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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 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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

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.

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 categoriesMeta-epidemiology (narrow)
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 score0.459
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.002
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.040
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