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Record W2084636059 · doi:10.5539/jmsr.v3n1p25

Using PVA, Methacrylate and Lauroyl Chitosan as Separator in Supercapacitors

2013· article· en· W2084636059 on OpenAlexvenueno aff
M. A. Hashim, Lawal Sa’adu, Masbudi Bin Baharuddin, Karsono Ahmad Dasuki

Bibliographic record

VenueJournal of Materials Science Research · 2013
Typearticle
Languageen
FieldMaterials Science
TopicSupercapacitor Materials and Fabrication
Canadian institutionsnot available
FundersUniversiti Sains Islam Malaysia
KeywordsMaterials scienceSupercapacitorPolyvinyl alcoholSeparator (oil production)Dielectric spectroscopyPhosphoric acidElectrolyteMethacrylateChitosanComposite materialPolymerElectrochemistryChemical engineeringElectrodeMonomer

Abstract

fetched live from OpenAlex

A separator is prepared from the resulting mixture of hybrid polymer electrolyte polyvinyl alcohol (PVA) (70%) and Phosphoric acid (H3PO4) (30%) immersed in the solution of the combination of polymethyl (methacrylate) and lauroyl chitosan (PLC), for supercapacitor application. The Electrochemical Impedance Spectroscopy (EIS) was conducted twice at normal temperature in an interval of Seven days with very high electrical conductivity of to be 6.42 × 10-4 S cm-1 and 1.84 × 10-4 S cm-1 within these intervals respectively and very low resistance. The supercapacitor was then fabricated using a Commercially Prepared Multi-walled Carbon Nanotube (CPMWCNT) which was sized to an area of 3.5 cm2 and assembled in an innovative supercapacitor tester. The result indicates a relatively high efficiency of about 90% and also exhibited long and excellent cycleability of life time (>1000 cycles) under different voltage windows.

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 score0.002

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 communication0.0000.001
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 head0.105
GPT teacher head0.402
Teacher spread0.297 · 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

Citations10
Published2013
Admission routes1
Has abstractyes

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