MétaCan
Menu
Back to cohort
Record W4291787999 · doi:10.1039/d2sm00826b

Plasticized liquid crystal networks and chemical motors for the active control of power transmission in mechanical devices

2022· article· en· W4291787999 on OpenAlexafffund
Natalie P. Pinchin, Chia-Heng Lin, Cecelia Kinane, Naoki Yamada, Abdon Pena‐Francesch, Hamed Shahsavan

Bibliographic record

VenueSoft Matter · 2022
Typearticle
Languageen
FieldPhysics and Astronomy
TopicMicro and Nano Robotics
Canadian institutionsUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of CanadaAmerican Chemical Society Petroleum Research Fund
KeywordsMechanical transmissionTransmission (telecommunications)Power (physics)Materials sciencePower transmissionControl (management)Liquid crystalElectrical engineeringNanotechnologyOptoelectronicsMechanical engineeringEngineeringComputer scienceMechanical systemPhysics

Abstract

fetched live from OpenAlex

The miniaturization of mechanical devices poses new challenges in powering, actuation, and control since traditional approaches cannot be used due to inherent size limitations. This is particularly challenging in untethered small-scale machines where independent actuation of multicomponent and multifunctional complex systems is required. This work showcases the integration of self-powered chemical motors and liquid crystal networks into a powertrain transmission device to achieve orthogonal untethered actuation for power and control. Driving gears with a protein-based chemical motor were used to power the transmission system with Marangoni propulsive forces, while photothermal liquid crystal networks were used as a photoresponsive clutch to engage/disengage the gear system. Liquid crystal networks were plasticized for optimized photothermal bending actuation to break the surface tension of water and achieve reversible immersion/resurfacing at the air-water interface. This concept is demonstrated in a milliscale transmission gear system and offers potential solutions for aquatic soft robots whose powering and control mechanisms must be necessarily decoupled.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.574
Threshold uncertainty score0.751

Codex and Gemma teacher scores by category

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 head0.004
GPT teacher head0.207
Teacher spread0.203 · 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

Citations12
Published2022
Admission routes2
Has abstractyes

Explore more

Same venueSoft MatterSame topicMicro and Nano RoboticsFrench-language works237,207