MétaCan
Menu
← Back to cohort
Record W2901459079 · doi:10.1149/ma2018-02/41/1384

Novel Method for Measuring in-Plane Effective Diffusivity of Ultrathin Catalyst Layer for PEMFC

2018· article· en· W2901459079 on OpenAlexaff
Yongwook Kim, Jeff T. Gostick

Bibliographic record

VenueECS Meeting Abstracts · 2018
Typearticle
Languageen
FieldEngineering
TopicFuel Cells and Related Materials
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsThermal diffusivityNanoporousProton exchange membrane fuel cellCatalysisCommercializationMaterials scienceLayer (electronics)DiffusionNanotechnologyChemical engineeringPlatinumFuel cellsProcess engineeringComputer scienceChemistryEngineeringThermodynamicsOrganic chemistryPhysics

Abstract

fetched live from OpenAlex

Fuel cells are being increasingly deployed in various applications, however like all emerging technologies, its cost still needs to be lowered for wider commercialization. The most expensive component is the noble platinum (Pt) catalyst, so reducing the amount of Pt remains an ongoing effort. There are two ways to approach this problem: find a cheaper alternative1,2 or use less Pt.3,4 Regarding the former, most candidates are less active than Pt, so require higher loading and thicker catalyst layers, while the latter case, the lower loading must be compensated by higher accessibility and utilization. In both cases it is essential to design improved catalyst layer (CL) structures to provide maximum accessibility of reactant to the catalytic sites.5 Characterizing the nanoporous structure of the CL in terms of transport processes is essential to understanding how to design a better layer. The key transport parameter in fuel cell operation is the effective diffusivity through the CL structure since diffusion is the primary mode of reactant transport. Despite the importance of characterizing the effective diffusivity of the CL, the thinness of the CL combined with the fact that it’s not self-supporting, has hindered the development of well-established, easy to apply, and standardized tool and only a limited number of technique is available.6 There are several requirements in designing the proper ex-situ effective diffusivity measurement experiment for the CL. First, any surface in contact with the sample must be scratch-free to eliminate additional diffusion pathways. Second, the need for sealing should be avoided as it is nearly impossible to properly seal such thin (~10µm) layer. Lastly, the experiment should be designed so that it is performed within a reasonable time. This work presents a new method building on previous work7,8, but using a radial geometry instead. The main advantage of the method is that it requires no sealing around the edge of the sample, therefore easily applied even to ultrathin porous layers. Inside a cylindrical chamber, the catalyst layer sample is placed between two circular pedestals and the oxygen concentration is measured at the center as shown in Figure 1(a). The sample is initially flushed with air, then nitrogen gas is flowed past the sample perimeter at high flow rate to ensure instant change in boundary condition. The depletion of the oxygen concentration at the center of the sample is measured and recorded as a function of time. The effective diffusivity is determined by fitting the analytical solution of the Fick’s second law in cylindrical coordinates to the transient oxygen concentration profile.9 The technique was validated against open air and known GDL materials.7,8 In the present study, this novel experimental technique was applied to ultrathin porous layers fabricated with different ink formulae to explore the impact of morphology, and strong differences were seen. References: M. Lefèvre, E. Proietti, F. Jaouen, and J.-P. Dodelet, Science (80-. )., 324, 71–74 (2009) E. Proietti et al., Nat. Commun., 2, 416 (2011) S. Martin, B. Martinez-Vazquez, P. L. Garcia-Ybarra, and J. L. Castillo, J. Power Sources, 229, 179–184 (2013) S. Shukla, K. Domican, and M. Secanell, ECS Trans., 69, 761–772 (2015). Y. Tabe, M. Nishino, H. Takamatsu, and T. Chikahisa, J. Electrochem. Soc., 158, B1246 (2011) Z. Yu and R. N. Carter, J. Power Sources, 195, 1079–1084 (2010). R. Rashapov, F. Imami, and J. T. Gostick, Int. J. Heat Mass Transf., 85, 367–374 (2015) R. R. Rashapov and J. T. Gostick, Transp. Porous Media, 115, 1–23 (2016). J. Crank, (1975) "The Mathematics of Diffusion". 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: Methods · Consensus signal: Methods
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.018
GPT teacher head0.256
Teacher spread0.237 · 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
GenreMethods

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
Published2018
Admission routes1
Has abstractyes

Explore more

Same venueECS Meeting Abstracts→Same topicFuel Cells and Related Materials→French-language works237,207→