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Record W4224234349 · doi:10.1016/j.apsadv.2022.100252

Formulation of nanohybrid coating based on essential oil and fluoroalkyl silane for antibacterial superhydrophobic surfaces

2022· article· en· W4224234349 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 · 2022
Typearticle
Languageen
FieldMaterials Science
TopicSurface Modification and Superhydrophobicity
Canadian institutionsUniversité du Québec à Chicoutimi
Fundersnot available
KeywordsBacillus cereusAntibacterial activityScanning electron microscopeMaterials scienceSilaneFourier transform infrared spectroscopyWettingCoatingNuclear chemistryContact angleChemical engineeringChemistryBacteriaComposite material

Abstract

fetched live from OpenAlex

This paper reveals the role of microstructures formed on the surface of aluminum substrates created by acid etching. A wettability study showed that using a fluorinated silica polymer (PF) and the essential oil of eucalyptus (ECA) with adequate surface texture promoted antibacterial activity. Scanning electron microscope (SEM) images showed that the micro-nanostructure enhanced the possibility of loading the ECA as an antibacterial agent. Fourier Transform Infrared (FTIR) spectroscopy analysis confirmed the chemical linkage between the different components of the surface. Antibacterial tests carried out using inhibition discs displayed the antibacterial behavior of the surface's components against Bacillus cereus and Escherichia coli bacteria. The direct tests on the surface confirmed its capacity to limit bacterial adherence when exposed to bacteria contamination, 99.19% and 97.28% in the case of B. cereus with (PF) and ECA:PF-based coatings. In the case of E. coli these were 94.65% and 98.69% with PF and ECA:PF respectively.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
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 score0.083
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.001
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 head0.254
Teacher spread0.242 · 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