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Record W4403095221 · doi:10.1021/acscatal.4c02871

Active Sites and Stability Study of Fe/N/C Catalyst in PEMFCs: A Decade of Stunning Progress and the Remaining Challenges

2024· article· en· W4403095221 on OpenAlexaff
Xiaohua Yang, Zhiyin Huang, Lei Du, Qian Li, Siyu Ye

Bibliographic record

VenueACS Catalysis · 2024
Typearticle
Languageen
FieldEnergy
TopicElectrocatalysts for Energy Conversion
Canadian institutionsInstitut National de la Recherche Scientifique
FundersNatural Science Foundation of Guangdong ProvinceNational Natural Science Foundation of China
KeywordsCatalysisStunningChemical engineeringChemistryMaterials scienceEngineeringOrganic chemistryMedicine

Abstract

fetched live from OpenAlex

Over the past few decades, Fe/N/C, as the most promising platinum group metal (PGM) alternative, has provided a unique roadmap for developing proton exchange membrane fuel cells (PEMFCs) due to the low-cost and earth-abundant elements. However, the high-performance Fe/N/C catalysts always demonstrate poor stability, especially during real-world fuel cell operation, which has attracted increased attention in recent years. Great efforts have been devoted in the past decade to understanding active sites and their degradation mechanisms. Herein, we review the progress in the past decade in terms of active site identification and degradation phenomena/mechanisms of Fe/N/C catalysts, particularly in PEMFCs (rather than the conventional three-electrode system). Meanwhile, we also highlight the latest advances in improving the stability of PGM-free catalysts. At the end of this critical review, perspectives are provided to determine the potential strategies to further improve the activity and stability of Fe/N/C catalysts for PEMFCs.

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: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.517
Threshold uncertainty score0.712

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
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 head0.021
GPT teacher head0.264
Teacher spread0.243 · 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 designQualitative
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

Citations29
Published2024
Admission routes1
Has abstractyes

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