MétaCan
Menu
← Back to cohort
Record W4254401873 · doi:10.1002/ange.202017082

Physical Separation of H<sub>2</sub> Activation from Hydrogenation Chemistry Reveals the Specific Role of Secondary Metal Catalysts

2021· article· en· W4254401873 on OpenAlexafffund
Aiko Kurimoto, Ryan P. Jansonius, Aoxue Huang, Antonio M. Marelli, David Dvořák, Camden Hunt, Curtis P. Berlinguette

Bibliographic record

VenueAngewandte Chemie · 2021
Typearticle
Languageen
FieldEnergy
TopicElectrocatalysts for Energy Conversion
Canadian institutionsCanadian Institute for Advanced ResearchUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research ChairsCanadian Institute for Advanced Research
KeywordsPalladiumCatalysisChemistryHydrogenElectrochemistryMembraneMetalInorganic chemistryChemical engineeringPhotochemistryOrganic chemistryPhysical chemistryElectrode

Abstract

fetched live from OpenAlex

Abstract An electrocatalytic palladium membrane reactor (ePMR) uses electricity and water to drive hydrogenation without H2 gas. The device contains a palladium membrane to physically separate the formation of reactive hydrogen atoms from hydrogenation of the unsaturated organic substrate. This separation provides an opportunity to independently measure the hydrogenation reaction at a surface without any competing H2 activation or proton reduction chemistry. We took advantage of this feature to test how different metal catalysts coated on the palladium membrane affect the rates of hydrogenation of C=O and C=C bonds. Hydrogenation occurs at the secondary metal catalyst and not the underlying palladium membrane. These secondary catalysts also serve to accelerate the reaction and draw a higher flux of hydrogen through the membrane. These results reveal insights into hydrogenation chemistry that would be challenging using thermal or electrochemical hydrogenation experiments.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.002
Threshold uncertainty score0.006

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 integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.217
Teacher spread0.209 · 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 designNot applicable
Domainnot available
GenreOther

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

Citations1
Published2021
Admission routes2
Has abstractyes

Explore more

Same venueAngewandte Chemie→Same topicElectrocatalysts for Energy Conversion→French-language works237,207→