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Record W2964259901 · doi:10.1080/2374068x.2017.1413527

Generalisation of phonon confinement model for interpretation of Raman line-shape from nano-silicon

2017· article· en· W2964259901 on OpenAlexaff
Manushree Tanwar, Priyanka Yogi, Simran Lambora, Suryakant Mishra, Shailendra K. Saxena, Pankaj R. Sagdeo, А. С. Крылов, Rajesh Kumar

Bibliographic record

VenueAdvances in Materials and Processing Technologies · 2017
Typearticle
Languageen
FieldMaterials Science
TopicSilicon Nanostructures and Photoluminescence
Canadian institutionsNational Institute for NanotechnologyUniversity of Alberta
FundersScience and Engineering Research BoardMinistry of Education, IndiaUGC-DAE Consortium for Scientific Research, University Grants CommissionDepartment of Science and Technology, Ministry of Science and Technology, India
KeywordsRaman spectroscopyPhononRaman scatteringMaterials scienceSiliconCoherent anti-Stokes Raman spectroscopyLine (geometry)SemiconductorOpticsCondensed matter physicsOptoelectronicsPhysicsGeometry

Abstract

fetched live from OpenAlex

A comparative analysis of two Raman line-shape functions has been carried out to validate the true representation of experimentally observed Raman scattering data for semiconducting nanomaterials. A modified form of already existing phonon confinement model (PCM) incorporates two basic considerations, phonon momentum conservation and shift in zone centre phonon frequency. After incorporation of the above mentioned two factors, a rather symmetric Raman line-shape is generated which is in contrary to the usual asymmetric Raman line-shapes obtained from nanostructured semiconductor. By fitting an experimentally observed Raman scattering data from silicon nanostructures, prepared by metal induced etching, it can be established that the Raman line-shape obtained within the framework of PCM is a true representative Raman line-shape of sufficiently low dimensions semiconductors.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.021
GPT teacher head0.302
Teacher spread0.280 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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

Citations10
Published2017
Admission routes1
Has abstractyes

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