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Record W2296252886 · doi:10.5539/mas.v10n4p12

ZnO QDs Deposited on Si by Sol-Gel Method: Role of Annealing Temperature on Structural and Optical Properties

2016· article· en· W2296252886 on OpenAlexvenueno aff
Abrar Alazawi, Hayder J. Al-Asedy, Noriah Bidin, Khaldoon N. Abbs.

Bibliographic record

VenueModern Applied Science · 2016
Typearticle
Languageen
FieldMaterials Science
TopicZnO doping and properties
Canadian institutionsnot available
Fundersnot available
KeywordsMaterials scienceWurtzite crystal structurePhotoluminescenceOstwald ripeningAnnealing (glass)Quantum dotLuminescenceSpin coatingAnalytical Chemistry (journal)CrystallographyThin filmChemical engineeringZincNanotechnologyOptoelectronicsChemistryComposite material

Abstract

fetched live from OpenAlex

Zinc acetate, propanol, and ethanolamine are used to synthesize ZnO quantum dots (QDs) via the sol–gel method. Spin coating at 220°C is then employed to prepare ZnO thin films consist of QDs. The effects of thermal annealing temperature on the structural and optical properties of the samples are investigated. The prepared QDs are characterized using X-ray diffraction (XRD), field emission scanning electron microscopy (FESEM), and photoluminescence spectroscopy (PL). XRD patterns reveal the crystalline nature of samples existing in the hexagonal wurtzite phase. Increasing annealing temperatures bring about changes to the crystal orientation through the c-axis and increase the size of the QDs from ~10 nm to ~22 nm; this increment can be explained by a coarsening mechanism and the Ostwald ripening process. The observed broadening of X-ray peaks confirms the evolution of crystalline phases in the ZnO QDs. Quantitative analysis of size-dependent strain effects is performed through the Williamson-Hall model. The QD mean size estimated from FESEM and XRD displays high consistency. Room-temperature PL peaks of 3.37ev are attributed to the radiative recombination of electrons and holes from the ZnO QDs/Si interface.

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)0.000
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 score0.027
Threshold uncertainty score0.430

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.016
GPT teacher head0.244
Teacher spread0.228 · 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

Citations6
Published2016
Admission routes1
Has abstractyes

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