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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 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

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.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.998
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.136
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