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Record W4416585930 · doi:10.1021/acssensors.5c03297

Strain-Insensitive Iontronic Tactile Sensor with Rigid–Soft Hybrid Architecture for Cardiovascular Assessment

2025· article· en· W4416585930 on OpenAlexaff
Weiyan Li, Yiyun Fan, Huijun Kong, Meixi Liu, Shengjie Liu, Xiangqing Li, Li Niu, Zhongqian Song

Bibliographic record

VenueACS Sensors · 2025
Typearticle
Languageen
FieldEngineering
TopicAdvanced Sensor and Energy Harvesting Materials
Canadian institutionsArtificial Intelligence in Medicine (Canada)
FundersGuangdong Science and Technology DepartmentShandong Provincial Postdoctoral Science FoundationNatural Science Foundation of Shandong ProvinceTaishan Scholar Project of Shandong ProvinceChina Postdoctoral Science FoundationNational Natural Science Foundation of China
KeywordsCapacitive sensingWaveformTactile sensorPressure sensorInterface (matter)Finite element methodSIGNAL (programming language)Electronic skin

Abstract

fetched live from OpenAlex

Mechanical deformation of human skin often induces strain-pressure crosstalk and compromises signal reliability. Here, we present a strain-insensitive iontronic tactile sensor by introducing a rigid-soft hybrid architecture to decouple pressure signals from tensile strain. Rigid microspheres are embedded into a stretchable iontronic film to construct an iontronic interface with a soft conductive electrode. The iontronic interface exhibits a strain-independent contact area under uniaxial strain up to 50%, as confirmed by finite element simulations and experimental results. This design enables consistent capacitive responses under stretching and bending states, ensuring high-fidelity pressure sensing under dynamic skin motion. Integrated with a robotic arm, the tactile sensor captures high-fidelity pulse waveforms from multiple arterial sites and distinguishes between healthy, hypertensive, and coronary heart disease subjects through second-derivative waveform analysis. This strain-decoupling strategy establishes a universal approach for strain-insensitive tactile sensing, enabling practical applications in biomedical monitoring and future adaptive electronic systems.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.895
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.006
GPT teacher head0.223
Teacher spread0.216 · 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 designSimulation or modeling
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

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