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Record W2317156118 · doi:10.1021/ja106217u

Biologically Inspired Highly Durable Iron Phthalocyanine Catalysts for Oxygen Reduction Reaction in Polymer Electrolyte Membrane Fuel Cells

2010· article· en· W2317156118 on OpenAlexaff
Wenmu Li, Aiping Yu, Drew Higgins, Bernard G. Llanos, Zhongwei Chen

Bibliographic record

VenueJournal of the American Chemical Society · 2010
Typearticle
Languageen
FieldEnergy
TopicElectrocatalysts for Energy Conversion
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsChemistryCatalysisPhthalocyanineElectrolyteElectrocatalystPolymerOxygenMembraneInorganic chemistryChemical engineeringElectrodeElectrochemistryOrganic chemistryPhysical chemistry

Abstract

fetched live from OpenAlex

In the present work, we have designed and synthesized a new highly durable iron phtalocyanine based nonprecious oxygen reduction reaction (ORR) catalyst (Fe-SPc) for polymer electrolyte membrane fuel cells (PEMFCs). The Fe-SPc, with a novel structure inspired by that of naturally occurring oxygen activation catalysts, is prepared by a nonpyrolyzing method, allowing adequate control of the atomic structure and surface properties of the material. Significantly improved ORR stability of the Fe-SPc is observed compared with the commercial Fe-Pc catalysts. The Fe-SPc has similar activity to that of the commercial Fe-Pc initially, while the Fe-SPc displays 4.6 times higher current density than that of the commercial Fe-Pc after 10 sweep potential cycles, and a current density that is 7.4 times higher after 100 cycles. This has been attributed to the incorporation of electron-donating functional groups, along with a high degree of steric hindrance maintaining active site isolation. Nonprecious Fe-SPc is promising as a potential alternative ORR electrocatalyst 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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.679

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.218
Teacher spread0.212 · 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

Citations288
Published2010
Admission routes1
Has abstractyes

Explore more

Same venueJournal of the American Chemical SocietySame topicElectrocatalysts for Energy ConversionFrench-language works237,207