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Record W2167241524 · doi:10.5539/apr.v6n4p106

Design, Fabrication and Characterization of a Commercially Prepared Carboxyl Multiwalled Carbon Nanotubes With a Hybrid Polymer Electrolytes

2014· article· en· W2167241524 on OpenAlexvenueno aff
M. A. Hashim, Lawal Sa’adu

Bibliographic record

VenueApplied Physics Research · 2014
Typearticle
Languageen
FieldMaterials Science
TopicSupercapacitor Materials and Fabrication
Canadian institutionsnot available
FundersUniversiti Sains Islam Malaysia
KeywordsMaterials scienceSupercapacitorElectrolyteCapacitanceCyclic voltammetryElectrodeElectrochemistryFabricationChemical engineeringPolymerCarbon nanotubeNanotechnologyComposite materialChemistry

Abstract

fetched live from OpenAlex

We reported the fabrication of three different supercapacitor cells using a commercially prepared carboxyl multiwalled carbon nanotubes (CPCMWCNTs) as electrode, and hybrid solid polymer electrolytes (HSPE) of different electrical conductivities as separators. The Three cells were then constructed and leveled as cell-A (C90PVdF-HFP10 |H50| C90PVdF-HFP10), cell-B (C90PVdF-HFP10 |H60| C90PVdF-HFP10) and cell-C (C90PVdF-HFP10 |H70| C90PVdF-HFP10). Numbers of analysis, such as FESEM, XRD, TGA and electrochemical analysis were carried out on both the commercial CNT and that of the electrolytes. From the overall results of the electrochemical analysis of cyclic voltammetry (CV), cell-B delivered higher capacitance of 60.10 Fg?1doubling that of cell-A, and tripling cell-C. Whereas the charge-discharge (CD) tests carried out in the cells reveals that even at the lower voltage window of 1.5 V, cell-A delivered slightly better than that of B and C with a balanced and good discharge capacitance of 86.06 Fg?1 and higher energy/power densities of 432.22 Jg?1/8.37 Jg?1s?1and less internal resistance. All the cells were able to deliver a modest capacitance at a voltage window of 3V.

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 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 score0.009
Threshold uncertainty score0.523

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)0.0000.000

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 head0.030
GPT teacher head0.272
Teacher spread0.242 · 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.

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

Citations2
Published2014
Admission routes1
Has abstractyes

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