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Record W2949561804 · doi:10.1149/2.0461909jes

Manufacturing of Biomimetic Silicone Rubber Films for Experimental Fluid Mechanics: 3D Printed Shark Skin Molds

2019· article· en· W2949561804 on OpenAlexaff
Yuji Yasuda, Kai Zhang, O. Sasaki, Masaru Tomita, David E. Rival, Josephine Galipon

Bibliographic record

VenueJournal of The Electrochemical Society · 2019
Typearticle
Languageen
FieldEngineering
TopicBiomimetic flight and propulsion mechanisms
Canadian institutionsQueen's University
Fundersnot available
KeywordsSilicone rubberTowingAirfoilNatural rubberMaterials scienceSiliconeComposite materialSilicone oilFluid mechanicsMechanical engineeringNanotechnologyPolymer scienceMechanicsMarine engineeringEngineeringPhysics

Abstract

fetched live from OpenAlex

The skin is at the interface of living organisms and their environment, and has evolved interesting structural properties usually over the course of millions of years, providing clues for the design of manmade biomimetic surfaces with favorable fluid mechanics properties. Here, we describe steps to produce silicone rubber films based on microscopy data from shark skin denticles, from data acquisition and manufacturing to attachment to an airfoil for experimental fluid dynamics in large towing tanks. This method is relatively low-cost, may be generalized to other types of patterned micro-structured surfaces, and the manufacturing process may be reproduced by anyone equipped with a 3D printer.

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 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 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 score0.015
Threshold uncertainty score0.523

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
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 head0.006
GPT teacher head0.207
Teacher spread0.202 · 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 teacher head, 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

Citations15
Published2019
Admission routes1
Has abstractyes

Explore more

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