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Record W2777731885 · doi:10.3390/polym9120725

Bio-Inspired Polymeric Structures with Special Wettability and Their Applications: An Overview

2017· review· en· W2777731885 on OpenAlexaff
Zihe Pan, Fangqin Cheng, Boxin Zhao

Bibliographic record

VenuePolymers · 2017
Typereview
Languageen
FieldMaterials Science
TopicSurface Modification and Superhydrophobicity
Canadian institutionsUniversity of Waterloo
FundersShanxi University
KeywordsSuperhydrophilicityCreaturesWettingNanotechnologyMaterials scienceLotus effectBiomimeticsPolymer scienceNatural (archaeology)ChemistryComposite materialBiology

Abstract

fetched live from OpenAlex

It is not unusual for humans to be inspired by natural phenomena to develop new advanced materials; such materials are called bio-inspired materials. Interest in bio-inspired polymeric superhydrophilic, superhydrophobic, and superoleophobic materials has substantially increased over the last few decades, as has improvement in the related technologies. This review reports the latest developments in bio-inspired polymeric structures with desired wettability that have occurred by mimicking the structures of lotus leaf, rose petals, and the wings and shells of various creatures. The intrinsic role of surface chemistry and structure on delivering superhydrophilicity, superhydrophobicity, and superoleophobicity has been extensively explored. Typical polymers, commonly used structures, and techniques involved in developing bio-inspired surfaces with desired wettability are discussed. Additionally, the latest applications of bio-inspired structures with desired wettability in human activities are also introduced.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.002

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 head0.136
GPT teacher head0.365
Teacher spread0.229 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations64
Published2017
Admission routes1
Has abstractyes

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