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Record W1972634648 · doi:10.1149/2.0931410jes

Effect of CeOx Crystallite Size on the Chemical Stability of CeOx Nanoparticles

2014· article· en· W1972634648 on OpenAlexfundno aff
Dustin Banham, Siyu Ye, Tommy Cheng, Shanna Knights, S. Michael Stewart, Mahlon S. Wilson, Fernando Garzon

Bibliographic record

VenueJournal of The Electrochemical Society · 2014
Typearticle
Languageen
FieldEngineering
TopicFuel Cells and Related Materials
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of CanadaU.S. Department of Energy
KeywordsCrystalliteElectrolyteDissolutionMaterials scienceChemical engineeringOxideAnodeNanoparticleCathodeChemical stabilityChemistryElectrodeMetallurgyNanotechnology

Abstract

fetched live from OpenAlex

CeOx is an excellent free radical scavenger to improve polymer electrolyte membrane durability. However, this metal oxide will dissolve during accelerated stress testing (AST), with the resulting cations transporting to the cathode catalyst layer (CCL) leading to performance reduction/degradation of the PEMFC. Controlling the rate of CeOx dissolution is therefore of great importance, as it may be possible to maintain sufficient Ce cations for free radical scavenging while minimizing the impact of these cations on the CCL. Here the effect of CeOx crystallite size on CeOx dissolution was investigated. Three CeOx additives were prepared having crystallite sizes of 6, 13, or 25 nm. An ex-situ method was used to evaluate the chemical stability of these three CeOx samples, as well as one commercially available CeOx. It was determined that surface area, rather than crystallite size, is the best predictor of chemical stability. In-situ membrane electrode assembly AST cycling was then performed, demonstrating that when low loadings of CeOx (0.006 mg/cm2) are used, the ex-situ method correctly predicts trends in end of life (EOL) performance. Finally, it is shown that increasing the anode RH during AST cycling leads to significantly higher EOL performance losses.

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 score0.001
Threshold uncertainty score0.279

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.003
GPT teacher head0.183
Teacher spread0.180 · 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

Citations45
Published2014
Admission routes1
Has abstractyes

Explore more

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