MétaCan
Menu
Back to cohort
Record W289241723 · doi:10.1007/s40846-015-0029-4

Anti-fouling Coatings of Poly(dimethylsiloxane) Devices for Biological and Biomedical Applications

2015· article· en· W289241723 on OpenAlexafffund
Hongbin Zhang, Mu Chiao

Bibliographic record

VenueJournal of Medical and Biological Engineering · 2015
Typearticle
Languageen
FieldMaterials Science
TopicPolymer Surface Interaction Studies
Canadian institutionsUniversity of British Columbia
FundersCanadian Institutes of Health ResearchNatural Sciences and Engineering Research Council of CanadaUniversity of British ColumbiaCanada Research ChairsCalifornia HIV/AIDS Research Program
KeywordsFoulingMaterials scienceNanotechnologyProtein adsorptionBiofoulingAdsorptionCoatingBiocompatible materialFabricationPolymerChemistryMembraneComposite materialBiomedical engineeringOrganic chemistryEngineering

Abstract

fetched live from OpenAlex

Fouling initiated by nonspecific protein adsorption is a great challenge in biomedical applications, including biosensors, bioanalytical devices, and implants. Poly(dimethylsiloxane) (PDMS), a popular material with many attractive properties for device fabrication in the biomedical field, suffers serious fouling problems from protein adsorption due to its hydrophobic nature, which limits the practical use of PDMS-based devices. Effort has been made to develop biocompatible materials for anti-fouling coatings of PDMS. In this review, typical nonfouling materials for PDMS coatings are introduced and the associated basic anti-fouling mechanisms, including the steric repulsion mechanism and the hydration layer mechanism, are described. Understanding the relationships between the characteristics of coating materials and the accompanying anti-fouling mechanisms is critical for preparing PDMS coatings with desirable anti-fouling 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 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)0.000
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 score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
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)0.0010.000

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 head0.059
GPT teacher head0.315
Teacher spread0.255 · 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

Citations410
Published2015
Admission routes2
Has abstractyes

Explore more

Same venueJournal of Medical and Biological EngineeringSame topicPolymer Surface Interaction StudiesFrench-language works237,207