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Record W4388972963 · doi:10.1016/j.apsadv.2023.100509

Impact of operating pressure and oxygen gas flow on the characteristics of zinc oxide coatings

2023· article· en· W4388972963 on OpenAlex

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.

Bibliographic record

VenueApplied Surface Science Advances · 2023
Typearticle
Languageen
FieldMaterials Science
TopicZnO doping and properties
Canadian institutionsMcMaster University
Fundersnot available
KeywordsMaterials scienceContact angleWaferZincTransmittanceThin filmSputter depositionOxideSputteringCavity magnetronDeposition (geology)OxygenOpticsComposite materialAnalytical Chemistry (journal)OptoelectronicsMetallurgyNanotechnologyChemistry

Abstract

fetched live from OpenAlex

The primary aim of this study is to examine the effects of various combinations of working pressure and oxygen gas flow on the properties of zinc oxide (ZnO) coatings. Well-crystalline ZnO coatings are formed by employing RF magnetron sputtering on silicon wafers and glass while varying the working pressure and oxygen gas flow. The structure of the thin film was analyzed using X-ray diffraction (XRD). According to the XRD data, the film's structure was made of ZnO, and the peak (002) was visible. Over the wavelength range of 300–800 nm, the optical transmittance of ZnO thin film was examined. The observed transmission value was on average 85 %. With the assistance of contact angle measurement equipment, the surface energy and contact angle of the developed coatings were both determined. Measured contact angles ranged from 96.6° to 115.3°, whereas the surface energy ranged from 14.08 to 25.11 mJ/m2. The enhancement of the anti-icing attributes was achieved by the modification of the operating pressure and gas flow during the deposition process of the coatings.

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)0.001
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 score0.034
Threshold uncertainty score0.357

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.019
GPT teacher head0.275
Teacher spread0.256 · 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