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Record W1936915740 · doi:10.1002/cssc.201500190

Zeolitic Core@Shell Adsorbents for the Selective Removal of Free Glycerol from Crude Biodiesel

2015· article· en· W1936915740 on OpenAlexafffund
Nima Masoumifard, Pablo Arnal, Serge Kaliaguine, Freddy Kleitz

Bibliographic record

VenueChemSusChem · 2015
Typearticle
Languageen
FieldMaterials Science
TopicMesoporous Materials and Catalysis
Canadian institutionsUniversité Laval
FundersFonds de recherche du Québec – Nature et technologiesFonds Québécois de la Recherche sur la Nature et les Technologies
KeywordsMesoporous materialAdsorptionMicroporous materialZeoliteChemical engineeringGlycerolSorbentBiodieselMaterials scienceShell (structure)PorositySelective adsorptionBiodiesel productionChemistryOrganic chemistryCatalysisComposite material

Abstract

fetched live from OpenAlex

Selective adsorption of free glycerol from crude biodiesel was investigated by using mesoporous silica spheres coated with a thin shell of microporous silicalite-1. A polycrystalline silicalite-1 shell was formed upon first covering the external surfaces of various core templates with discrete silicalite-1 nanocrystals, and this was followed by short hydrothermal treatment to ensure shell uniformity. Batch glycerol adsorption experiments were conducted to evaluate the ability of the sorbents to remove free glycerol selectively from crude biodiesel mixtures at various temperatures, also in comparison to that of conventional sorbents, for example, bare mesoporous silica gel spheres and zeolites. The silicalite-1 shell provided a microporous membrane that hindered the diffusion of fatty acid methyl esters into the mesopores of the composite sorbent, whereas the large pore volume of the mesoporous core enabled multilayer glycerol adsorption; this ultimately substantially enhanced the performance in terms of purification yield and adsorption capacity.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.007
Threshold uncertainty score0.578

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 science0.0010.000
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 head0.055
GPT teacher head0.271
Teacher spread0.215 · 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

Citations14
Published2015
Admission routes2
Has abstractyes

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