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Record W4200477301 · doi:10.1002/elsa.202100153

Thin carbon–polypyrrole composite materials for supercapacitor electrodes by novel bipolar electrochemical setup

2021· article· en· W4200477301 on OpenAlexafffund
Nigel Patterson, Anna Ignaszak

Bibliographic record

VenueElectrochemical Science Advances · 2021
Typearticle
Languageen
FieldMaterials Science
TopicSupercapacitor Materials and Fabrication
Canadian institutionsUniversity of New Brunswick
FundersNew Brunswick Innovation FoundationCanada Foundation for Innovation
KeywordsBuckypaperSupercapacitorMaterials sciencePolypyrroleElectrodeStackingComposite numberCarbon fibersStack (abstract data type)Electrical conductorOptoelectronicsLayer (electronics)ElectrochemistryConductorNanotechnologyThin filmComposite materialCarbon nanotubeComputer scienceChemistryOrganic chemistry

Abstract

fetched live from OpenAlex

Abstract A simple approach to mitigate the use of high voltages on thin bipolar electrodes is described here for the first time. The method involves stacking the desired thin conductor onto a thicker one, thereby creating a pseudo‐single electrode whose width is the sum of the two. Placing the target material on the positive or negative face of the combined bipolar electrode selects which reaction it will be involved in. This method is used to graft sheets of carbon paper, cloth, and buckypaper with an aminophenyl layer that subsequently binds polypyrrole in a simple two stage reaction to create a one‐sided deposit of a supercapacitive material. By itself the necessary driving potential for these reactions exceed 90 V, while with the stack it drops to 9 V. The resulting supercapacitor electrodes display good performance, with specific capacitances up to 210 mF/cm 2 and lasting 2500 charge/discharge cycles.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.011
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.007
GPT teacher head0.244
Teacher spread0.237 · 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

Citations3
Published2021
Admission routes2
Has abstractyes

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