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Record W4405764670 · doi:10.1002/adfm.202470310

Self‐Propelled Morphing Matter for Small‐Scale Swimming Soft Robots (Adv. Funct. Mater. 52/2024)

2024· article· en· W4405764670 on OpenAlexaff
Chuqi Huang, Natalie P. Pinchin, Chia‐Heng Lin, Irving Hafed Tejedor, Matthew Gene Scarfo, Hamed Shahsavan, Abdon Pena‐Francesch

Bibliographic record

VenueAdvanced Functional Materials · 2024
Typearticle
Languageen
FieldPhysics and Astronomy
TopicMicro and Nano Robotics
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsMorphingMaterials scienceSoft roboticsSoft matterScale (ratio)NanotechnologyRobotComposite materialChemical engineeringComputer scienceArtificial intelligenceColloid

Abstract

fetched live from OpenAlex

Self-Propelled Morphing Matter In article number 2413129, Hamed Shahsavan, Abdon Pena-Francesch, and co-workers design bioinspired morphing swimmers integrating chemical protein motors and photoresponsive liquid crystal networks. This approach gives access to five interchangeable modes of locomotion within a single swimming robot via shape-morphing of the soft structure. The proposed design, which mimics the mechanisms of surface gliding and posture change of semiaquatic insects, offers untethered and orthogonal power and control for small-scale swimming soft robots.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
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 score0.483
Threshold uncertainty score1.000

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)0.0080.002

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 head0.011
GPT teacher head0.227
Teacher spread0.216 · 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; both teacher heads agree on what is shown here.

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

Citations1
Published2024
Admission routes1
Has abstractyes

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