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Understanding the Influence of Surface Acid and Base Properties and Water on Work Functions and Triboelectric Charging Using Inverse Gas Chromatography

2006· article· en· W4378446136 on OpenAlexaff
Rick Veregin, Maria McDougall, Mike Hawkins, Cuong Vong, Vlad Skorokhod, H. A. Schreiber

Bibliographic record

VenueTechnical programs and proceedings/Technical program and proceedings · 2006
Typearticle
Languageen
FieldChemistry
TopicAdsorption, diffusion, and thermodynamic properties of materials
Canadian institutionsPolytechnique MontréalXerox (Canada)
Fundersnot available
KeywordsInverse gas chromatographyAdsorptionWork functionRelative humidityTriboelectric effectChemistryWork (physics)HumidityOxideBase (topology)Chemical engineeringInverseMaterials scienceAnalytical Chemistry (journal)MetalChromatographyThermodynamicsOrganic chemistryPhysical chemistryGeometryMathematicsPhysics

Abstract

fetched live from OpenAlex

Inverse Gas Chromatography (IGC) has been applied to study surface Lewis acid and base properties of xerographic developers. Model carrier and toners were prepared and the toners blended with metal oxide surface additives: silica, titania and alumina. The effect of additives on charging, work functions and surface chemistry, as measured by IGC, was studied. All properties were evaluated as a function of relative humidity, to improve understanding of the effect of water as relative humidity increases. Results under dry and wet conditions generally support a work function model for charging, where work functions are determined by surface acid-base properties. Adsorption of water onto surfaces can be followed by IGC, work functions and charging. All provide a consistent picture that water adsorption leads to surfaces that have essentially the properties of adsorbed water at sufficiently high RH.

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 score0.002
Threshold uncertainty score0.003

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 communication0.0000.001
Open science0.0000.000
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 head0.040
GPT teacher head0.222
Teacher spread0.182 · 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

Citations1
Published2006
Admission routes1
Has abstractyes

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