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Record W2901871045 · doi:10.1016/j.matdes.2018.11.041

A lotus effect-inspired flexible and breathable membrane with hierarchical electrospinning micro/nanofibers and ZnO nanowires

2018· article· en· W2901871045 on OpenAlexaff
Rouxi Chen, Yuqin Wan, Weiwei Wu, Ji‐Huan He, Jianhua Cheng, Reinhard Jetter, Fank K. Ko, Yuancai Chen

Bibliographic record

VenueMaterials & Design · 2018
Typearticle
Languageen
FieldMaterials Science
TopicSurface Modification and Superhydrophobicity
Canadian institutionsUniversity of British Columbia
FundersChina Scholarship CouncilChina Postdoctoral Science Foundation
KeywordsMaterials scienceElectrospinningMicrofiberLotus effectContact angleNanowireNanofiberOleic acidMembraneCoatingNanotechnologyChemical engineeringHydrothermal circulationComposite materialPolymerOrganic chemistry

Abstract

fetched live from OpenAlex

Hierarchical structures such as the leaf of lotus are promising models for self-cleaning surfaces. A biomimetic structure that contains PVDF microfibers (ZnO nanowires covered with oleic acid) was prepared here to illustrate the biomimetic lotus effect concept. This was prepared via electrospinning, hydrothermal synthesis, and dip coating. The hierarchical structure and oleic acid coating was shown to contribute to super hydrophobicity with a water contact angle (WCA) > 150°. The super hydrophobic flexible membrane not only exhibited water droplet bouncing and rolling behaviors, but also demonstrated promising self-cleaning properties, water resistance, and permeability of air and water vapor. These characteristics have far reaching implications in broadening the application of the self-cleaning textiles, waterproof breathable membrane, medical devices, and surgical plants such as artificial blood vessels.

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 score0.000
Threshold uncertainty score0.001

Distilled classifier scores by category (both heads)

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 head0.012
GPT teacher head0.228
Teacher spread0.216 · 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

Citations75
Published2018
Admission routes1
Has abstractyes

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