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Record W2810629993 · doi:10.1080/09506608.2018.1484577

Surface modification to control the water wettability of electrospun mats

2018· article· en· W2810629993 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

VenueInternational Materials Reviews · 2018
Typearticle
Languageen
FieldMaterials Science
TopicElectrospun Nanofibers in Biomedical Applications
Canadian institutionsÉcole de Technologie Supérieure
Fundersnot available
KeywordsWettingElectrospinningMaterials scienceSurface modificationPolymerPorosityNanotechnologyMembraneChemical engineeringComposite materialChemistryEngineering

Abstract

fetched live from OpenAlex

Electrospun mats have many possible applications in which it is important to control their interaction with water: when used as separation membranes, superhydrophobic mats can remove oil from water, whereas when used as scaffolds for tissue engineering, hydrophilic mats present better cell affinity. Frequently, however, the surface properties of the polymer fibers that compose the mat need to be modified and tuned. This review covers the main surface modification techniques used to change the water wettability of mats produced by electrospinning. Some basic aspects of the electrospinning process and wetting theories are presented as a starting point for the discussion, highlighting the common wetting switching mechanism found in highly porous structures like electrospun mats. The surface modification techniques are then classified as post-treatments or one-step modification during electrospinning. The fundamental aspects of each technique are followed by a discussion emphasizing their technical advantages and drawbacks.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
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 score0.156
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.003

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 head0.324
Teacher spread0.300 · 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