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Record W4289917765 · doi:10.1002/adfm.202206939

Semi‐Crystalline Rubber as a Light‐Responsive, Programmable, Resilient Robotic Material

2022· article· en· W4289917765 on OpenAlexaff
Qi Yang, Hamed Shahsavan, Zixuan Deng, Hongshuang Guo, Hang Zhang, Heng Liu, Chunyu Zhang, Arri Priimägi, Xuequan Zhang, Hao Zeng

Bibliographic record

VenueAdvanced Functional Materials · 2022
Typearticle
Languageen
FieldEngineering
TopicAdvanced Materials and Mechanics
Canadian institutionsUniversity of Waterloo
FundersAcademy of FinlandNational Natural Science Foundation of China
KeywordsMaterials scienceNatural rubberElastomerComposite materialDeformation (meteorology)Soft roboticsCrystallinityPolymerActuatorPolybutadieneViscoelasticityPolyethyleneWaterproofingCopolymerComputer scienceArtificial intelligence

Abstract

fetched live from OpenAlex

Abstract Polymers with large and reversible light‐induced deformation offer a plethora of opportunities for the wireless control of small‐scale soft robots. However, their widespread adoption in real‐world applications is hindered, mainly due to their intrinsic softening upon illumination. Such limitation has detrimental effects on the achievable stress, durability, and precise positional control of the soft actuators after multiple cycles of use. Here, a synthetic rubber from a polybutadiene‐polyethylene copolymer is reported as a durable material for light‐controlled soft robots. The rubber can be programmed to exhibit various deformation modes controlled by visible‐to‐infrared light through a photothermal effect. Semi‐crystallinity of polyethylene within the rubbery network provides this material with a remarkable modulus at high temperatures (2.5 MPa at 100–140 °C), deformation repeatability (>90%) and shape‐recovery (>98%) after 100 actuation cycles subject to loads ranging from 10 to 10 000 times of its body weight (1.4 kPa–1.4 MPa). Soft robotic applications are demonstrated, such as thermally‐driven jumping and photo‐driven cargo transport carrying up to 1200 times its own weight. The results expand the portfolio of materials in designing remotely‐controlled, robust, and resilient soft robots working at small scales.

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 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 score0.256
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)0.0090.000

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 head0.007
GPT teacher head0.202
Teacher spread0.196 · 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.

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

Citations20
Published2022
Admission routes1
Has abstractyes

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