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Record W2643860675 · doi:10.1002/cplu.201700245

Facile Synthesis of a Porous Pd/Cu Alloy and its Enhanced Performance toward Methanol and Formic Acid Electrooxidation

2017· article· en· W2643860675 on OpenAlexaff
Bo Yan, Caiqin Wang, Hui Xu, Ke Zhang, Shumin Li, Yukou Du

Bibliographic record

VenueChemPlusChem · 2017
Typearticle
Languageen
FieldEnergy
TopicElectrocatalysts for Energy Conversion
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsFormic acidCatalysisAlloyMethanolPalladiumAscorbic acidCopperElectrochemistryMaterials scienceElectrocatalystInorganic chemistryPorosityChemical engineeringChemistryMetallurgyOrganic chemistryElectrodeComposite material

Abstract

fetched live from OpenAlex

Abstract Reported is a porous Pd/Cu alloy catalyst synthesized by a one‐pot method, in which the Pd/Cu alloy is formed by using l‐ascorbic acid as reducing agent to simultaneously reduce the copper precursor and the palladium precursor. The copper incorporated with Pd can reduce the cost of the catalyst and enhance the catalytic performance. The morphology of the Pd/Cu alloy catalyst can be controlled by altering the ratios of Pd to Cu. Electrochemical characterizations indicate that Pd/Cu alloy catalysts possess good activity and long‐term stability for the electrooxidation of methanol and formic acid. Compared with commercial Pd/C, the as‐prepared Pd65Cu35 shows enhanced activities of electrooxidation of methanol and formic acid. This study highlights an easy strategy to obtain the shape‐controlled Pd/Cu alloyed catalysts and their potential application as electrocatalysts in fuel cells or other fields.

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)0.0000.000

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 head0.015
GPT teacher head0.232
Teacher spread0.217 · 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

Citations27
Published2017
Admission routes1
Has abstractyes

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