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Record W2792255898 · doi:10.1002/adma.201707635

Chemical‐to‐Electricity Carbon: Water Device

2018· article· en· W2792255898 on OpenAlexaff
Sisi He, Yueyu Zhang, Longbin Qiu, Longsheng Zhang, Yun Xie, Jian Pan, Peining Chen, Bingjie Wang, Xiaojie Xu, Yongfeng Hu, Cao‐Thang Dinh, Phil De Luna, Mohammad Norouzi Banis, Zhiqiang Wang, Tsun‐Kong Sham, Xin-Gao Gong, Bo Zhang, Huisheng Peng, Edward H. Sargent

Bibliographic record

VenueAdvanced Materials · 2018
Typearticle
Languageen
FieldMaterials Science
TopicSupercapacitor Materials and Fabrication
Canadian institutionsWestern UniversityCanada Research ChairsUniversity of Toronto
FundersNational Natural Science Foundation of ChinaScience and Technology Commission of Shanghai MunicipalityMinistry of Science and Technology
KeywordsMaterials scienceCarbon nanotubeElectricityCarbon fibersAqueous solutionOxygenChemical energyPower densityElectric potential energyPolarization (electrochemistry)Ionic bondingChemical engineeringNanotechnologyChemical physicsPower (physics)Electrical engineeringComposite materialIonChemistryThermodynamicsOrganic chemistry

Abstract

fetched live from OpenAlex

Abstract The ability to release, as electrical energy, potential energy stored at the water:carbon interface is attractive, since water is abundant and available. However, many previous reports of such energy converters rely on either flowing water or specially designed ionic aqueous solutions. These requirements restrict practical application, particularly in environments with quiescent water. Here, a carbon‐based chemical‐to‐electricity device that transfers the chemical energy to electrical form when coming into contact with quiescent deionized water is reported. The device is built using carbon nanotube yarns, oxygen content of which is modulated using oxygen plasma‐treatment. When immersed in water, the device discharges electricity with a power density that exceeds 700 mW m −2 , one order of magnitude higher than the best previously published result. X‐ray absorption and density functional theory studies support a mechanism of operation that relies on the polarization of sp 2 hybridized carbon atoms. The devices are incorporated into a flexible fabric for powering personal electronic devices.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
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 score0.010
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0030.003

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 head0.255
Teacher spread0.241 · 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; both teacher heads agree on what is shown here.

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

Citations66
Published2018
Admission routes1
Has abstractyes

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