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Chemical classification of iron meteorites with Ge concentrations between 80 and 200 ppm, reporting concentrations of Ge, Ni, Ga and Ir
The quantization of the Ga and Ge contents in iron meteorites, which is used as a key parameter in the chemical classification of iron meteorites, is discussed in terms of nebular condensation. The calculation of nebular equilibrium condensation is examined, taking into account the dependence of the activity coefficient on temperature and composition, and recent calculations of the condensation temperatures of Ga, Ge, Sb, Au, As and Cu are presented, noting that Ge is the most volatile siderophile, followed by Ga and Sb. The narrow intragroup ranges of Ga and Ge are interpreted in terms of minimal fractionation during core crystallization, while the larger ranges of Sb are attributed to its significantly smaller solid/liquid distribution coefficient in IIIAB meteorites.
The paper discusses the chemical classification of independent iron meteorites which include 57 meteorites based on structural observations and concentrations of Ni, Ga, Ge, and Ir. Instrumental neutron activation analysis indicates that five previously studied irons with very high Ge/Ga ratios are compositionally closely related and can be gathered together as group IIF; a previously unstudied iron, Dehesa, has the highest Ge/Ga ratios known in an iron meteorite, a ratio 18 times higher than that in CI chondrites. In terms of Ge/Ga ratios and other properties, group IIF shows genetic links to the Eagle station pallasites and CO/CV chondrites. The iron with the highest Ni concentration, Oktibbeha County, is a member of group IAB, and it extends the concentration ranges of all elements in this nonmagnetic group.
Iron and stony iron meteorites chemical classification, noting Ni, Ga, Ge and Ir concentrations and metal phases
Accurately classifying chemical structures is essential for cheminformatics and bioinformatics, including tasks such as identifying bioactive compounds of interest, screening molecules for toxicity to humans, finding non-organic compounds with desirable material properties, or organizing large chemical libraries for drug discovery or environmental monitoring. However, manual classification is labor-intensive and difficult to scale to large chemical databases. Existing automated approaches either rely on manually constructed classification rules, or are deep learning methods that lack explainability. This work presents an approach that uses generative artificial intelligence to automatically write chemical classifier programs for classes in the Chemical Entities of Biological Interest (ChEBI) database. These programs can be used for efficient deterministic run-time classification of SMILES structures, with natural language explanations. The programs themselves constitute an explainable computable ontological model of chemical class nomenclature, which we call the ChEBI Chemical Class Program Ontology (C3PO). We validated our approach against the ChEBI database, and compared our results against deep learning models and a naive SMARTS pattern based classifier. C3PO outperforms the naive classifier, but does not reach the performance of state of the art deep learning methods. However, C3PO has a number of strengths that complement deep learning methods, including explainability and reduced data dependence. C3PO can be used alongside deep learning classifiers to provide an explanation of the classification, where both methods agree. The programs can be used as part of the ontology development process, and iteratively refined by expert human curators.
Ge and Ga concentration in selected Fe meteorites used to determine quantization in terms of multiple parent body hypothesis and planetary fractionation processes
Fe meteorites with high Ge content, tabulating concentrations of Ni, Ga, Ge and Ir
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Results are reported for determinations of Ni, Ga, Ge, and Ir concentrations in 106 iron meteorites. Three new groups are defined (IC, IIE, and IIIF) which contain 10, 12, and 5 irons, respectively. It is noted that group IC is a cohenite-rich group distantly related to IA, group IIE consists of those irons previously designated as Weekeroo Station type together with five others having similar compositions but diverse structures, and group IIIF is a well-defined group of low-Ni and low-Ge irons. Several anomalous irons are discussed, including a cluster of five plessitic octahedrites and ataxites with Ge/Ga atomic ratios ranging from 10 to 16 and a meteorite that has the second highest Ni content of any iron. It is shown that the IIE irons are compositionally similar to the mesosiderites and pallasites, and it is suggested that the three groups probably formed at approximately the same heliocentric distance.
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Concentrations of 14 elements in the metal of 38 iron meteorites and a pallasite are reported. Three samples are paired with previously classified irons, raising the number of well-classified, independent iron meteorites to 598. Several of the new irons are from Antarctica. Of 24 independent irons from Antarctica, eight are ungrouped, a much higher fraction than that among all classified irons. The difference is probably related to the fact that the median mass of Antarctic irons is about two orders of magnitude smaller than that of non-Antarctic irons. Smaller meteoroids may tend to sample a larger number of asteroidal source regions, perhaps because small meteoroids tend to have higher ejection velocities or because they have random-walked a greater increment of orbital semimajor axis away from that of the parent body.
Some degree of correlation was noted between the thermomagnetic behavior of the carbonaceous chondrites and the chemical-petrographic classification scheme of Van Schmus and Hayes.
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The science instruments on the Mars Exploration Rover (MER) Spirit have provided an enormous amount of chemical and mineralogical data during more than 1450 sols of exploration at Gusev crater. The Moessbauer (MB) instrument identified 10 Fe-bearing phases at Gusev Crater: olivine, pyroxene, ilmenite, chromite, and magnetite as primary igneous phases and nanophase ferric oxide (npOx), goethite, hematite, a ferric sulfate, and pyrite/marcusite as secondary phases. The Miniature Thermal Emission Spectrometer (Mini-TES) identified some of these Fe-bearing phases (olivine and pyroxene), non- Fe-bearing phases (e.g., feldspar), and an amorphous high-SiO2 phase near Home Plate. Chemical data from the Alpha Particle X-Ray Spectrometer (APXS) provided the framework for rock classification, chemical weathering/alteration, and mineralogical constraints. APXS-based mineralogical constraints include normative calculations (with Fe(3+)/FeT from MB), elemental associations, and stoichiometry (e.g., 90% SiO2 implicates opalline silica). If Spirit had cached a set of representative samples and if those samples were returned to the Earth for laboratory analysis, what value is added by Mars Sample return (MSR) over and above the mineralogical and chemical data provided by MER?
One design goal of Closed Ecological Life Support Systems (CELSS) for long duration space missions is to maintain an atmosphere which is healthy for all the desirable biological species and not deleterious to any of the mechanical components in that atmosphere. CELESS design must take into account the interactions of at least six major components; (1) humans and animals, (2) higher plants, (3) microalgae, (4) bacteria and fungi, (5) the waste processing system, and (6) other mechanical systems. Each of these major components can be both a source and a target of airborne trace contaminants in a CELSS. A range of possible airborne trace contaminants is discussed within a chemical classification scheme. These contaminants are analyzed with respect to their probable sources among the six major components and their potential effects on those components. Data on airborne chemical contaminants detected in shuttle missions is presented along with this analysis. The observed concentrations of several classes of compounds, including hydrocarbons, halocarbons, halosilanes, amines and nitrogen oxides, are considered with respect to the problems which they present to CELSS.
The paired howardite breccias Mt. Pratt (PRA) 04401 and PRA 04402 are notable for their high proportion of carbonaceous chondrite clasts [1]. They consist predominantly of coarse (0.1-7 mm) diogenite (orthopyroxene), eucrite (plagioclase + pyroxene), and carbonaceous chondrite clasts set in a finer grained matrix of these same materials. Coarse C-chondrite clasts up to 7 mm are composed mainly of fine-grained phyllosilicates with lesser sulfides and high-mg# anhydrous magnesian silicates. Most of these clasts appear to be texturally consistent with CM2 classification [1] and some contain relict chondrules. The clasts are angular and reaction or alteration textures are not apparent in the surrounding matrix. PRA 04401 contains about 70 modal% C-chondrite clasts while PRA 04402 contains about 7%. Although many howardites are known to contain abundant C-chondrite clasts [2,3,4], PRA 04401 is, to our knowledge, the most chondrite-rich howardite lithology identified to date. Low EPMA totals from CM2-type clasts in other howardites suggest that they frequently contain 10 wt% or more water [2], a figure consistent with their mineralogy. PRA 04401, therefore, demonstrates the potential for hydrous lithologies with greater than 5 wt% water to occur locally within the nominally anhydrous HED parent body. Since the origin of this water is xenogenic, it might therefore be concentrated in portions of the asteroid surface where it would be more readily observable by remote sensing techniques. We plan to further examine C-chondrite clasts in PRA 04401/2 with the intent of establishing firm chemical classification, estimating water content, and evaluating their relationship with the host breccia. To help place them in context of the HED parent, we will also compare these breccias with other howardites to evaluate which lithologies are likely to be more prevalent on the asteroid surface.
ChemEcho is a tool that converts tandem mass spectra into embeddings used to build machine learning (ML) models with fully explainable predictions. It provides an API for transforming raw tandem mass spectral data into embeddings, along with functions for training and validating ML models. Additionally, it includes utilities for retrieving and cleaning training data. ChemEcho is broadly applicable in ML pipelines that use tandem mass spectra for a variety of tasks, such as chemical classification or bioactivity mining. While there are existing methods to generate embeddings from fragmentation data, ChemEcho's approach ensures that predictions remain interpretable, enabling experts to evaluate results and generate hypotheses about the underlying data.