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Record W2800544811 · doi:10.1002/adma.201870128

Wearable Electronics: Self‐Powered Wearable Electronics Based on Moisture Enabled Electricity Generation (Adv. Mater. 18/2018)

2018· article· en· W2800544811 on OpenAlexaff
Daozhi Shen, Ming Xiao, Guisheng Zou, Lei Liu, W. W. Duley, Y. Zhou

Bibliographic record

VenueAdvanced Materials · 2018
Typearticle
Languageen
FieldEngineering
TopicAdvanced Sensor and Energy Harvesting Materials
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsWearable technologyWearable computerElectronicsMaterials scienceFlexibility (engineering)ElectricityLight-emitting diodeElectrical engineeringOptoelectronicsComputer scienceEmbedded systemEngineering

Abstract

fetched live from OpenAlex

In article number 1705925, Lei Liu, Y. Norman Zhou, and co-workers demonstrate a highly efficient moisture-driven electrical generator based on the diffusive flow of water in 3D nanowire networks. This new power device has excellent intrinsic flexibility and is applied in self-powered wearable human-breathing monitors, touch pads, and as a power source for light-emitting diodes (LEDs). This concept constitutes an innovative paradigm of applications for self-powered wearable electronics in the Internet-of-Things.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
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 score0.067
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
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 head0.010
GPT teacher head0.226
Teacher spread0.215 · 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 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

Citations11
Published2018
Admission routes1
Has abstractyes

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