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Record W3134738706 · doi:10.1039/d1na00123j

Probing the coupling between the components in a graphene–mesoporous germanium nanocomposite using high-pressure Raman spectroscopy

2021· article· en· W3134738706 on OpenAlexafffund
Denis Machon, Stéphanie Sauze, Richard Arès, Abderraouf Boucherif

Bibliographic record

VenueNanoscale Advances · 2021
Typearticle
Languageen
FieldMaterials Science
TopicGraphene research and applications
Canadian institutionsInstitut interdisciplinaire d'innovation technologiqueUniversité de Sherbrooke
FundersInstitut National des Sciences Appliquées de LyonLABEX iMUSTCentre National de la Recherche ScientifiqueFonds de recherche du Québec – Nature et technologiesUniversité Grenoble AlpesAgence Nationale de la RechercheUniversité de SherbrookeUniversité de LyonIndian National Science Academy
KeywordsNanocompositeRaman spectroscopyGrapheneMaterials scienceCoupling (piping)Mesoporous materialGermaniumNanotechnologyOptoelectronicsComposite materialChemistryOpticsOrganic chemistryCatalysisPhysics

Abstract

fetched live from OpenAlex

The nature of the interface between the components of a nanocomposite is a major determining factor in the resulting properties. Using a graphene-mesoporous germanium nanocomposite with a core-shell structure as a template for complex graphene-based nanocomposites, an approach to quantify the interactions between the graphene coating and the component materials is proposed. By monitoring the pressure-induced shift of the Raman G-peak, the degree of coupling between the components, a parameter that is critical in determining the properties of a nanocomposite, can be evaluated. In addition, pressure-induced transformations are a way to tune the physical and chemical properties of materials, and this method provides an opportunity for the controlled design of nanocomposites.

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 categoriesnone
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 score0.018
Threshold uncertainty score0.595

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.026
GPT teacher head0.296
Teacher spread0.270 · 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 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

Citations3
Published2021
Admission routes2
Has abstractyes

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