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Record W2980400682 · doi:10.1139/cjp-2018-0945

Ellipsometric evaluation and morphology of mixed zinc sulfide/zinc oxide and zinc oxide nanostructures synthesized at various temperatures

2019· article· en· W2980400682 on OpenAlexvenueno aff
Mohammed S. Alqahtani, S. H. Mohamed, Z.A. Alrowaili, N. M. A. Hadia

Bibliographic record

VenueCanadian Journal of Physics · 2019
Typearticle
Languageen
FieldMaterials Science
TopicZnO doping and properties
Canadian institutionsnot available
FundersKing Saud University
KeywordsZincZinc sulfideEllipsometryNanostructureMaterials scienceBand gapOxideSulfideMorphology (biology)Analytical Chemistry (journal)NanotechnologyChemical engineeringThin filmMetallurgyOptoelectronicsChemistry

Abstract

fetched live from OpenAlex

The aim of this work was to carry out systematic studies of how synthesis temperatures affect the morphology and properties of mixed zinc sulfide/zinc oxide (ZnS x O y and ZnO) nanostructures, and to get reliable data on optical constants of ZnS x O y and ZnO nanowires/nanobelts (NW/NB) for the use in device applications. ZnS x O y and ZnO NWs/NBs were fabricated using vapor transport in an open-end tube. Mixed ZnS 0.47 O 0.62 NWs was obtained at the synthesis temperature of 850 °C. The sulfur content disappeared as the temperature increased to 950 °C and 1050 °C and the morphology changed to a mixture of NW/NB. The NW prepared at 850 °C were indexed as mixed phases of hexagonal ZnS and hexagonal ZnO structures. The NW/NB prepared at 950 °C and 1050 °C were indexed as pure hexagonal ZnO structures. The thickness, surface roughness, and optical constants of the synthesized nanostructured samples were extracted from measurements of spectroscopic ellipsometry. A two-layers model was proposed to fit the calculated data to the measured ellipsometric spectra. The estimated band gap values of the prepared nanostructures lay 0.66–0.79 eV below the bulk ZnO value due to the lower oxygen content present in the samples and the stresses built in the samples during preparation.

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 score0.074
Threshold uncertainty score0.545

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 studies0.0000.000
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 head0.228
Teacher spread0.212 · 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

Citations4
Published2019
Admission routes1
Has abstractyes

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