MétaCan
Menu
Back to cohort
Record W2020000342 · doi:10.1177/1045389x14557505

Electroactive polymer actuators for active optical components

2014· article· en· W2020000342 on OpenAlexaff
Aaron D. Price, Hani E. Naguib, Foued Ben Amara

Bibliographic record

VenueJournal of Intelligent Material Systems and Structures · 2014
Typearticle
Languageen
FieldEngineering
TopicElectrowetting and Microfluidic Technologies
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsActuatorElectroactive polymersAdaptive opticsWavefrontMaterials scienceCompensation (psychology)Piston (optics)Context (archaeology)Deformable mirrorOpticsOptoelectronicsEngineeringElectrical engineeringPhysics

Abstract

fetched live from OpenAlex

Adaptive optical systems incorporate active components that compensate for wavefront aberrations introduced by optical defects. The quality of optical compensation is largely determined by the stroke of the adaptive component’s underlying actuating mechanism. Development of compact polypyrrole trilayer actuator arrays may deliver superior performance over conventional active technologies such as electrostatic electrodes or piezoelectric actuators. This study introduces a novel piston–tilt mirror apparatus that utilizes low-voltage electroactive polymer actuators to reorient a plane mirror. The design of the mirror and its ancillary systems are first reported, followed by the polymer synthesis procedure and actuator fabrication method. Finally, laser beam steering results are provided in the context of an experimental retinal imaging system. The outcomes indicate a promising future for electroactive polymer-enabled devices in adaptive optical systems with technological implications ranging from more powerful astronomical telescopes to improved retinal tissue diagnosis.

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: none
Teacher disagreement score0.003
Threshold uncertainty score0.009

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 communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.008
GPT teacher head0.214
Teacher spread0.206 · 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

Citations9
Published2014
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Intelligent Material Systems and StructuresSame topicElectrowetting and Microfluidic TechnologiesFrench-language works237,207