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Record W2786258485 · doi:10.1021/acsaem.7b00227

Scalable Fabrication of Supercapacitors by Nozzle-Free Electrospinning

2018· article· en· W2786258485 on OpenAlexaff
Kai Shi, Konstantinos P. Giapis

Bibliographic record

VenueACS Applied Energy Materials · 2018
Typearticle
Languageen
FieldMaterials Science
TopicSupercapacitor Materials and Fabrication
Canadian institutionsNational Research Council Canada
FundersAdvanced Research Projects AgencyAdvanced Research Projects Agency - EnergyCalifornia Institute of TechnologySmall Business Innovative Research and Small Business Technology TransferU.S. Department of Energy
KeywordsElectrospinningSupercapacitorMaterials scienceNanofiberFabricationPolyvinylpyrrolidoneElectrodeCapacitanceNozzleComposite materialHorizontal scan rateCarbon nanotubeCarbon nanofiberEnergy storageNanotechnologyElectrochemistryPolymer chemistryPolymerCyclic voltammetryChemistry

Abstract

fetched live from OpenAlex

Nozzle-free electrospinning was investigated as a facile technique for producing nanoscale materials for supercapacitors. MnO 2 nanofibers and their composites with multiwalled carbon nanotubes (MWCNTs) were synthesized in a single step, using polyvinylpyrrolidone (PVP) and Mn(CH 3 COO) 2 ·4H 2 O as starting materials, followed up by heat treatment in ambient air. Nanofibers of relatively uniform diameter were produced at high rates. The nanofibers exhibited good electrical contact between MnO 2 and MWCNT without degradation, which was attributed to the improved stability of MWCNTs in PVP solutions. Electrochemical testing of the composites demonstrated a high capacitance of 1.43 F cm –2 at a scan rate of 100 mV s –1 in the three-electrode tests. Electrodes and devices produced by nozzle-free electrospinning are promising for practical energy storage applications.

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 score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.001
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 head0.215
Teacher spread0.205 · 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

Citations30
Published2018
Admission routes1
Has abstractyes

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