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Record W3022424959 · doi:10.1111/maps.13346

Co-ordinated UV Reflectance and Raman Spectroscopy of Martian Meteorites and Terrestrial Analogue Samples

2019· article· en· W3022424959 on OpenAlexaboutno aff
M. M. Grady, Feargus Abernethy, Thomas J. Barrett, C. Batty, C. C. Bedford, Pamela Rowden

Bibliographic record

VenueOpen Research Online (The Open University) · 2019
Typearticle
Languageen
FieldPhysics and Astronomy
TopicPlanetary Science and Exploration
Canadian institutionsnot available
Fundersnot available
KeywordsMeteoriteAstrobiologyMartianReflectivityRaman spectroscopyMars Exploration ProgramSpectroscopyRemote sensingGeologyMineralogyMaterials scienceChemistryOpticsPhysicsAstronomy

Abstract

fetched live from OpenAlex

Introduction: Reflectance spectroscopy acquired by orbiting spacecraft over a range of wavelengths is a common tool used to determine the mineralogy of planetary surfaces. Hydrous and anhydrous silicate minerals, sulphides, sulphates and carbonates have prominent features in the Vis-NIR (λ ~ 300 – 2100 nm). Organic species are detectable at lower wavelengths, λ ~ 180 – 300 nm. Raman spectroscopy is a method employed to identify minerals and organic species <i>in situ</i> and will be part of the instrument suites carried by the Mars 2020 and ExoMars 2020 rovers for analysis of surface materials.<br/><br/>Aim: to investigate mineral-organic relationships in martian meteorites by UV-Vis microspectrophotometry (MSP) and Raman spectroscopy. Our intention is to produce spatial distributions of organic species on broken surfaces of martian meteorites and terrestrial analogues, providing a series of measurements to assist in interpretation of data acquired on Mars. That would then help in identifying suitable (potentially organic-rich) rocks for acquisition<br/>and caching. The project has synergies with the investigations that the Sherloc instrument will carry out on the surface of Mars<br/><br/>Method: Diffuse reflectance spectra of a series of minerals, powdered organic compounds and martian meteorites were obtained using a CRAIC UV-Vis MSP fitted with 3 Cassegrain objective lenses. The system has two lamps, deuterium and xenon, that operate in parallel, resulting in a useful spectral range of 200 – 900 nm. The lamp output is delivered to the microscope by fibre-optic, resulting in a focusable area of 2 x 2 µm to 10 x 10 µm, depending on the combination of mirrors selected. Incidence and exit angles are perpendicular to sample. Raman spectra were obtained using a Jobin Yvon Labram HR laser Raman microprobe equipped with 3 lasers (514 nm Ar ion, 632 nm HeNe and a 785 nm diode); separate spectra were taken using each of the three lasers. Material (usually &lt; 5 mg) broken from chips of martian meteorites and terrestrial basalts from Iceland, Norway and Canada were coarsely crushed (grain-size &lt; 200µm) and placed on quartz-glass slides for UV-Vis then Raman analysis.<br/><br/>Results: UV-Vis spectra (240 – 400 nm) from broken surfaces of inorganic minerals relevant to the composition of Mars’ surface and from the Chassigny, Nakhla, Shergotty and Zagami martian meteorites are shown in the figures below. The marked weak features at 257 nm and 280 nm are possibly from electronic transitions, whilst the stronger features in the meteorites at 320 nm and 345 nm may be from organic functional groups. Raman analysis will assist in absolute identification of these features. The next stage of the project is to re-analyse the specimens after they<br/>have been mixed with (a) individual organic molecules and (b) combinations of molecules, with the aim of determining the minimum concentration of organics detectable, both in UV-Vis and Raman spectra.<br/><br/>Summary: UV-Vis microspectrophotometry complemented by Raman spectroscopy is a potential tool for rapid, non-invasive and non-destructive laboratory analysis of planetary samples. Comparison with spectra from organic and inorganic species should allow elucidation of interactions between organics and mineral matrices, and assist in interpretation of data from the surface of Mars.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.544
Threshold uncertainty score0.704

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.088
GPT teacher head0.365
Teacher spread0.277 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2019
Admission routes1
Has abstractyes

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