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Record W2168739971 · doi:10.1002/cctc.201500517

Development of a Magnetically Recyclable Molybdenum Disulfide Catalyst for Direct Hydrodesulfurization

2015· article· en· W2168739971 on OpenAlexafffund
Seyyedmajid Sharifvaghefi, Ying Zheng

Bibliographic record

VenueChemCatChem · 2015
Typearticle
Languageen
FieldEngineering
TopicCatalysis and Hydrodesulfurization Studies
Canadian institutionsUniversity of New Brunswick
FundersCanada Research Chairs
KeywordsHydrodesulfurizationDibenzothiopheneCatalysisMolybdenumMolybdenum disulfideCationic polymerizationChemical engineeringMagnetiteChemistrySulfideSuperparamagnetismPulmonary surfactantHeterogeneous catalysisInorganic chemistryMaterials scienceOrganic chemistryMagnetizationMetallurgy

Abstract

fetched live from OpenAlex

Abstract Superparamagnetic MoS 2 /SiO 2 /Fe 3 O 4 catalysts that consist of magnetite as the core, silica as the covering layer, and molybdenum sulfide as the top layer were prepared in easy steps. Two different surfactants (anionic and cationic) were used to assist with the synthesis of two of the samples, and one sample was prepared without surfactant. The surfactant was found to have a significant effect on the properties and activity of the final catalysts. Hydrodesulfurization (HDS) tests of the catalysts show higher activity for the cationic‐surfactant‐assisted catalyst. Between the direct desulfurization (DDS) and hydrogenation pathways for the prepared catalysts, the DDS pathway was found to be dominant for the HDS of dibenzothiophene. As a result of the magnetic properties of this catalyst, it can be separated easily from the reaction media by a magnetic field applied externally and reused, which makes it an ideal choice for slurry reactors that process heavy and extra‐heavy crude oil.

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 score0.059
Threshold uncertainty score0.715

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 head0.028
GPT teacher head0.236
Teacher spread0.208 · 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

Citations21
Published2015
Admission routes2
Has abstractyes

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