MétaCan
Menu
Back to cohort
Record W2605752496 · doi:10.11159/icnnfc17.121

Growth of ZnO Nanorod Film on Glass Substrate by Nitric AcidAssisted Aqueous Solution Deposition

2017· article· en· W2605752496 on OpenAlexvenueno aff
Ming‐Kwei Lee, Mu-Kai Wang, Haoyu Wang, Chang-Chin Tsai, Cheng-Yu Kung, Chia‐En Yang, Huan-Chi Lung

Bibliographic record

VenueProceedings of the World Congress on Recent Advances in Nanotechnology · 2017
Typearticle
Languageen
FieldMaterials Science
TopicZnO doping and properties
Canadian institutionsnot available
FundersNational Science Council
KeywordsNanorodNitric acidDeposition (geology)Aqueous solutionSubstrate (aquarium)Materials scienceChemical engineeringNanotechnologyChemistryMetallurgyOrganic chemistry

Abstract

fetched live from OpenAlex

In this paper, the growth of ZnO nanorod film transformed from ZnO nanorod array on glass substrate was investigated. ZnO seed layer was first prepared on substrate by RF sputtering. Zinc nitrate and hexamethylenetetramine were used as precursors for the growth of ZnO nanorod array on glass substrate at 50 o C. The aqueous solution was assisted with the incorporation of nitric acid to increase the growth rate and the grain size of ZnO nanorod. With the growth time, ZnO nanorod film is gradually transformed from ZnO nanorod array.

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)0.000
metaresearch head score (Gemma)0.001
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 score0.026
Threshold uncertainty score0.651

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 science0.0010.000
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 head0.012
GPT teacher head0.252
Teacher spread0.240 · 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

Citations0
Published2017
Admission routes1
Has abstractyes

Explore more

Same venueProceedings of the World Congress on Recent Advances in NanotechnologySame topicZnO doping and propertiesFrench-language works237,207