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Record W2328132961 · doi:10.1021/cm403224m

Evidence for the Formation of Nitrogen-Rich Platinum and Palladium Nitride Nanoparticles

2013· article· en· W2328132961 on OpenAlexaff
Gabriel M. Veith, Andrew R. Lupini, Loïc Baggetto, James F. Browning, Jong K. Keum, Alberto Villa, Laura Prati, Alexander B. Papandrew, Gabriel A. Goenaga, David R. Mullins, Steven E. Bullock, Nancy J. Dudney

Bibliographic record

VenueChemistry of Materials · 2013
Typearticle
Languageen
FieldEnergy
TopicElectrocatalysts for Energy Conversion
Canadian institutionsLockheed Martin (Canada)
Fundersnot available
KeywordsNanoparticleMaterials sciencePlatinumNitridePalladiumCatalysisMetalSputteringElectrochemistryNitrogenAtom (system on chip)Chemical engineeringPhase (matter)Benzyl alcoholInorganic chemistryElectrodeNanotechnologyMetallurgyThin filmChemistryPhysical chemistryOrganic chemistry

Abstract

fetched live from OpenAlex

We report evidence for the formation of nitrogen-rich precious metal nanoparticles (Pt, Pd) prepared by reactive sputtering of the pure metal in a N 2 plasma. The composition of the nanoparticles varies as a function of particle size and growth conditions. For the smallest particles the nitrogen content appears to be as high as 6.7 N atoms for each Pd atom or 5.9 N atoms for each Pt atom whereas bulk films have nominal compositions of Pt 7.3 N and Pd 2.5 N. The unusually large N content in the nanoparticles is balanced with H. The nanoparticles are metastable in air and moisture, slowly decomposing over several years. The catalytic properties of these N-rich nanoparticles were accessed by rotating disk electrode electrochemical studies, the liquid phase oxidation of benzyl alcohol, and gas phase CO oxidation, and support the experimental evidence for the materials composition.

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)0.000
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 score0.001
Threshold uncertainty score0.277

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 head0.024
GPT teacher head0.237
Teacher spread0.214 · 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

Citations47
Published2013
Admission routes1
Has abstractyes

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