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Record W3204870268 · doi:10.1021/acsaem.1c02254

Two-Step Dry Synthesis of Binderless 3D Low Pt-Loading Electrocatalysts for Direct Alkaline Methanol Fuel Cell Anodes

2021· article· en· W3204870268 on OpenAlexafffund
Elmira Pajootan, Sylvain Coulombe, Sasha Omanovic

Bibliographic record

VenueACS Applied Energy Materials · 2021
Typearticle
Languageen
FieldEnergy
TopicElectrocatalysts for Energy Conversion
Canadian institutionsMcGill University
FundersFonds de recherche du Québec – Nature et technologiesNatural Sciences and Engineering Research Council of CanadaFaculty of Engineering, McGill University
KeywordsMaterials scienceMethanolBimetallic stripElectrocatalystElectrolyteChemical engineeringElectrochemistryCatalysisDielectric spectroscopyAnodePlatinumInorganic chemistryElectrodeChemistryOrganic chemistry

Abstract

fetched live from OpenAlex

Three-dimensional (3D) porous electrocatalysts of low platinum loading were synthesized by gas-phase pulsed laser ablation (PLA) and deposited on multiwalled carbon nanotubes (MWCNTs) directly grown on a stainless steel mesh current collector by catalyst-free chemical vapor deposition (Pt/MWCNT/SS). The performance of the developed electrocatalysts was evaluated for the methanol oxidation reaction (MOR). The PLA chamber pressure and ablation time were optimized to achieve the highest methanol oxidation current per Pt loading and electrochemically active surface area while maintaining structural stability. The most robust electrocatalyst, obtained with PLA at 10–5 Torr for 5 min, exhibited a 50% higher methanol oxidation current compared to the commercial Pt/C with similar Pt loading. In addition, it showed the lowest loss of active sites after stability tests while maintaining its structural integrity. Increasing the electrolyte temperature from 0 to 80 °C significantly improved the methanol oxidation current on Pt/MWCNT/SS by 13 times compared to 5.8 times for Pt/C. Electrochemical impedance spectroscopy confirmed the faster kinetics of MOR and accelerated oxidation of adsorbed CO on the surface of the Pt/MWCNT/SS relative to the Pt/C.

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 categoriesMeta-epidemiology (narrow)
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 score0.077
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
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 head0.008
GPT teacher head0.221
Teacher spread0.212 · 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.

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

Citations12
Published2021
Admission routes2
Has abstractyes

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