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Record W2155170349 · doi:10.5539/apr.v7n6p1

Progressively Doping Graphene with S i: from Graphene to Silicene, a Numerical Study

2015· article· en· W2155170349 on OpenAlex
Nathalie Olivi‐Tran

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueApplied Physics Research · 2015
Typearticle
Languageen
FieldMaterials Science
TopicGraphene research and applications
Canadian institutionsnot available
Fundersnot available
KeywordsSiliceneGrapheneMaterials scienceDopingElectronNanotechnologyCondensed matter physicsOptoelectronicsPhysics

Abstract

fetched live from OpenAlex

For three different sizes of graphene nanosheets, we computed the Density of states when these nanosheets are progressively doped with an increasing percentage of S i atoms. The pure graphene nanosheets are semi conducting or not depending on their size. The pure silicene nanosheets are conducting with a conduction due to π (pi) electrons. <br />The S i doped graphene nanosheets are also semi conducting or not depending on their size: for small sizes, there are semi conducting and they become conducting for larger sizes and larger percentages of S idoping. We computed also the total electronic energy which is linked to the mechanical stability of all our nanosheets. This mechanical stability decreases regularly as a function of the S i percentage of doping , but for the pure silicene nanosheets, the mechanical stability decreases more abruptly.

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.

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 categoriesInsufficient payload (model declined to judge)
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 score0.117
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.003
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.001

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 head0.121
GPT teacher head0.392
Teacher spread0.271 · 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