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Record W2083135384 · doi:10.5539/mas.v4n12p56

Study the Effect of CO2 Laser Annealing on Silicon Nanostructures

2010· article· en· W2083135384 on OpenAlexvenueno aff
Ali Abed, Bassam G. Rasheed

Bibliographic record

VenueModern Applied Science · 2010
Typearticle
Languageen
FieldMaterials Science
TopicSilicon Nanostructures and Photoluminescence
Canadian institutionsnot available
Fundersnot available
KeywordsMaterials scienceSiliconLaserAmorphous siliconOptoelectronicsSilicon oxideAnnealing (glass)Hybrid silicon laserAmorphous solidThin filmLaser power scalingSubstrate (aquarium)NanostructureLaser ablationOpticsNanotechnologyCrystalline siliconComposite materialChemistry

Abstract

fetched live from OpenAlex

The recent discovery of strong room-temperature photoluminescence from silicon nanocrystals fabricated by different methods is an extremely important scientific breakthrough with enormous technological implications because of possibility of integration of silicon based electronic and optoelectronic devices. This paper the effect of Laser annealing technique used to produce nanoparticales by CW laser was proposed. The laser spot was focused within 0.6 mm beam radius to synthesize silicon rich oxide SiOx nanostructures in silicon nanostructures thin films by laser ablation of silicon target on glass. The CO2­ laser beam λ (10.6µm) with power Plaser ranging from 1 to 10 Watt. Two lasers were employed; the first, Q-switched Nd:YAG laser to prepare an amorphous silicon film on glass substrate and the second, CW CO2 laser beam to produce local heating and synthesize a silicon oxide nanostructures. A sufficient absorbing CO2 laser beam was used to induce local heating at optimizing surface temperature. The optical and surface morphological properties of SiOx films annealed by CO2 laser are primarily investigated the effect of parameters such as power density and elimination time.

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)0.002
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 score0.013
Threshold uncertainty score0.536

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
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 head0.256
Teacher spread0.248 · 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

Citations5
Published2010
Admission routes1
Has abstractyes

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