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Record W4401243971 · doi:10.1038/s41528-024-00335-x

Reusable free-standing hydrogel electronic tattoo sensors with superior performance

2024· article· en· W4401243971 on OpenAlexafffund
Shuyun Zhuo, Alexandre Tessier, Mina Arefi, Anan Zhang, Chris Williams, Shideh Kabiri Ameri

Bibliographic record

Venuenpj Flexible Electronics · 2024
Typearticle
Languageen
FieldEngineering
TopicAdvanced Sensor and Energy Harvesting Materials
Canadian institutionsQueen's University
FundersCanada Foundation for Innovation
KeywordsSelf-healing hydrogelsComputer scienceMaterials scienceComputer hardwareBiomedical engineeringPolymer chemistryEngineering

Abstract

fetched live from OpenAlex

Recently electronic tattoo sensors have attracted immense interest for health monitoring mainly due to their higher sensing performance than conventional dry sensors, owing to the ultra-low thickness which results in their conformability to the skin. However, their performance is worse than wet sensors. Further, these electronic tattoo sensors are not durable and reusable when free-standing because of their low thickness and being too delicate. Here, we report a remarkably high-performance freestanding, reusable, ultrathin and ultra-soft electronic tattoo sensor made of parylene-hydrogel double layer system with high water retention over extended periods that can be used for the extended period of 6 months. The hydrogel electronic tattoo (HET) sensors consist of electrically conductive self-adhesive hydrogel with a thickness of 20 µm and Young’s modulus of only 31 kPa at 37 °C, allowing for ultra-conformal contact to the skin microscopic features. Our HET sensors are fabricated using a scalable cost-effective method on ordinary tattoo papers and are laminated on the skin like temporary tattoos and were used for electrophysiological signals recording such as electrocardiography (ECG), electromyography (EMG), and skin hydration, temperature sensing. The HET sensors, for the first time, show 234% lower sensor-skin interface impedance (SSII) and significantly lower susceptibility to motion than gold standard medical grade silver/silver chloride wet gel electrodes which are known to have the lowest SSII and susceptibility to motion. Further, the low HET-skin interface impedance leads to a considerably larger signal amplitude and signal-to-noise ratio (SNR) of the electrophysiological signals recorded using HET sensors in comparison with those obtained using gold standard medical grade silver/silver chloride wet gel electrodes. The SNR of some types of electrophysiological signals such as EMG recorded using HET is up to 19 dB higher than gold standard medical grade electrodes due to higher signal amplitude, significantly lower susceptibility of HET to motion and lower motion artifacts. Also, the HET sensor is the first free-standing ultrathin tattoo sensor that can be transferred from the skin to tattoo paper and vice versa many times and the electrophysiological sensing quality remained high during repeated use for over 6 months.

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 communication0.0000.001
Open science0.0000.000
Research integrity0.0010.000
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 head0.008
GPT teacher head0.207
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

Citations45
Published2024
Admission routes2
Has abstractyes

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