MétaCan
Menu
Back to cohort

Bacterial Anti-Adhesion of Coated and Uncoated Thin-Film-Composite (TFC) Polyamide (PA) Membranes

2014· article· en· W1620926642 on OpenAlexvenueno aff
Juha Nikkola, Hanna‐Leena Alakomi, Chuyang Y. Tang

Bibliographic record

VenueJournal of Coating Science and Technology · 2014
Typearticle
Languageen
FieldEnvironmental Science
TopicMembrane Separation Technologies
Canadian institutionsnot available
FundersTeknologian Tutkimuskeskus VTTTekes
KeywordsMembranePolyamidePolyvinyl alcoholThin-film composite membraneAdhesionMaterials scienceChemical engineeringSurface roughnessContact angleReverse osmosisSurface energyCoatingPolymer chemistryComposite materialChemistry

Abstract

fetched live from OpenAlex

This study investigates the bacterial anti-adhesion performance of uncoated and coated reverse osmosis (RO) membranes. All the membranes were commercially available fully-aromatic thin-film-composite (TFC) polyamide (PA) membranes. Two of the TFC PA membranes (SW30 and BW30) were coated using polyvinyl alcohol (PVA) coating, while the other three membranes (LE, XLE and NF90) were uncoated. Among the characterised TFC PA membranes, the PVA coated were more hydrophilic and their surface energy was higher in comparison to uncoated. In addition, the PVA coated membranes had lower surface roughness. AFM interaction force measurement demonstrated higher repellence performance for the more polar surface. Bacteria attachment test showed differences between the coated and the uncoated membranes. Indeed, the increase in hydrophilicity and surface polarity showed decrease in the attachment of Pseudomonas aeruginosa cells. Moreover, the results demonstrated that the surface polarity showed better correlation with the attachment of the bacteria. In addition, the type of the surface roughness may somehow contribute to the bacteria repellence.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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 integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.008
GPT teacher head0.234
Teacher spread0.226 · 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 source (direct Gemma or distilled Codex), 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

Citations1
Published2014
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Coating Science and TechnologySame topicMembrane Separation TechnologiesFrench-language works237,207