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Record W4389304617 · doi:10.15251/djnb.2023.184.1433

Preparation and properties of PTFE@TiO2/epoxy superhydrophobic coating

2023· article· en· W4389304617 on OpenAlexaff
G. Q Xu, Chenshi Li, F. J. Wang, Junfei Ou, Zhongxin Xue, Alidad Amirfazli

Bibliographic record

VenueDigest Journal of Nanomaterials and Biostructures · 2023
Typearticle
Languageen
FieldMaterials Science
TopicSurface Modification and Superhydrophobicity
Canadian institutionsYork University
FundersChangzhou Science and Technology BureauNational Natural Science Foundation of China
KeywordsContact angleMaterials scienceWettingCoatingScanning electron microscopeEpoxyPolytetrafluoroethyleneComposite materialAdhesionNanoparticleNanotechnology

Abstract

fetched live from OpenAlex

The problem of bacterial adhesion has been a challenge in everyday life and industry for decades. In this paper, polytetrafluoroethylene(PTFE) micropowder, titanium dioxide(TiO2) nanopowder, ethyl acetate and epoxy resin were sequentially added to a beaker and stirred well, then the nanoparticles were modified using perfluorooctyltriethoxysilane (POTS), and finally superhydrophobic coatings were fabricated on the surface of an aluminium sheet by spraying process. Characterisation was carried out using scanning electron microscopy and contact angle measurement, and the coating wettability, chemical stability and mechanical stability properties were investigated, and finally the coating was tested for antimicrobial properties. The study suggests that the hydrophobicity of the sample was optimal at a contact angle of 163.3° and a rolling angle of 3.2° when the ratio of PTFE micropowder to nano-TiO2 by mass was 1:4 and the ration between POTS and nanoparticles by mass was 12%. The contact angles were 137.8° and 143.6° after 25 and 32 hours of soaked in an anhydrous solution with a pH of 14 and 1, respectively. Most importantly, it exhibits good antimicrobial properties.

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 score0.020
Threshold uncertainty score0.375

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 head0.023
GPT teacher head0.260
Teacher spread0.237 · 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

Citations3
Published2023
Admission routes1
Has abstractyes

Explore more

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