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Record W4414984062 · doi:10.1002/smll.202510062

TPU/MOFs Electrospun Composite Film for Underwater Tactile Sensing and Finger Joint Bending Monitoring

2025· article· en· W4414984062 on OpenAlexaff
Quanyu Wang, Zichao Wang, Wenshuai Tian, Zhiqian Li, Pu Liu, Zonglin Pan, Yongxin Song, Dongqing Li

Bibliographic record

VenueSmall · 2025
Typearticle
Languageen
FieldEngineering
TopicAdvanced Sensor and Energy Harvesting Materials
Canadian institutionsUniversity of Waterloo
FundersNational Natural Science Foundation of China
KeywordsUnderwaterSIGNAL (programming language)ElastomerBendingTactile sensorPressure sensorHydrostatic pressurePressingThermoplastic polyurethane

Abstract

fetched live from OpenAlex

Abstract Most flexible tactile pressure sensors can hardly be used in deep sea due to the strict water sealing requirement on the soft material and the hardening effect due to high hydroSstatic pressure. To address these issues, a self‐powered and hydrostatic pressure‐balanced underwater flexible tactile sensor based on an electrospun nanofiber film composed of a thermoplastic polyurethane elastomer (TPU) and Metal Organic Frameworks (MOFs‐801) is presentedr. The sensor generates ionic current via different ion movement speeds under pressure, and can simultaneously achieve pressure and position sensing. Experimental results show the signal magnitude increases with the increase in the applied pressure, carboxyl group concentration, and stretching length. The direction and magnitude of the signal depend on the pressing or stretching position of the film, with bigger current magnitude closer to the film ends. The maximum sensitivity is 1.31 kPa −1 , with the response and recovery times of 0.16 and 0.51 s, respectively. The sensor remains operational after over 1400 cycles under 600 kPa external load. Furthermore, the signal magnitude decreased only by 10.38% under 100 m water depth. Proof of concept demonstration of object shape differentiation by monitoring the bending of finger joints is successfully achieved.

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 score0.121
Threshold uncertainty score0.642

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 head0.020
GPT teacher head0.232
Teacher spread0.212 · 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

Citations3
Published2025
Admission routes1
Has abstractyes

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