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Record W2567500813 · doi:10.1002/admt.201600191

Advanced Biowaste‐Based Flexible Photocatalytic Fuel Cell as a Green Wearable Power Generator

2016· article· en· W2567500813 on OpenAlexafffund
Gregory Lui, Gaopeng Jiang, Jared Lenos, Edric Lin, Michael Fowler, Aiping Yu, Zhongwei Chen

Bibliographic record

VenueAdvanced Materials Technologies · 2016
Typearticle
Languageen
FieldEngineering
TopicAdvanced Sensor and Energy Harvesting Materials
Canadian institutionsUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsWearable computerMicrobial fuel cellGenerator (circuit theory)PhotocatalysisMaterials sciencePower densityElectricity generationComputer sciencePower (physics)Process engineeringEnvironmental scienceChemistryCatalysisEmbedded systemEngineeringOrganic chemistryPhysics

Abstract

fetched live from OpenAlex

This study designs a wearable power generator from a flexible, photocatalytic fuel cell (fPFC) using various biowaste sources (lactic acid, ethanol, methanol, urea, glycerol, and glucose) as fuel. The fPFC uses light irradiation and the decomposition of biowaste to generate electrical power under both flat and bending (r = 3 cm) conditions. When employed as a sweat band, the fPFC generates a maximum power 4.0 mW cm−2 g−1 from human sweat. The wearable fPFC is able to overcome many of the disadvantages of wearable microbial and enzymatic cells while providing comparable if not superior power density.

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 score0.003

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 communication0.0010.001
Open science0.0010.000
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 head0.009
GPT teacher head0.217
Teacher spread0.208 · 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

Citations29
Published2016
Admission routes2
Has abstractyes

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