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Record W2807832819 · doi:10.1021/acs.chemmater.8b01324

Accurate Coulometric Quantification of Hydrogen Absorption in Palladium Nanoparticles and Thin Films

2018· article· en· W2807832819 on OpenAlexafffund
Rebecca S. Sherbo, Marta Moreno-González, Noah J. J. Johnson, David Dvořák, D. K. Fork, Curtis P. Berlinguette

Bibliographic record

VenueChemistry of Materials · 2018
Typearticle
Languageen
FieldEnergy
TopicElectrocatalysts for Energy Conversion
Canadian institutionsUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of CanadaCanada First Research Excellence FundCanada Research ChairsCanadian Institute for Advanced ResearchCanada Foundation for InnovationGoogle
KeywordsPalladiumMaterials scienceHydrogenElectrochemistryNanoparticleThin filmCoulometryChemical engineeringAnalytical Chemistry (journal)NanotechnologyElectrodeChemistryCatalysisOrganic chemistryPhysical chemistry

Abstract

fetched live from OpenAlex

We report here an electrochemical method for precise and accurate quantification of hydrogen absorption in palladium materials. We demonstrate that conventional chronocoulometry over-reports adsorbed hydrogen due to charge from the accompanying hydrogen oxidation reaction (HOR). We designed and built a bespoke electrochemical flow cell that mitigates the concurrent HOR reaction and consequently provides improved accuracy and reproducibility relative to other existing electrochemical techniques. The efficacy of this technique is demonstrated experimentally for a series of palladium sample types: a 100 nm electron-beam deposited thin film, a 20 μm electrodeposited palladium film, a casting of 21 nm edge-length cubic nanoparticles, and a casting of 27 nm edge-length octahedral nanoparticles. We contend that this method is the most effective for measuring hydrogen uptake in different palladium samples.

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)0.001
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: none
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
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 head0.016
GPT teacher head0.243
Teacher spread0.227 · 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

Citations42
Published2018
Admission routes2
Has abstractyes

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