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Record W2790854841 · doi:10.1002/marc.201800019

A Novel Anisotropic Hydrogel with Integrated Self‐Deformation and Controllable Shape Memory Effect

2018· article· en· W2790854841 on OpenAlexaff
Xiaoxia Le, Yuchong Zhang, Wei Lü, Li Wang, Jing Zheng, Israt Ali, Jiawei Zhang, Youju Huang, Michael J. Serpe, Xi Yang, Xin‐dong Fan, Tao Chen

Bibliographic record

VenueMacromolecular Rapid Communications · 2018
Typearticle
Languageen
FieldEngineering
TopicAdvanced Materials and Mechanics
Canadian institutionsUniversity of Alberta
FundersNatural Science Foundation of NingboYouth Innovation Promotion Association of the Chinese Academy of SciencesNatural Science Foundation of Zhejiang ProvinceNingbo Municipal Bureau of Science and TechnologyChinese Academy of SciencesNational Natural Science Foundation of China
KeywordsShape-memory alloyShape-memory polymerMaterials scienceDeformation (meteorology)AnisotropySelf-healing hydrogelsComposite materialPolymerComputer scienceOpticsPolymer chemistryPhysics

Abstract

fetched live from OpenAlex

Abstract Although shape memory polymers have been highlighted widely and developed rapidly, it is still a challenging task to realize complex temporary shapes automatically in practical applications. Herein, a novel shape memory hydrogel with the ability of self‐deformation is presented. Through constructing an anisotropic poly(acrylic acid)–polyacrylamide (PAAc–PAAm) structure, the obtained hydrogel exhibits stable self‐deformation behavior in response to pH stimulus, and the shapes that formed automatically can be fixed by the coordination between carboxylic groups and Fe3+; therefore, self‐deformation and shape memory behaviors are integrated in one system. Moreover, the magnitude of auto‐deformation and shape memory could be adjusted with the concentration of corresponding ions, leading to programmable shape memory and shape recovery processes.

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 score0.002

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 head0.006
GPT teacher head0.205
Teacher spread0.199 · 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

Citations58
Published2018
Admission routes1
Has abstractyes

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