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Record W1995065388 · doi:10.1021/ie048948f

Adsorption of Asphaltenes on Metals

2005· article· en· W1995065388 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

VenueIndustrial & Engineering Chemistry Research · 2005
Typearticle
Languageen
FieldChemistry
TopicPetroleum Processing and Analysis
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsAsphalteneAdsorptionTolueneSaturation (graph theory)HeptaneHydrocarbonChemistryMolar massMetalEmulsionLangmuirInorganic chemistryMonolayerChemical engineeringOrganic chemistryPolymer

Abstract

fetched live from OpenAlex

The adsorption of Athabasca and Cold Lake C 7 -asphaltenes on stainless steel (304L), iron, and aluminum powders was measured using UV−vis spectrophotometry. The effects of resins, temperature, and n -heptane-to-toluene ratio were also investigated. In all cases, Langmuir (type I) isotherms were observed, indicating that asphaltenes saturated the available surface area for adsorption. The saturation adsorptions of the asphaltenes on metals (0.25−2.7 mg/m 2 ) were of the same order of magnitude as adsorption of asphaltenes on minerals. The saturation adsorptions were less than the monolayer surface coverage observed on water-in-hydrocarbon emulsion interfaces, indicating that there are a limited number of adsorption sites on the metals. Higher molar saturation adsorptions were observed for resins and low molar mass asphaltenes, suggesting that adsorption was limited by the morphology of the metal surface. In general, higher mass saturation adsorptions were observed when asphaltenes self-associated to greater extents and consequently larger molecules adsorbed on the surface.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
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 score0.023
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.093
GPT teacher head0.341
Teacher spread0.249 · 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