MétaCan
Menu
Back to cohort
Record W2990899735 · doi:10.1039/c9na00502a

Structural, optical and terahertz properties of graphene-mesoporous silicon nanocomposites

2019· article· en· W2990899735 on OpenAlexafffund
Défi Junior Jubgang Fandio, Stéphanie Sauze, Abderraouf Boucherif, Richard Arès, Denis Morris

Bibliographic record

VenueNanoscale Advances · 2019
Typearticle
Languageen
FieldMaterials Science
TopicSilicon Nanostructures and Photoluminescence
Canadian institutionsRegroupement Québécois sur les Matériaux de PointeUniversité de Sherbrooke
FundersFonds de recherche du Québec – Nature et technologiesNatural Sciences and Engineering Research Council of Canada
KeywordsGrapheneMaterials scienceTerahertz radiationNanocompositeMesoporous materialSiliconNanotechnologyTerahertz metamaterialsOptoelectronicsFar-infrared laserOpticsChemistryPhysics

Abstract

fetched live from OpenAlex

We investigate the structural, optical and terahertz properties of graphene-mesoporous silicon nanocomposites using Raman, terahertz time-domain and photoluminescence spectroscopy. The nanocomposites consist of a free-standing mesoporous silicon membrane with its external and pore surfaces coated with few-layer graphene. Results show a stabilization of the porous silicon morphology by the graphene coating. The terahertz refractive index and absorption coefficient were found to increase with graphene deposition temperature. Four bands in the 1.79-2.2 eV range emerge from the PL spectra of the nanocomposites. The broad bands centered at 1.79 eV and 1.96 eV were demonstrated to originate from Si nanocrystallites of different sizes. The narrower bands at 2.11 eV and 2.14 eV could be related to a thin SiC film at the Si/C interface.

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 score0.029
Threshold uncertainty score0.588

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.007
GPT teacher head0.220
Teacher spread0.212 · 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

Citations9
Published2019
Admission routes2
Has abstractyes

Explore more

Same venueNanoscale AdvancesSame topicSilicon Nanostructures and PhotoluminescenceFrench-language works237,207