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Record W2584350454 · doi:10.1038/ncomms14230

Hydraulic hydrogel actuators and robots optically and sonically camouflaged in water

2017· article· en· W2584350454 on OpenAlexfundno aff
Hyunwoo Yuk, Shaoting Lin, Chu Ma, Mahdi Takaffoli, Nicholas X. Fang, Xuanhe Zhao

Bibliographic record

VenueNature Communications · 2017
Typearticle
Languageen
FieldPhysics and Astronomy
TopicMicro and Nano Robotics
Canadian institutionsnot available
FundersOffice of Naval ResearchNatural Sciences and Engineering Research Council of CanadaMultidisciplinary University Research InitiativeCharles Stark Draper LaboratorySamsungDivision of Civil, Mechanical and Manufacturing InnovationNational Science Foundation
KeywordsSelf-healing hydrogelsActuatorCamouflageRobotComputer scienceMaterials scienceRobustness (evolution)BiomimeticsNanotechnologyArtificial intelligenceChemistry

Abstract

fetched live from OpenAlex

Sea animals such as leptocephali develop tissues and organs composed of active transparent hydrogels to achieve agile motions and natural camouflage in water. Hydrogel-based actuators that can imitate the capabilities of leptocephali will enable new applications in diverse fields. However, existing hydrogel actuators, mostly osmotic-driven, are intrinsically low-speed and/or low-force; and their camouflage capabilities have not been explored. Here we show that hydraulic actuations of hydrogels with designed structures and properties can give soft actuators and robots that are high-speed, high-force, and optically and sonically camouflaged in water. The hydrogel actuators and robots can maintain their robustness and functionality over multiple cycles of actuations, owing to the anti-fatigue property of the hydrogel under moderate stresses. We further demonstrate that the agile and transparent hydrogel actuators and robots perform extraordinary functions including swimming, kicking rubber-balls and even catching a live fish in water.

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 score0.004

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 head0.012
GPT teacher head0.284
Teacher spread0.271 · 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

Citations975
Published2017
Admission routes1
Has abstractyes

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