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Record W3092861228 · doi:10.3390/coatings10100982

Tunable Superhydrophobic Aluminum Surfaces with Anti-Biofouling and Antibacterial Properties

2020· article· en· W3092861228 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueCoatings · 2020
Typearticle
Languageen
FieldMaterials Science
TopicSurface Modification and Superhydrophobicity
Canadian institutionsUniversité du Québec à Chicoutimi
FundersFonds de recherche du Québec – Nature et technologies
KeywordsBiofoulingWettingNanotechnologyMaterials scienceEnvironmentally friendlySurface modificationAntibacterial activityChemical engineeringChemistryBacteriaComposite material

Abstract

fetched live from OpenAlex

Surfaces in a hygiene critical environment can become potential reservoirs for transmission of pathogenic infections. Engineering surfaces with the tunable anti-biofouling and antibacterial properties could reduce infections particularly in hospitals and public transport hubs. In the present work, a facile two-step process has been deployed to fabricate a superhydrophobic and antibacterial aluminum surface by chemical etching, followed by passivation with low surface energy octyltriethoxysilane (OTES) molecules. The wettability and antibacterial properties of the OTES passivated aluminum was monotonically tuned by adding quaternary ammonium (QUATs) molecules. An anti-biofouling property of 99.9% against Staphylococcus aureus, 99% against Pseudomonas aeruginosa and 99% against E. coli bacteria, was achieved.

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)0.000
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 score0.006
Threshold uncertainty score0.641

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.031
GPT teacher head0.210
Teacher spread0.179 · 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