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Record W2022307104 · doi:10.1088/0268-1242/25/4/045023

Effect of annealing on the structural and optical properties of heavily carbon-doped ZnO

2010· article· en· W2022307104 on OpenAlexafffund
He Huang, Z. W. Deng, Dashu Li, E Barbir, Weilin Jiang, Ming Chen, K. L. Kavanagh, P. M. Mooney, S. P. Watkins

Bibliographic record

VenueSemiconductor Science and Technology · 2010
Typearticle
Languageen
FieldMaterials Science
TopicZnO doping and properties
Canadian institutionsSimon Fraser University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsAnnealing (glass)DopingMaterials scienceCarbon fibersChemistryOptoelectronicsMetallurgyComposite material

Abstract

fetched live from OpenAlex

ZnO films grown by metalorganic vapor phase epitaxy (MOVPE) at low temperatures (~500 °C) exhibit very high levels of carbon incorporation in the range of up to several percent. Such large levels of carbon incorporation significantly affect the structural properties of the thin films resulting in broadening of symmetric (0 0 2) rocking curves as well as broadened (1 0 1) pole figures compared with films grown at high temperature. Annealing of the films under air ambient at temperatures between 800 and 1100 °C results in dramatic sharpening of symmetric (0 0 2) rocking curves, indicating improved crystal alignment along the c-axes. (1 0 1) pole figure scans also show significant sharpening in the azimuthal axis, indicating similar improvements in the in-plane crystal alignment perpendicular to the c-axis. Raman spectra for as-grown ZnO at 500 °C show strong D and G peaks at 1381 and 1578 cm−1 due to sp2 carbon clusters. Annealing at 1000 °C results in the elimination of these bands, indicating that post-growth annealing treatment is a useful method to reduce the concentration of sp2 carbon clusters.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0010.000

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 head0.014
GPT teacher head0.243
Teacher spread0.230 · 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 source (direct Gemma or distilled Codex), 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 routes2
Has abstractyes

Explore more

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