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Record W2622497163 · doi:10.1088/2053-1591/aa701a

ZnO nanowire growth by chemical vapor deposition with spatially controlled density on Zn<sub>2</sub>GeO<sub>4</sub>:Mn polycrystalline substrates

2017· article· en· W2622497163 on OpenAlex
Siwei Ma, Adrian H. Kitai

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueMaterials Research Express · 2017
Typearticle
Languageen
FieldMaterials Science
TopicZnO doping and properties
Canadian institutionsMcMaster University
FundersNatural Sciences and Engineering Research Council of CanadaMcMaster University
KeywordsNanowireMaterials scienceCrystalliteSubstrate (aquarium)Chemical vapor depositionElectroluminescenceTube furnaceChemical engineeringVapor–liquid–solid methodNanotechnologyCatalysisCurrent densityOptoelectronicsLayer (electronics)MetallurgyChemistry

Abstract

fetched live from OpenAlex

Aligned ZnO nanowires were successfully synthesized by CVD growth on polycrystalline Zn2GeO4:Mn substrates. The density of the nanowires was explored as a function of gold catalyst thickness as well as the geometry of the growth system. For the first time we demonstrated that the density of ZnO nanowires can be directly tailored by adjusting the lateral distance between a Zn2GeO4:Mn substrate and source powders, and symmetric growth was observed when source powders were placed both upstream and downstream in two separated zones during the synthesis process in a tube furnace. Electric field modelling was employed to predict a desired nanowire density range for future application of the Zn2GeO4:Mn/ZnO structures to electroluminescent devices.

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.

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)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Scholarly communication
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 score0.006
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0020.001
Scholarly communication0.0030.001
Open science0.0010.001
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 head0.030
GPT teacher head0.280
Teacher spread0.250 · 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