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Record W1973976917 · doi:10.1021/ef801059y

Separation of Petroporphyrins from Asphaltenes by Chemical Modification and Selective Affinity Chromatography

2009· article· en· W1973976917 on OpenAlexaff
Cindy-Xing Yin, Jeffrey M. Stryker, Murray R. Gray

Bibliographic record

VenueEnergy & Fuels · 2009
Typearticle
Languageen
FieldMaterials Science
TopicPorphyrin and Phthalocyanine Chemistry
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsAsphalteneChemistryOxalyl chlorideVanadiumFraction (chemistry)ChromatographyMetalSilica gelChlorideSize-exclusion chromatographyPorphyrinGel permeation chromatographyNickelChemical modificationDerivative (finance)Organic chemistryPolymer chemistryPolymer

Abstract

fetched live from OpenAlex

Part of the metalloporphyrin fraction was separated from asphaltenes by a combination of reactive modification and affinity chromatography. The targeted vanadyl porphyrin complexes were modified by reaction with oxalyl chloride followed by a long chain alkylamine or perfluoroalkylaniline to produce an imido vanadium(IV) derivative bearing an octadecyl or perfluoroctyl side chain. This derivatized asphaltene was then chromatographed on C 18 -silica gel or perfluorous (-C 8 F 17 ) silica gel to remove the tagged metalloporphyrins from the remaining asphaltene. With the C 18 -tagging and reverse-phase chromatography method, from 15 to 40% of the total vanadium content together with comparable percentages of nickel was removed, with less than 5% mass loss from the asphaltene fraction. The fluorous tagging process gave better removal of metals on one sample but was limited by the lower recovery of asphaltenes on other samples. This approach requires further optimization for quantitative selective separation of metal complexes from heavy oils and to overcome the aggregation of the metals with other components of the asphaltenes.

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 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)0.000
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 score0.006
Threshold uncertainty score0.541

Codex and Gemma teacher scores by category

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 science0.0000.000
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 head0.250
Teacher spread0.240 · 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 teacher head, 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

Citations37
Published2009
Admission routes1
Has abstractyes

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