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Record W2938163582 · doi:10.1021/jacs.9b01442

Efficient Electrocatalytic Hydrogenation with a Palladium Membrane Reactor

2019· article· en· W2938163582 on OpenAlexafffund
Rebecca S. Sherbo, Aiko Kurimoto, Christopher M. Brown, Curtis P. Berlinguette

Bibliographic record

VenueJournal of the American Chemical Society · 2019
Typearticle
Languageen
FieldEnergy
TopicElectrocatalysts for Energy Conversion
Canadian institutionsUniversity of British Columbia
FundersCanada Foundation for InnovationNatural Sciences and Engineering Research Council of CanadaCanada Research ChairsCanadian Institute for Advanced Research
KeywordsChemistryPalladiumElectrolysisReagentElectrochemistryElectrolyteMembraneMembrane reactorInorganic chemistryCatalysisOrganic chemistryElectrode

Abstract

fetched live from OpenAlex

. This technique uses a palladium membrane to physically separate the electrochemical and hydrogenation chemistry. As a result, hydrogenation can be performed electrochemically with protons but in any organic solvent. In this article, we outline a series of experiments showing how hydrogenation in the palladium membrane reactor proceeds at faster reaction rates and with much higher voltage efficiency than hydrogenation at an electrode. Moreover, the organic reaction chemistry in the membrane reactor can be performed in organic solvents and without contamination by electrolytes. The physical separation of the hydrogenation compartment from the electrolysis compartment therefore broadens the scope of electrolytically-driven reactions that are available, and simplifies reagent handling and purification.

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 score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0010.001

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 head0.004
GPT teacher head0.200
Teacher spread0.196 · 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

Citations150
Published2019
Admission routes2
Has abstractyes

Explore more

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