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Generation of Energy from a Single Fuel Cell Using Synthesized Solid Electrolyte Membrane from Functionalized Polyisoprene/ Carbon Nanotubes

2018· article· en· W2799955446 on OpenAlexvenueno aff
C. A. Idibie, K. J. Awatefe, R. O. Ogboru

Bibliographic record

VenueJournal of Research Updates in Polymer Science · 2018
Typearticle
Languageen
FieldEngineering
TopicFuel Cells and Related Materials
Canadian institutionsnot available
Fundersnot available
KeywordsElectrolyteMaterials scienceProton exchange membrane fuel cellMembraneCarbon nanotubePolymerOpen-circuit voltageSurface modificationChemical engineeringCatalysisCarbon fibersIon exchangeIonComposite materialVoltageElectrodeOrganic chemistryChemistryPhysical chemistry

Abstract

fetched live from OpenAlex

Study on the electrical energy generated from a single fuel cell using a synthesized solid electrolyte membrane from polyisoprene that was impregnated with carbonanotubes was carried out. The initial functionalization of the polymer yielded an ion exchange capacity (IEC) of 4.04, 7.82, 11.1 and 15.53 mmol/g with their corresponding degrees of sulphonation (DS) of 10.93, 21.1, 30.03 and 42.02 %, respectively. The later but highest DS achieved water uptake of 49.23 wt % and proton conductivities of 4.3 x 10-3, 1.2 x 10-3 and 2.6 x 10-2 S/cm for membrane of thickness 250, 215 and 120 mm, respectively. The performance testing of the membrane in a single fuel cell achieved an open circuit voltage (OCV) of 647.23 mV with the synthesised membrane of 35 wt % catalyst loading, 589.79 mV and 410.48 mV of 25 wt % and 15 wt % catalyst loading, respectively at constant DS (42.02 %). Their corresponding power densities achieved were 68.67, 49.20 and 35.83 Mw/cm2, respectively. Thus the functionalization of polyisoprene impregnated with carbon nanotubes through the process of sulphonation with chlorosulphonic acid resulted into the development of solid polymer electrolyte membrane for fuel cell application.

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 score0.090
Threshold uncertainty score0.496

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
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 head0.044
GPT teacher head0.299
Teacher spread0.255 · 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".

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Citations1
Published2018
Admission routes1
Has abstractyes

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