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Record W2146443567 · doi:10.1139/v03-027

Considerations about phenol electrohydrogenation on electrodes made with reticulated vitreous carbon cathode

2003· article· en· W2146443567 on OpenAlexvenueno aff
François Laplante, L. Brossard, Hugues Ménard

Bibliographic record

VenueCanadian Journal of Chemistry · 2003
Typearticle
Languageen
FieldEngineering
TopicLignin and Wood Chemistry
Canadian institutionsnot available
Fundersnot available
KeywordsChemistryPhenolPalladiumCatalysisAdsorptionElectrochemistryCarbon fibersAqueous solutionMetalComposite numberSubstrate (aquarium)Noble metalChemical engineeringElectrodeInorganic chemistryOrganic chemistryMaterials scienceComposite materialPhysical chemistry

Abstract

fetched live from OpenAlex

The electrocatalytic hydrogenation (ECH) of phenol was carried out in aqueous media with catalytic powders. The catalytic powders were composed of palladium nanoaggregates deposited on various substrates such as Al 2 O 3 , BaSO 4 , and BaCO 3 . Composite powders are trapped (upon stirring) into a reticulated vitreous carbon (RVC) matrix, allowing a rapid in situ build-up of the electrode and alleviating the use of a binder, since the latter may have a detrimental effect on the ECH. For a given amount of noble metal (5% palladium by weight), it is observed that the ECH of phenol to cyclohexanol increases in the following order: Pd/BaCO 3 < Pd/BaSO 4 < Pd/Al 2 O 3 . It is deduced that the ECH rate is largely dependent on the adsorption of organic molecules on the nonmetallic substrate, and a model is considered to explain such a behavior. The ECH of phenol is also feasible at a reasonable rate with composite Pd/Al 2 O 3 (0.25% Pd by weight) powders fabricated by physical vapor deposition because the peripheral distribution of Pd nanoaggregates is favorable towards the ECH.Key words: electrocatalytic hydrogenation (ECH) of phenol, reticulated vitreous carbon (RVC), alumina-based catalyst, agglomeration process, adlineation point, adsorption.

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 score0.041
Threshold uncertainty score0.641

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 head0.004
GPT teacher head0.165
Teacher spread0.161 · 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

Citations42
Published2003
Admission routes1
Has abstractyes

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