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Record W4391339687 · doi:10.1002/adma.202312135

4D Multimaterial Printing of Soft Actuators with Spatial and Temporal Control

2024· article· en· W4391339687 on OpenAlexfundno aff
Kun Zhou, Rujie Sun, Jonathan P. Wojciechowski, Richard Wang, Jonathan Yeow, Yuyang Zuo, Xin Song, Chunliang Wang, Yue Shao, Molly M. Stevens

Bibliographic record

VenueAdvanced Materials · 2024
Typearticle
Languageen
FieldEngineering
TopicAdvanced Materials and Mechanics
Canadian institutionsnot available
FundersH2020 Marie Skłodowska-Curie ActionsChinese Government ScholarshipMedical Research CouncilUK Regenerative Medicine PlatformChina Scholarship CouncilImperial College LondonRosetrees TrustNatural Sciences and Engineering Research Council of CanadaEuropean CommissionRoyal Academy of Engineering
KeywordsMaterials scienceActuatorSoft robotics3D printingNanotechnologySoft materialsMechanical engineeringEngineering drawingComposite materialComputer scienceEngineeringArtificial intelligence

Abstract

fetched live from OpenAlex

Soft actuators (SAs) are devices which can interact with delicate objects in a manner not achievable with traditional robotics. While it is possible to design a SA whose actuation is triggered via an external stimulus, the use of a single stimulus creates challenges in the spatial and temporal control of the actuation. Herein, a 4D printed multimaterial soft actuator design (MMSA) whose actuation is only initiated by a combination of triggers (i.e., pH and temperature) is presented. Using 3D printing, a multilayered soft actuator with a hydrophilic pH-sensitive layer, and a hydrophobic magnetic and temperature-responsive shape-memory polymer layer, is designed. The hydrogel responds to environmental pH conditions by swelling or shrinking, while the shape-memory polymer can resist the shape deformation of the hydrogel until triggered by temperature or light. The combination of these stimuli-responsive layers allows for a high level of spatiotemporal control of the actuation. The utility of the 4D MMSA is demonstrated via a series of cargo capture and release experiments, validating its ability to demonstrate active spatiotemporal control. The MMSA concept provides a promising research direction to develop multifunctional soft devices with potential applications in biomedical engineering and environmental engineering.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.196
Threshold uncertainty score0.781

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)0.0000.000

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 head0.204
Teacher spread0.200 · 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

Citations40
Published2024
Admission routes1
Has abstractyes

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