MétaCan
Menu
Back to cohort
Record W2512690621 · doi:10.1149/ma2016-02/53/3962

Local Mass Transport Resistance of Low-Loaded PEM Fuel-Cell Catalyst-Layers

2016· article· en· W2512690621 on OpenAlexaff
Anna T.S. Freiberg, Tobias Schuler, Michael C. Tucker, Marc Secanell, Adam Z. Weber

Bibliographic record

VenueECS Meeting Abstracts · 2016
Typearticle
Languageen
FieldEngineering
TopicFuel Cells and Related Materials
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsProton exchange membrane fuel cellLimiting currentFabricationMass transportMaterials scienceStack (abstract data type)CatalysisOxidePlatinumNanotechnologyElectrochemistryChemical engineeringChemistryElectrodeComputer scienceEngineering physicsEngineering

Abstract

fetched live from OpenAlex

To this day, the content of noble metals in the catalyst layers of proton exchange membrane fuel cells (PEMFCs) is still accounting for up to 60 % of the total stack cost and is therefore needed to be reduced for large-scale applications as automobile industry. Though a lot of effort was put into fabrication of catalyst-layers (CLs) with sufficiently low platinum loading, the performance below a certain content starts to decay rapidly. Within the CL, a complex interplay of mass and charge transport takes place, making the identification and quantification of the limiting parameters especially difficult and laborious. Finally, this resistance was found to be due to a local mass transport resistance scaling inversely with the electrochemical active surface area (ECSA). In this presentation, the results for the local transport resistance measured in-situ via our unique hydrogen limiting current setup for CLs of varying loading, ionomer content and fabrication are shown. The effects of temperature and relative humidity on this intrinsic property are discussed, allowing a deeper understanding of the processes occurring close to the catalyst surface. Though water production, oxide effects and possible peroxide formation can make oxygen limiting currents hard to quantify in detail, data is included, allowing a direct comparison of the transport properties and pathways of both reactants. The data provided shows the potential of this measurement technic to identify the transport limitations in a fast and easy way, which helps to optimize the electrode structure, composition and fabrication for several operation-conditions and therefore applications. Acknowledgements This work was supported by the Assistant Secretary for Energy Efficiency and Renewable Energy, Fuel Cell Technologies Program of the U.S. Department of Energy under Contract No. DE-AC02-05CH11231 and by CRADA agreement LB08003874 between Lawrence Berkeley National Laboratory and Toyota Motor Company. Figure 1

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.006
GPT teacher head0.182
Teacher spread0.176 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2016
Admission routes1
Has abstractyes

Explore more

Same venueECS Meeting AbstractsSame topicFuel Cells and Related MaterialsFrench-language works237,207