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Record W4285497302 · doi:10.1149/ma2022-01351436mtgabs

Channel Diameter Effect of Porous Carbon Microparticles on PEMFC Performance for Highly Active Ultra-Low Pt Catalysts

2022· article· en· W4285497302 on OpenAlexaff
Hee‐Eun Kim, Young Jun Lee, Hyunjoo Lee

Bibliographic record

VenueECS Meeting Abstracts · 2022
Typearticle
Languageen
FieldEngineering
TopicFuel Cells and Related Materials
Canadian institutionsKootenay Association for Science & Technology
Fundersnot available
KeywordsProton exchange membrane fuel cellCatalysisMaterials scienceChemical engineeringCathodeMembrane electrode assemblyMesoporous materialPorosityBattery (electricity)NanotechnologyElectrodeComposite materialChemistryAnodePower (physics)Organic chemistry

Abstract

fetched live from OpenAlex

Proton exchange membrane fuel cells (PEMFCs) have received much attention as environmentally benign automotive power sources. PEMFCs can offer a large amount of electricity required for autonomous vehicles that battery-powered systems may not be able to provide. However, because PEMFC electrodes are based on expensive and scarce Pt catalysts, Pt minimization is necessary to expand the PEMFC market. To reduce Pt usage, various attempts have been made to increase the intrinsic activity of Pt catalysts, particularly at the cathode where the oxygen reduction reaction (ORR) occurs. Despite decades of progress, high ORR activity was typically reported in half-cell setups and frequently failed to show corresponding performance in single-cell. Catalysts with low Pt content perform poorly in the high-current density region because the thick catalyst layer limits mass transport. [1] In addition, low Pt loading catalysts typically suffer more in long-term operation. [2] As a result, it is critical to develop PEMFC catalysts with low Pt content that can facilitate mass transport while also exhibiting high durability. Herein, we report highly active and durable PtFe@C catalysts with ultra-low amounts of Pt (1 wt%) on channeled mesoporous carbon (CMC) particles. These CMC particles are designed to have continuous channels with open porosity and a large surface area, facilitating the mass transport behavior and maximizing cell performance. Block-copolymer particles (BCPs) with different molecular weights were used to fabricate CMC particles with pore diameters ranging from 13 to 63 nm. Two steps of pre-crosslinking and hyper-crosslinking were conducted prior to the carbonization step to preserve the porous internal structure of the BCP-based carbon support during high-temperature treatment. After depositing Pt onto the support by facile incipient wetness impregnation method followed by reduction, thin layers of carbon shell were observed to encapsulate the PtFe alloy nanoparticles. The channel diameter effect on the mass transport was studied in both half-cell and single-cell. Interestingly, from the cell performance obtained with varying channel diameters and different oxygen concentrations, we concluded that both reactant (proton and oxygen) supply and product (water) removal were greatly enhanced with larger channel size. With the largest channel diameter of 63 nm, initial mass activity in the single-cell was obtained to be 3.5 A mg Pt -1 , which is the highest value reported to date to the best of our knowledge. Cell performance under H 2 -air flow, which is the industrially relevant condition, surpassed the commercial 20 wt% Pt/C with only 1/20 of the Pt loading. The origin of enhanced cell performance upon enlarging the channel diameter was investigated by separating kinetic, ohmic (electronic and ionic charge transport), and mass transport overpotentials. Both the proton and oxygen transport resistance were confirmed to be reduced with larger channel size. Moreover, carbon shell protected Fe from getting leached out in an acidic environment, resulting in preserved catalyst structure and high durability. The outstanding performance of 51 kW/g Pt in H 2 /air condition after 30,000 cycles of accelerated degradation tests (ADTs) was observed. This work will open a new paradigm to develop PEMFC catalysts with much higher activity and durability while simultaneously minimizing Pt use. References [1] A. Kongkanand, M. F. Mathias, J . Phys. Chem. Lett. 2016 , 7 , 1127-1137. [2] R. Borup , A. Weber, Fuel Cell Performance and Durability Consortium, US DOE 2019 Annual Merit Review Proceedings, https://www.hydrogen.energy.gov/pdfs/review19/fc135_borup_2019_o.pdf (accessed: September 2021).

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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 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 score0.476
Threshold uncertainty score0.729

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 science0.0000.000
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 head0.006
GPT teacher head0.192
Teacher spread0.187 · 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

Citations1
Published2022
Admission routes1
Has abstractyes

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