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Record W2958765021 · doi:10.1002/adfm.201903471

Harnessing the Power of Stimuli‐Responsive Polymers for Actuation

2019· article· en· W2958765021 on OpenAlexafffund
Liang Hu, Yu Wan, Qiang Zhang, Michael J. Serpe

Bibliographic record

VenueAdvanced Functional Materials · 2019
Typearticle
Languageen
FieldEngineering
TopicAdvanced Materials and Mechanics
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaPriority Academic Program Development of Jiangsu Higher Education InstitutionsSoochow UniversityGrand Challenges CanadaNational Natural Science Foundation of ChinaCanada Foundation for InnovationUniversity of Alberta
KeywordsMaterials scienceNanotechnologySmart materialLift (data mining)Power (physics)ActuatorWork (physics)Biochemical engineeringComputer scienceMechanical engineeringEngineeringArtificial intelligencePhysics

Abstract

fetched live from OpenAlex

Abstract A common behavior found in nature is the ability of plants and animals to naturally respond to their surroundings through actuation. Stimuli‐responsive polymers exhibit the same ability to naturally respond to changes in their environment, although manipulating them in a manner that allows their responses to be harnessed to do work via actuation is far from trivial. In this Review, examples that use temperature, pH, light, and electric field (and other) stimulation for actuation are highlighted. The actuation can result in materials that can be used to grip, lift, and move objects as well as for their own movement. As tremendous progress is being made in this research area, it is hard to imagine a future without these materials impacting lives in some way.

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 score0.004
Threshold uncertainty score0.013

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 science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.012
GPT teacher head0.234
Teacher spread0.223 · 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

Citations139
Published2019
Admission routes2
Has abstractyes

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