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Record W2010945344 · doi:10.1126/science.1252553

Interfacial Effects in Iron-Nickel Hydroxide–Platinum Nanoparticles Enhance Catalytic Oxidation

2014· article· en· W2010945344 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

VenueScience · 2014
Typearticle
Languageen
FieldMaterials Science
TopicCatalytic Processes in Materials Science
Canadian institutionsDalhousie University
FundersNational Synchrotron Radiation Research Center
KeywordsCatalysisPlatinumNickelTransition metalHydroxideInorganic chemistryNanoparticleCarbon monoxideChemistryAdsorptionMetalOxidePlatinum nanoparticlesChemical engineeringMaterials scienceNanotechnologyPhysical chemistryOrganic chemistry

Abstract

fetched live from OpenAlex

Hybrid metal nanoparticles can allow separate reaction steps to occur in close proximity at different metal sites and accelerate catalysis. We synthesized iron-nickel hydroxide-platinum (transition metal-OH-Pt) nanoparticles with diameters below 5 nanometers and showed that they are highly efficient for carbon monoxide (CO) oxidation catalysis at room temperature. We characterized the composition and structure of the transition metal-OH-Pt interface and showed that Ni(2+) plays a key role in stabilizing the interface against dehydration. Density functional theory and isotope-labeling experiments revealed that the OH groups at the Fe(3+)-OH-Pt interfaces readily react with CO adsorbed nearby to directly yield carbon dioxide (CO2) and simultaneously produce coordinatively unsaturated Fe sites for O2 activation. The oxide-supported PtFeNi nanocatalyst rapidly and fully removed CO from humid air without decay in activity for 1 month.

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)0.003
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
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 score0.011
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.002
Open science0.0020.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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 head0.258
Teacher spread0.252 · 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