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Record W2626167692 · doi:10.1126/sciadv.1602051

Flexible and stretchable power sources for wearable electronics

2017· article· en· W2626167692 on OpenAlexfundno aff
Alla M. Zamarayeva, Aminy E. Ostfeld, Michael J. Wang, Jerica K. Duey, Igal Deckman, Balthazar Lechêne, Greg Davies, Daniel A. Steingart, Ana Claudia Arias

Bibliographic record

VenueScience Advances · 2017
Typearticle
Languageen
FieldEngineering
TopicAdvanced Sensor and Energy Harvesting Materials
Canadian institutionsnot available
FundersMcMaster UniversityNational Science Foundation
KeywordsWearable computerFlexibility (engineering)Wearable technologyBattery (electricity)ElectronicsStretchable electronicsComputer sciencePower (physics)Embedded systemElectrical engineeringEngineeringPhysics

Abstract

fetched live from OpenAlex

Flexible and stretchable power sources represent a key technology for the realization of wearable electronics. Developing flexible and stretchable batteries with mechanical endurance that is on par with commercial standards and offer compliance while retaining safety remains a significant challenge. We present a unique approach that demonstrates mechanically robust, intrinsically safe silver-zinc batteries. This approach uses current collectors with enhanced mechanical design, such as helical springs and serpentines, as a structural support and backbone for all battery components. We show wire-shaped batteries based on helical band springs that are resilient to fatigue and retain electrochemical performance over 17,000 flexure cycles at a 0.5-cm bending radius. Serpentine-shaped batteries can be stretched with tunable degree and directionality while maintaining their specific capacity. Finally, the batteries are integrated, as a wearable device, with a photovoltaic module that enables recharging of the batteries.

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 score0.003
Threshold uncertainty score0.010

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 integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.267
Teacher spread0.255 · 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

Citations402
Published2017
Admission routes1
Has abstractyes

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