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Record W2601221250 · doi:10.1021/acs.nanolett.7b00870

Pd Nanoparticles Coupled to WO<sub>2.72</sub> Nanorods for Enhanced Electrochemical Oxidation of Formic Acid

2017· article· en· W2601221250 on OpenAlexafffund
Zheng Xi, Daniel P. Erdosy, Adriana Mendoza‐Garcia, Paul N. Duchesne, Junrui Li, Michelle Muzzio, Qing Li, Peng Zhang, Shouheng Sun

Bibliographic record

VenueNano Letters · 2017
Typearticle
Languageen
FieldEnergy
TopicElectrocatalysts for Energy Conversion
Canadian institutionsDalhousie University
FundersArmy Research LaboratoryArmy Research OfficeNatural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health ResearchMultidisciplinary University Research InitiativeCanadian Light SourceUniversity of WashingtonU.S. Department of EnergyNational Science Foundation
KeywordsNanorodCatalysisFormic acidElectrochemistryNanoparticleChronoamperometryChemistryElectrocatalystChemical engineeringMaterials scienceInorganic chemistryNanotechnologyPhysical chemistryCyclic voltammetryElectrodeOrganic chemistry

Abstract

fetched live from OpenAlex

We synthesize a new type of hybrid Pd/WO 2.72 structure with 5 nm Pd nanoparticles (NPs) anchored on 50 × 5 nm WO 2.72 nanorods. The strong Pd/WO 2.72 coupling results in the lattice expansion of Pd from 0.23 to 0.27 nm and the decrease of Pd surface electron density. As a result, the Pd/WO 2.72 shows much enhanced catalysis toward electrochemical oxidation of formic acid in 0.1 M HClO 4; it has a mass activity of ∼1600 mA/mg Pd in a broad potential range of 0.4–0.85 V (vs RHE) and shows no obvious activity loss after a 12 h chronoamperometry test at 0.4 V. Our work demonstrates an important strategy to enhance Pd NP catalyst efficiency for energy conversion reactions.

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 categoriesMeta-epidemiology (narrow)
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 score1.000

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 science0.0010.000
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 head0.008
GPT teacher head0.234
Teacher spread0.226 · 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.

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

Citations150
Published2017
Admission routes2
Has abstractyes

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