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Record W2069931670 · doi:10.1088/0957-4484/20/6/065304

Fabrication of flexible superhydrophobic films by lift-up soft-lithography and decoration with Ag nanoparticles

2009· article· en· W2069931670 on OpenAlexaff
Tongjie Yao, Chuanxi Wang, Quan Lin, Xiao Li, Xiaolu Chen, Jie Wu, Junhu Zhang, Kui Yu, Yang Bai

Bibliographic record

VenueNanotechnology · 2009
Typearticle
Languageen
FieldMaterials Science
TopicSurface Modification and Superhydrophobicity
Canadian institutionsNational Research Council CanadaSteacie Institute for Molecular Sciences
FundersNational Key Research and Development Program of ChinaNational Natural Science Foundation of China
KeywordsMaterials sciencePolydimethylsiloxaneNanotechnologySoft lithographyFabricationWettingElastomerScanning electron microscopeNanoparticleSubstrate (aquarium)LithographyThin filmElectron-beam lithographyContact angleComposite materialResistOptoelectronics

Abstract

fetched live from OpenAlex

Superhydrophobic films with excellent flexibility have been fabricated by combining the lift-up soft-lithography technique and chemical reduction of [Ag(NH(3))(2)](+) ions to Ag nanoparticles (NPs) on the surface of silica spheres which are patterned on the polydimethylsiloxane (PDMS) films. Scanning electron microscopy (SEM) images reveal the presence of raspberry-like hierarchical structures on the PDMS films. The influence of the amount of Ag NPs and the size of the silica spheres on the wettability of the soft films is investigated carefully. Because PDMS films are elastomeric materials, our superhydrophobic films offer great flexibility. The resulting films can be easily transferred from one substrate surface to another without destroying their superhydrophobicity. These flexible and superhydrophobic films can be used repeatedly to satisfy a wide range of applications.

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)0.0000.000
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)0.0010.000

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 head0.010
GPT teacher head0.229
Teacher spread0.219 · 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

Citations59
Published2009
Admission routes1
Has abstractyes

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