MétaCan
Menu
Back to cohort
Record W4414676081 · doi:10.1002/adfm.202518604

Multi‐Functional and Multi‐Stimulus Sensitive Self‐Powered Hydrogel for Skin Mimicry and Energy Harvester

2025· article· en· W4414676081 on OpenAlexaff
Linbin Li, Xuechuan Wang, Wei Wang, Wenlong Zhang, Long Xing, Yitong Wang, Long Xie, Yi Zhou, Pedram Fatehi, Xiangyu You, Hui Jie Zhang

Bibliographic record

VenueAdvanced Functional Materials · 2025
Typearticle
Languageen
FieldEngineering
TopicAdvanced Sensor and Energy Harvesting Materials
Canadian institutionsLakehead University
FundersNational Natural Science Foundation of China
KeywordsVoltageSelf-healing hydrogelsSelf-healingHydrogen bondHydrogenEnergy harvestingSensitivity (control systems)Mechanical energy

Abstract

fetched live from OpenAlex

Abstract Self‐powered hydrogel i‐skin (ionic skins) with excellent mechanical properties and multi‐stimuli responsiveness resembling natural skin is highly desirable but remains a significant challenge. This study introduced a novel multi‐stimuli‐sensitive self‐powered hydrogel, formed by integrating multiple hydrogen bonds and a polyampholyte network, which exhibited excellent mechanical properties, fatigue resistance, high transparency, strong adhesion, and anti‐freezing capability. The cations and anions on the polyampholyte, along with their free counterions, enabled the piezoionic behavior with the output voltage and voltage decay time highly sensitive to the applied pressure and contact shape. Following the release of relatively high pressure, the hydrogel exhibited a prolonged voltage decay time of up to 35 s. The presence of multiple hydrogen bonds and the low water content endowed the hydrogel with sensitivity to both humidity and temperature. These functionalities rendered the hydrogel a promising candidate for skin‐mimetic applications and energy harvesting in next‐generation flexible sensing devices and human–machine interfaces.

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)
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 score0.180
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 communication0.0000.001
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 head0.011
GPT teacher head0.219
Teacher spread0.208 · 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

Citations2
Published2025
Admission routes1
Has abstractyes

Explore more

Same venueAdvanced Functional MaterialsSame topicAdvanced Sensor and Energy Harvesting MaterialsFrench-language works237,207