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Record W4234207932 · doi:10.24908/iqurcp.8919

Treatment of Silicon Dioxide Films Resulting in Luminescence from Silicon Nanoclusters

2018· article· en· W4234207932 on OpenAlexvenueno aff
David Wisniewski

Bibliographic record

VenueInquiry Queen s Undergraduate Research Conference Proceedings · 2018
Typearticle
Languageen
FieldMaterials Science
TopicSilicon Nanostructures and Photoluminescence
Canadian institutionsnot available
Fundersnot available
KeywordsNanoclustersSiliconMaterials scienceLuminescenceOptoelectronicsNanotechnologyRADIUSSilicon dioxideComputer scienceComposite material

Abstract

fetched live from OpenAlex

A large conglomerate of silicon atoms is known to group together forming a silicon nanocluster. Through quantum confinement effects, these silicon nanoclusters are known to luminesce when they are grown to a critical radius. Luminescence may have applications ranging from data transmission over wireless networks, to building low cost biomedical lasers, and to effectively eliminating the bandwidth of a signal inside of a silicon chip, hence it is a worthwhile research interest. This critical radius was modeled as a function of the concentration of excess silicon originally embedded in a silicon dioxide film. Using a simplified diffusional approach, the theoretical size, growth and pacification of nanoclusters were compared to experimentally confirmed results. Processing techniques that may allow for the growth of such a film were examined, and an American company called Veeco was found that could accommodate such a process.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.013
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.002
Scholarly communication0.0000.001
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 head0.073
GPT teacher head0.351
Teacher spread0.277 · 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

Citations0
Published2018
Admission routes1
Has abstractyes

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