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Record W2907165843 · doi:10.1002/adma.201804846

Integrating PGM‐Free Catalysts into Catalyst Layers and Proton Exchange Membrane Fuel Cell Devices

2019· review· en· W2907165843 on OpenAlexaff
Dustin Banham, Ja‐Yeon Choi, Takeaki Kishimoto, Siyu Ye

Bibliographic record

VenueAdvanced Materials · 2019
Typereview
Languageen
FieldEnergy
TopicElectrocatalysts for Energy Conversion
Canadian institutionsBallard Power Systems (Canada)
Fundersnot available
KeywordsProton exchange membrane fuel cellCatalysisMaterials scienceCathodeChemical engineeringPlatinumCommercializationNanotechnologyOxygen reduction reactionElectrodeElectrochemistryChemistryEngineeringElectrical engineeringOrganic chemistryBusiness

Abstract

fetched live from OpenAlex

While proton exchange membrane fuel cells (PEMFCs) continue to expand into commercial markets, there is still pressure to decrease cost. One of the largest opportunities to reducing cost is to reduce the amount of platinum-group metal (PGM) catalysts used in the electrodes (particularly the cathode). Over the past decade, exciting advances in the Fe/N/C family of PGM-free oxygen reduction reaction (ORR) catalysts has provided great optimism that not only can PGMs at the cathode be reduced but possibly be completely eliminated. In fact, in September 2017, Ballard Power Systems announced the commercialization of the world's first PEMFC product to utilize a PGM-free catalyst at the cathode (FCgen-micro (non-precious-metal catalyst, NPMC)). However, for these catalysts to be used in more demanding applications, an improved understanding and new design approaches for PGM-free catalyst layers will be required. Herein, some of the latest research on both modeling and experimental studies in the field of PGM-free catalyst layer research are discussed. In addition, a short discussion on Ballard's new NPMC is provided.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.854
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0010.000
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 head0.018
GPT teacher head0.279
Teacher spread0.261 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations158
Published2019
Admission routes1
Has abstractyes

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