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Record W2771466750 · doi:10.1021/acssuschemeng.7b03580

Web-like 3D Architecture of Pt Nanowires and Sulfur-Doped Carbon Nanotube with Superior Electrocatalytic Performance

2017· article· en· W2771466750 on OpenAlexafffund
Md Ariful Hoque, Fathy M. Hassan, Altamash M. Jauhar, Gaopeng Jiang, Mark Pritzker, Ja‐Yeon Choi, Shanna Knights, Siyu Ye, Zhongwei Chen

Bibliographic record

VenueACS Sustainable Chemistry & Engineering · 2017
Typearticle
Languageen
FieldEnergy
TopicElectrocatalysts for Energy Conversion
Canadian institutionsBallard Power Systems (Canada)University of Waterloo
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsCarbon nanotubeCatalysisMaterials scienceElectrochemistryProton exchange membrane fuel cellNanowireSulfurChemical engineeringNanotechnologyDopingCarbon fibersElectrodeChemistryComposite materialOrganic chemistryPhysical chemistryOptoelectronicsMetallurgy

Abstract

fetched live from OpenAlex

Development of highly durable electrocatalysts for oxygen reduction reaction (ORR) is critical for proton exchange membrane fuel cells. Herein, we report the synthesis, characterization, and electrochemical performance of 1D sulfur-doped carbon nanotubes (S-CNT) supported 1D Pt nanowires (PtNW/S-CNT). PtNW/S-CNT synthesized by a modified solvothermal method possesses a unique web-like 3D architecture that is beneficial for oxygen reduction. We demonstrate that PtNW/S-CNT exhibits impressive activity retention under potential cycling between 0.05 and 1.5 V vs RHE over 3000 cycles. The reductions in electrochemically active surface area (ECSA, 7% loss) and mass activity (19% loss) of PtNW/S-CNT after accelerated durability testing (ADT) are found to be much lower than the dramatic losses observed with commercial Pt/C (>99% loss in ECSA and mass activity) under identical conditions. The PtNW/S-CNT catalyst also shows very high specific activity (1.61 mA cm –2 ) in comparison to Pt/C (0.24 mA cm –2 ).

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)0.000
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 score0.008
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

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 communication0.0000.000
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 head0.003
GPT teacher head0.171
Teacher spread0.168 · 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

Citations65
Published2017
Admission routes2
Has abstractyes

Explore more

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