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Record W4310438816 · doi:10.1364/ome.478301

Monitoring sulfur loss in polycrystalline solid solutions of CaS-La<sub>2</sub>S<sub>3</sub> by Raman spectroscopy and X-ray diffraction

2022· article· en· W4310438816 on OpenAlexaff
Matthew Havel, Alexandros Kostogiannes, W. Taylor Shoulders, Victoria L. Blair, Daniel McGill, Clara Rivero‐Baleine, Rade Bunijevac, Jeffery Eichler, Matthew R. Kincer, Myungkoo Kang, Kathleen Richardson, Romain Gaumé

Bibliographic record

VenueOptical Materials Express · 2022
Typearticle
Languageen
FieldEngineering
TopicChalcogenide Semiconductor Thin Films
Canadian institutionsLockheed Martin (Canada)
FundersFlorida High Tech Corridor Council
KeywordsRaman spectroscopyCrystalliteStoichiometryMaterials scienceSulfurAnalytical Chemistry (journal)Thermoelectric effectFull width at half maximumSulfideX-ray crystallographyDiffractionSpectroscopySolid solutionChemistryOpticsPhysical chemistryOptoelectronics

Abstract

fetched live from OpenAlex

Controlling stoichiometry in materials is a critical consideration in advanced applications. Non-stoichiometry due to sulfur excess or sulfur loss is observed in the CaS-La 2 S 3 (CLS) solid solution, a promising optical and thermoelectric material. We show that Raman spectroscopy can be used to assess deviation from stoichiometry in polycrystalline CLS. Use of this technique shows an increase in the full width at half maximum (FWHM) of the A 1 vibrational peak, associated with disorder on the sulfur sublattice. This method is validated using X-ray diffraction, where a decrease in the lattice parameter is observed on either side of the stoichiometric composition. This paper illustrates the usefulness of Raman spectroscopy as a complementary metrology technique to assess stoichiometry in sulfide-based polycrystalline ceramics.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.010
GPT teacher head0.227
Teacher spread0.217 · 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.

Study designBench or experimental
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

Citations7
Published2022
Admission routes1
Has abstractyes

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