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Record W2325119476 · doi:10.1149/06131.0035ecst

Manganese-Based Non-Precious Metal Catalyst for Oxygen Reduction in Acidic Media

2014· article· en· W2325119476 on OpenAlexafffund
Drew Higgins, Gang Wu, Hoon T Chung, Ulises Martinez, Shuguo Ma, Zhongwei Chen, Piotr Zelenay

Bibliographic record

VenueECS Transactions · 2014
Typearticle
Languageen
FieldEngineering
TopicFuel Cells and Related Materials
Canadian institutionsUniversity of Waterloo
FundersLos Alamos National LaboratoryLaboratory Directed Research and DevelopmentNatural Sciences and Engineering Research Council of Canada
KeywordsCatalysisManganesePolyanilineElectrolyteLeaching (pedology)Inorganic chemistryMetalElectrochemistryChemistryNitrogenCarbon fibersMaterials scienceNuclear chemistryElectrodeOrganic chemistryComposite numberComposite material

Abstract

fetched live from OpenAlex

Non-precious metal catalysts (NPMCs) based on manganese (Mn) are prepared by heat treating polyaniline (PANI), manganese acetate and Ketjenblack EC300J (KJ) carbon supports. Using a heat treatment temperature of 950 o C, followed by an acid leaching and second heat treatment step, Mn-PANI-KJ catalysts are found to provide onset and half-wave potentials of ca . 0.90 and 0.77 V vs. RHE, respectively, in 0.5 M H 2 SO 4 electrolyte. After 5,000 cycles of electrochemical durability testing, Mn-PANI-KJ demonstrates a half-wave potential loss of only ca . 20 mV, superior to the 80 mV loss for our previously developed iron (Fe)-PANI-KJ catalyst. Increased surface nitrogen concentrations and relative ratios of pyridinic to graphitic nitrogen species were observed at increased Mn-PANI-KJ preparation temperatures, along with the evolution of graphene-like and graphitic nanoshell structures.

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)0.000
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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.007
GPT teacher head0.197
Teacher spread0.190 · 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
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

Citations10
Published2014
Admission routes2
Has abstractyes

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