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Record W2757469015 · doi:10.25177/jnms.1.1.1

Controlled Crystal-Growth and Structures of Silicon Nanowires for smart applications

2017· article· en· W2757469015 on OpenAlexaff
Brahim Aïssa, Maha M. Khayyat, Esam H. Abdul-Hafidh, Mourad Nedil

Bibliographic record

VenueSDRP Journal of Nanotechnology & Material Science · 2017
Typearticle
Languageen
FieldEngineering
TopicNanowire Synthesis and Applications
Canadian institutionsUniversité TÉLUQUniversité du Québec en Abitibi-Témiscamingue
Fundersnot available
KeywordsSilicon nanowiresNanowireMaterials scienceSiliconNanotechnologyCrystal growthEngineering physicsOptoelectronicsEngineeringCrystallographyChemistry

Abstract

fetched live from OpenAlex

One-dimensional nanostructures such as semiconductor nanowires (NWs) are attractive building blocks due to their promising physical properties and potential as active materials in future electronic and optoelectronic applications. The Crystal growth of nanowires occurs mainly at the interfaces between the growing crystals and the supply media. This article reports on the silicon nanowires grown using a vapor-liquid-solid (VLS) concept. One of the key advantages and the beauty of VLS is that controlled placement or templating of the seed metal produces consequently templated NW growth. This templating is highly required for direct integration of NWs into nanodevices for various smart applications, including sensors, actuators, thermoelectricity generation and photovoltaics. We discuss the major questions related to the discovery of fundamentally new phenomena versus performance benchmarking for many of the Si-NWs applications. Finally we attempt to look into the future and discuss our opinion regarding the upcoming trends in NW research.

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 score0.007
Threshold uncertainty score0.333

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 studies0.0000.001
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 head0.008
GPT teacher head0.243
Teacher spread0.235 · 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

Citations1
Published2017
Admission routes1
Has abstractyes

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