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Record W1990378648 · doi:10.1063/1.2202919

High throughput screening of electrocatalysts for fuel cell applications

2006· article· en· W1990378648 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

fundA Canadian funder is recorded on the work.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueReview of Scientific Instruments · 2006
Typearticle
Languageen
FieldEnergy
TopicElectrocatalysts for Energy Conversion
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of CanadaCornell Center for Materials Research
KeywordsElectrocatalystMaterials scienceSubstrate (aquarium)NanotechnologyTernary operationElectrochemistryNanoparticleResolution (logic)Fuel cellsFluorescenceFluorescence microscopeMicroscopyChemical engineeringHigh-throughput screeningElectrodeComputer scienceOpticsChemistry

Abstract

fetched live from OpenAlex

We describe methodologies for the generation and screening of combinatorial libraries of electrocatalyst materials for fuel cell applications, generated by cosputtering of three elements onto a Si substrate coated with a Ta adhesion underlayer. Screening was carried out via a fluorescence assay as well as by scanning electrochemical microscopy. Whereas the former provided rapid qualitative screening with limited spatial resolution, the latter provided high spatial resolution. The fluorescence screening method was tested on Pt, PtBi, PtPb, and PtRu nanoparticles, while both methods were tested on a film containing a Pt–Bi–Pb ternary composition spread.

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.

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)0.001
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 score0.288
Threshold uncertainty score0.749

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.010
GPT teacher head0.240
Teacher spread0.230 · 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