
Alpha ketoacid decarboxylases: Diversity, structures, reaction mechanisms, and applications for biomanufacturing of platform chemicals and fuels
In living cells, alpha-ketoacid decarboxylases (KDCs, EC 4.1.1.-) are a class of enzymes that convert alpha-ketoacids into aldehydes through decarboxylation. These aldehydes serve as either drop-in chemicals or precursors for the biosynthesis of alcohols, carboxylic acids, esters, and alkanes. These compounds play crucial roles in cellular metabolism and fitness and the bioeconomy, facilitating the sustainable and renewable biomanufacturing of platform chemicals and fuels. This review explores the diversity and classification of KDCs, detailing their structures, mechanisms, and functions. We highlight recent advancements in repurposing KDCs to enhance their efficiency and robustness for biomanufacturing. Additionally, we present modular KDC-dependent metabolic pathways for the microbial biosynthesis of aldehydes, alcohols, carboxylic acids, esters, and alkanes. Lastly, we discuss recent developments in the modular cell engineering technology that can potentially be applied to harness the diversity of KDC-dependent pathways for biomanufacturing platform chemicals and fuels.
09 BIOMASS FUELS↗
Air Classification of Forestry Residues for Fast Pyrolysis
Understanding critical biomass attributes through efficient fractionation is crucial for advancing sustainable pyrolysis for renewable energy and chemical production. This study investigates the intricate relationship between biomass preprocessing and pyrolysis product yields, employing the air classification technique for the treatment of loblolly pine residues with varying moisture content. A comprehensive exploration of the physicochemical properties of air-classified loblolly pine informs a sophisticated pyrolysis simulation model. Given the complex and multifaceted nature of biomass pyrolysis, operating across diverse temporal and spatial scales, a pyrolysis kinetics-based CFD–DEM simulation method is employed to predict product yields. Results showed that the elevated moisture content amplifies particle adhesiveness, necessitating augmented air velocities for effective separation, thereby influencing the efficiency of the separation process. While carbon and hydrogen contents exhibit relative stability across diverse moisture contents and blower frequencies, the oxygen content undergoes noticeable changes. For example, the oxygen contents were measured as 29.2 and 38.6 wt% in the light fraction of 30% moisture content sample at blower frequencies of 10 and 20 Hz, respectively. An intriguing finding emerges from pyrolysis simulation, indicating that a lower blower frequency in air classification moderately enhances bio-oil yield and significantly improves its quality, particularly in terms of water content. For instance, the water content in the bio-oil was about 1.5% and 10% in the heavy and light fractions, respectively from 10% moisture sample under 15 Hz blower frequency.
09 - BIOMASS FUELS↗
The Apollo 14 regolith - Chemistry of cores 14210/14211 and 14220 and soils 14141, 14148, and 14149
Neutron activation analysis was performed on bulk and size fractions from drive tube specimens from 39 cm and 16.5 cm depths, and soil samples taken at the Apollo 14 landing site. Chemical data were obtained for 31 major, minor, and trace elements in the KREEPy soils. The cores were homogeneous in chemical composition, containing 20% LKFM, 15% mare basalt, 6% ANT, and 59% high-K KREEP, according to the classifications of Laul and Papike (1980). The meteoritic fraction was 3-7% for both cores, while the chemical compositions of both cores and soils were similar. Differences were detected in the fractions finer than 10 microns, which were more feldspathic than the coarser samples. The similarities between the grains 1000-90 micron in diam and less than 10 micron in diam, in terms of chemical contents, indicates that the observed agglutinates were derived from fusion of the finest grained fraction. The dominant soil-forming processes were comminution and vertical mixing of the regolith.
Laul, J. C.↗
Apollo 17 high-Ti mare basalts - New bulk compositional data, magma types, and petrogenesis
Bulk compositional and mineral chemical data for 28 previously unanalyzed samples support the classification of Apollo-17 high-Ti mare basalts into three-types (A, B, and C), defined on the basis of analyses of fine-grained basalts. The most MgO- and TiO2-rich fine-grained basalts of these types appear to be the best choices for representing the compositions of the parent magmas.
Warner, R. D.↗
Mineralogy versus bulk composition of the carbonaceous chondrite clast Kaidun 2
Kaidun is an unusual chondritic meteorite. Its main mass is of CR-type (Kaidun I) and contains CI-like (Kaidun II), EL, and EH clasts. The classification of Kaidun II based on mineralogy and bulk chemical criteria does not allow an unambigious assignment to CI. Here we report on the bulk chemical composition and the mineralogy of the Kaidun II clasts. The results of our investigation show that Kaidun II has some characteristics of both CI and CM chondrites. A possible relationship to the texture and composition of micrometeorites is indicated. The Kaidun II clast shows some conflicting mineralogical and chemical characteristics that do not allow an unambigious assignment to the well established carbonaceous chondrite classes. The overall texture of the matrix-rich clast and morphology of opaques are typical for CI's. Bulk matrix and bulk sample compositions reveal similarities with CM's. In addition, the presence of low-Fe Mn-enriched olivines and the relatively high abundance of enstatite resembles some micrometeorites.
Brandstaetter, F.↗
Human Health Risk Assessment Simulations in a Distributed Environment for Shuttle Launch
During the launch of a rocket under prevailing weather conditions, commanders at Cape Canaveral Air Force station evaluate the possibility of whether wind blown toxic emissions might reach civilian and military personnel in the near by area. In our model, we focused mainly on Hydrogen chloride (HCL), Nitrogen oxides (NOx) and Nitric acid (HNO3), which are non-carcinogenic chemicals as per United States Environmental Protection Agency (USEPA) classification. We have used the hazard quotient model to estimate the number of people at risk. It is based on the number of people with exposure above a reference exposure level that is unlikely to cause adverse health effects. The risk to the exposed population is calculated by multiplying the individual risk and the number in exposed population. The risk values are compared against the acceptable risk values and GO or NO-go situation is decided based on risk values for the Shuttle launch. The entire model is simulated over the web and different scenaria can be generated which allows management to choose an optimum decision.
Thirumalainambi, Rajkumar↗
SHERLOC Investigations at the Máaz and Séítah formations within Jezero crater
Introduction: The Scanning Habitable Environments with Raman and Luminescence for Organics and Chemicals (SHERLOC) instrument combines microscopic imaging, native fluorescence and Raman spectroscopy to better understand the mineral and chemical makeup of rocks on the martian surface. Native fluorescence emissions from aromatic organic species allow for detection and classification of aromatic organic molecules, whereas Raman scattered photons from molecules allow identification of functional groups of organics, chemicals, and minerals. These signatures are obtained on a 100 micron spatial scale and collocated to images so textures, minerals and chemicals can all be compared [1]. Results: SHERLOC has been operating on Mars since February 18, 2021. As of this writing, we have analyzed 3 natural surfaces, and 5 abraded rock patches created during the Crater Floor Campaign within Jezero crater [2]. The Guillaumes target (from the Roubion outcrop, Roubion member of the Máaz Formation) is dominated by Ca-sulfate with patches of perchlorate. The Bellegarde target (from the Rochette outcrop, Rochette member of the Máaz Formation) exhibits Raman peaks that match hydrated Ca-sulfate, amorphous/microcrystalline silicate (AMS), carbonate, and phosphate phases. A fluorescence doublet at ~305 and ~325 nm was detected and is most likely due to indigenous organic material in the sample. The Garde target (from the Bastide outcrop, Bastide member of the Séítah Formation) is dominated by olivine and carbonate with AMS occurring across the material. The Dourbes target (from the Brac outcrop, Bastide member of the Séítah Formation) is dominated by olivine and shows minor amounts of carbonate, hydrated Ca-sulfate, and AMS. The Quartier target exhibits a large sulfate feature, as well as carbonate, perchlorate, olivine and a fluorescence doublet at 305 and 325 nm and is very similar to that observed at Bellegarde. In each of these samples we have identified fluorescence features that are likely aromatic organics native to the rock interiors. Acknowledgments: This work was carried out at the Jet Propulsion Laboratory, The California Institute of Technology under a contract from NASA.
L W Beegle↗
An Evolutionary System of Mineralogy. Part I: Stellar Mineralogy (>13 to 4.6 Ga)
Minerals preserve records of the physical, chemical, and biological histories of their origins and subsequent alteration, and thus provide a vivid narrative of the evolution of Earth and other worlds through billions of years of cosmic history. Mineral properties, including trace and minor elements, ratios of isotopes, solid and fluid inclusions, external morphologies, and other idiosyncratic attributes, represent information that points to specific modes of formation and subsequent environmental histories—information essential to understanding the co-evolving geosphere and biosphere. This perspective suggests an opportunity to amplify the existing system of mineral classification, by which minerals are defined solely on idealized end-member chemical compositions and crystal structures. Here we present the first in a series of contributions to explore a complementary evolutionary system of mineralogy—a classification scheme that links mineral species to their paragenetic modes. The earliest stage of mineral evolution commenced with the appearance of the first crystals in the universe at >13 Ga and continues today in the expanding, cooling atmospheres of countless evolved stars, which host the high-temperature (T > 1000 K), low-pressure (P < 10-2 atm) condensation of refractory minerals and amorphous phases. Most stardust is thought to originate in three distinct processes in carbon- and/or oxygen-rich mineral-forming stars: (1) condensation in the cooling, expanding atmospheres of asymptotic giant branch stars; (2) during the catastrophic explosions of supernovae, most commonly core collapse (Type II) supernovae; and (3) classical novae explosions, the consequence of runaway fusion reactions at the surface of a binary white dwarf star. Each stellar environment imparts distinctive isotopic and trace element signatures to the micro- and nanoscale stardust grains that are recovered from meteorites and micrometeorites collected on Earth’s surface, by atmospheric sampling, and from asteroids and comets. Although our understanding of the diverse mineral-forming environments of stars is as yet incomplete, we present a preliminary catalog of 41 distinct natural kinds of stellar minerals, representing 22 official International Mineralogical Association (IMA) mineral species, as well as 2 as yet unapproved crystalline phases and 3 kinds of non-crystalline condensed phases not codified by the IMA.
Robert M. Hazen↗
Chemical processes in the early solar system, as inferred from meteorites.
Chemical analysis, isotopic composition, structure and mineralogy of meteorites for classification into irons and stones, discussing time variation of temperature, pressure, etc
Anders, E.↗
The Calipso Version 4.5 Stratospheric Aerosol Subtyping Algorithm
The accurate classification of aerosol types injected into the stratosphere is important to properly characterize their chemical and radiative impacts within the Earth climate system. The updated stratospheric aerosol subtyping algorithm used in the version 4.5 (V4.5) release of the Cloud Aerosol Lidar with Orthogonal Polarization (CALIOP) level 2 data products now delivers more comprehensive and accurate classifications than its predecessor. The original algorithm identified four aerosol subtypes for layers detected above the tropopause: volcanic ash, smoke, sulfate/other, and polar stratospheric aerosol (PSA). In the revised algorithm, sulfates are separately identified as a distinct, homogeneous subtype, and the diffuse, weakly scattering layers previously assigned to the sulfate/other class are recategorized as a fifth “unclassified” subtype. By making two structural changes to the algorithm and revising two thresholds, the V4.5 algorithm improves the ability to discriminate between volcanic ash and smoke from pyrocumulonimbus injections, improves the fidelity of the sulfate subtype, and more accurately reflects the uncertainties inherent in the classification process. The 532 nm lidar ratio for volcanic ash was also revised to a value more consistent with the current state of knowledge. This paper briefly reviews the previous version of the algorithm (V4.1 and V4.2) then fully details the rationale and impact of the V4.5 changes on subtype classification frequency for specific events where the dominant aerosol type is known based on the literature. Classification accuracy is best for volcanic ash due to its characteristically high depolarization ratio. Smoke layers in the stratosphere are also classified with reasonable accuracy, though during the daytime a substantial fraction are misclassified as ash. It is also possible for mixtures of ash and sulfate to be misclassified as smoke. The V4.5 sulfate subtype accuracy is less than that for ash or smoke, with sulfates being misclassified as smoke about one-third of the time. However, because exceptionally tenuous layers are now assigned to the unclassified subtype and the revised algorithm levies more stringent criteria for identifying an aerosol as sulfate, it is more likely that layers labeled as this subtype are in fact sulfate compared to those assigned the sulfate/other classification in the previous data release.
Jason L Tackett↗
Detection and imaging of chemicals and hidden explosives using terahertz time-domain spectroscopy and deep learning
Detecting concealed chemicals and explosives remains a critical challenge in global security. Terahertz time-domain spectroscopy (THz-TDS) offers a promising non-invasive and stand-off detection technique owing to its ability to penetrate optically opaque materials without causing ionization damage. While many chemicals exhibit distinct spectral features in the terahertz range, conventional terahertz-based detection methods often struggle in real-world environments, where variations in sample geometry, thickness, and packaging can lead to inconsistent spectral responses. In this study, we present a chemical imaging system that integrates THz-TDS with deep learning to enable accurate pixel-level identification and classification of different explosives. Operating in reflection mode and enhanced with plasmonic nanoantenna arrays, our THz-TDS system achieves a peak dynamic range of 96 dB and a detection bandwidth of 4.5 THz, supporting practical, stand-off operation. By analyzing individual time-domain pulses with deep neural networks, the system exhibits strong resilience to environmental variations and sample inconsistencies. Blind testing across eight chemicals—including pharmaceutical excipients and explosive compounds—resulted in an average classification accuracy of 99.42% at the pixel level. Notably, the system maintained an average accuracy of 88.83% when detecting explosives concealed under opaque paper coverings, demonstrating its robust generalization capability. These results highlight the potential of combining advanced terahertz spectroscopy with neural networks for highly sensitive and specific chemical and explosive detection in diverse and operationally relevant scenarios.
Imaging and sensing↗