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Record W2623006752 · doi:10.1149/2.0051709jes

Product Distributions and Efficiencies for Ethanol Oxidation in a Proton Exchange Membrane Electrolysis Cell

2017· article· en· W2623006752 on OpenAlexafffund
Rakan M. Altarawneh, Peter G. Pickup

Bibliographic record

VenueJournal of The Electrochemical Society · 2017
Typearticle
Languageen
FieldEnergy
TopicElectrocatalysts for Energy Conversion
Canadian institutionsMemorial University of Newfoundland
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsChemistryElectrolysisProton exchange membrane fuel cellAcetic acidCatalysisAcetaldehydeStoichiometryInorganic chemistryDirect-ethanol fuel cellAnodeEthanolProduct distributionHydrogenEthanol fuelElectrodeOrganic chemistryPhysical chemistry

Abstract

fetched live from OpenAlex

The stoichiometry, efficiency, and product distribution for ethanol oxidation in fuel cell hardware has been determined at 80°C for commercial Pt/C, PtRu/C and PtSn/C anode catalysts. The amounts of ethanol consumed and acetic acid and acetaldehyde produced were determined by proton NMR spectroscopy while CO2 was measured with a non-dispersive infrared CO2 monitor. The Pt/C catalyst was most selective for the complete oxidation of ethanol to CO2 at all potentials and therefore produced the highest number of electrons per ethanol molecule (stoichiometry). Consequently, it would provide the highest efficiency for a fuel cell, and for an electrolysis cell at high current densities. However, PtRu/C provided much higher currents at low overpotentials and therefore better electrolysis efficiency than Pt/C at low current densities. The main product at the PtRu/C catalyst was acetic acid, with ≥ 86% conversion at potentials ≥ 0.35 V vs. a dynamic hydrogen electrode. The PtSn/C catalyst also provided high yields of acetic acid (65–75%), with substantial production of CO2 (26–27%) at high potentials.

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 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 score0.006
Threshold uncertainty score0.462

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 science0.0010.000
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 head0.009
GPT teacher head0.235
Teacher spread0.226 · 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

Citations43
Published2017
Admission routes2
Has abstractyes

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