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Record W2921476047 · doi:10.1002/mds3.10025

Cohesive dry ECG sensor using silver nanowires and PDMS tuned for adhesion

2019· article· en· W2921476047 on OpenAlex
Rick Helgason, Amey Banavali, Yongjun Lai

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueMedical Devices & Sensors · 2019
Typearticle
Languageen
FieldEngineering
TopicAdvanced Sensor and Energy Harvesting Materials
Canadian institutionsQueen's University
Fundersnot available
KeywordsPolydimethylsiloxaneMaterials scienceAdhesiveAdhesionLayer (electronics)NanotechnologyComposite materialOptoelectronics

Abstract

fetched live from OpenAlex

Abstract A technique is exploited to develop a dry ECG sensor using polydimethylsiloxane (PDMS) with enhanced adhesion to skin. The sensor consists of silver nanowires (AgNWs) embedded in PDMS. The adhesive solution presented is a novel technique which combines using high‐ratio 25:1 PDMS selectively deposited in a thin layer on the surface of a sensor body that is made from 15:1 PDMS. With this adhesive mechanism, we demonstrate a cohesive ECG sensor which can adhere to a subject without the use of additional sticky adhesives and that is compatible with current ECG technology. PDMS is residue free and highly biocompatible meaning this is a solution suitable for long‐term wear. We demonstrate that our sensor can be filtered to similar quality of a traditional ECG sensor, and further show that our sensors stay in place for more than 24 hr and it can adhere to skin with an average maximum force of about 1.09 N.

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.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.626
Threshold uncertainty score0.922

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 head0.013
GPT teacher head0.245
Teacher spread0.231 · 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