MétaCan
Menu
Back to cohort
Record W3018973836 · doi:10.1002/anie.202005248

Strain Influences the Hydrogen Evolution Activity and Absorption Capacity of Palladium

2020· article· en· W3018973836 on OpenAlexafffund
Ryan P. Jansonius, Phil A. Schauer, David Dvořák, Benjamin P. MacLeod, D. K. Fork, Curtis P. Berlinguette

Bibliographic record

VenueAngewandte Chemie International Edition · 2020
Typearticle
Languageen
FieldEnergy
TopicElectrocatalysts for Energy Conversion
Canadian institutionsUniversity of British Columbia
FundersCanada Research ChairsCanadian Institute for Advanced ResearchGoogle
KeywordsPalladiumElectrocatalystElectrochemistryHydrogenStrain (injury)Ultimate tensile strengthMaterials scienceAbsorption (acoustics)ElectrodeCatalysisChemical engineeringInorganic chemistryChemistryComposite materialOrganic chemistryPhysical chemistry

Abstract

fetched live from OpenAlex

Strain engineering can increase the activity and selectivity of an electrocatalyst. Tensile strain is known to improve the electrocatalytic activity of palladium electrodes for reduction of carbon dioxide or dioxygen, but determining how strain affects the hydrogen evolution reaction (HER) is complicated by the fact that palladium absorbs hydrogen concurrently with HER. We report here a custom electrochemical cell, which applies tensile strain to a flexible working electrode, that enabled us to resolve how tensile strain affects hydrogen absorption and HER activity for a thin film palladium electrocatalyst. When the electrodes were subjected to mechanically-applied tensile strain, the amount of hydrogen that absorbed into the palladium decreased, and HER electrocatalytic activity increased. This study showcases how strain can be used to modulate the hydrogen absorption capacity and HER activity of palladium.

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)0.001
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
Metaresearch0.0000.001
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)0.0010.000

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 head0.017
GPT teacher head0.229
Teacher spread0.211 · 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

Citations37
Published2020
Admission routes2
Has abstractyes

Explore more

Same venueAngewandte Chemie International EditionSame topicElectrocatalysts for Energy ConversionFrench-language works237,207