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Record W4206079113 · doi:10.1039/d1cp05130j

Surface sensitive infrared spectroelectrochemistry using palladium electrodeposited on ITO-modified internal reflection elements

2022· article· en· W4206079113 on OpenAlexafffund
Vi Thuy Thi Phan, Ian R. Andvaag, Nicole D. Boyle, Grace T. Flaman, Bipinlal Unni, Ian J. Burgess

Bibliographic record

VenuePhysical Chemistry Chemical Physics · 2022
Typearticle
Languageen
FieldChemistry
TopicElectrochemical Analysis and Applications
Canadian institutionsUniversity of Saskatchewan
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsPalladiumAttenuated total reflectionInfrared spectroscopyMaterials scienceInfraredOxideAnalytical Chemistry (journal)ChemistryOpticsOrganic chemistry

Abstract

fetched live from OpenAlex

precursor reveals a distinct and reproducible minimum that corresponds to the onset of the electronic percolation threshold of the deposited metal islands as confirmed by scanning electron microscopy. Effective medium theory (EMT) is used to model the reflectivity of the Si/ITO/Pd interface as a function of the volume fraction of the deposited metal and to calculate the ATR-SEIRA spectra of an adsorbed monolayer of organic molecules. EMT calculations are in qualitative agreement with most aspects of the experimental spectra which show that the intensities and spectral line shapes are highly dependent on the amount of deposited palladium. The methodology is applied to the potential dependent adsorption of 4-methoxypyridine on palladium. The experimental results show that the pyridine derivative adopts an edge-tilted orientation on oxide covered Pd and undergoes an orientation change in the hydrogen adsorption region that increases both the degree of edge-tilt and the extent of π-bonding between the pyridine ring and the metal.

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 score0.005

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 science0.0010.000
Research integrity0.0000.001
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 head0.270
Teacher spread0.254 · 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

Citations12
Published2022
Admission routes2
Has abstractyes

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