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Record W2567457467 · doi:10.1002/celc.201600701

Iridium−Rhodium Nanoparticles for Ammonia Oxidation: Electrochemical and Fuel Cell Studies

2016· article· en· W2567457467 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueChemElectroChem · 2016
Typearticle
Languageen
FieldChemical Engineering
TopicAmmonia Synthesis and Nitrogen Reduction
Canadian institutionsUniversity of Ottawa
FundersConselho Nacional de Desenvolvimento Científico e Tecnológico
KeywordsRhodiumIridiumCatalysisAmmoniaSodium borohydrideElectrochemistryChemistryDehydrogenationInorganic chemistryElectrolysisMetalAmmonia boraneNuclear chemistryOrganic chemistryPhysical chemistryElectrode

Abstract

fetched live from OpenAlex

Abstract This study reports the use of carbon‐supported IrRh/C electrocatalysts with different iridium‐to‐rhodium atomic ratios (0 : 100, 50 : 50, 70 : 30, 90 : 10, and 100 : 0) for ammonia electro‐oxidation (AmER) in alkaline media. The materials prepared by using the sodium borohydride method showed a mean diameter of 4.5, 4.8, 4.2, and 4.5 nm for Ir/C, Ir 90 Rh 10 /C, Ir 70 Rh 30 /C, and Ir 50 Rh 50 /C, respectively. According to electrochemical and fuel cell experiments, the Ir 50 Rh 50 /C catalyst was the most promising towards AmER. This catalyst, which consisted predominantly of the metallic Ir/Rh phases, showed a 500 % higher current density and 55 % higher maximum power than that obtained for Ir/C. After 8 h galvanostatic electrolysis, 93 % of initial ammonia was degraded when using Ir 50 Rh 50 /C, whereas it was only 70 % with Ir/C. The high activity of the Ir 50 Rh 50 /C is attributed to a synergic effect of two metals at this iridium‐to‐rhodium ratio, which enhances the kinetics of AmER contributing towards ammonia dehydrogenation at lower potentials.

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.

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 score0.007
Threshold uncertainty score0.791

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 head0.019
GPT teacher head0.251
Teacher spread0.232 · 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