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Slurry-Phase Batch Microreactor for Hydroconversion Studies

2015· article· en· W2513478116 on OpenAlexafffund
Ross S. Kukard, Kevin J. Smith

Bibliographic record

VenueEnergy & Fuels · 2015
Typearticle
Languageen
FieldEngineering
TopicCatalysis and Hydrodesulfurization Studies
Canadian institutionsUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMicroreactorBatch reactorSlurryCatalysisMixing (physics)Chemical engineeringChemistryPhase (matter)Materials scienceOrganic chemistryComposite material

Abstract

fetched live from OpenAlex

A novel microreactor that allows for mixed, slurry-phase batch experiments to be conducted at temperatures to 445 °C and pressures of 13.8 MPa is reported. The reactor uses a glass insert to isolate the reaction mixture from the catalytically active reactor walls and a vortex mixer to promote gas–liquid–solid mixing and requires only 150 μL of reaction mixture to operate. The rapid heating and cooling (to 445 °C and back to room temperature in under 30 min) afforded by the small size makes this reactor ideal for fast catalyst-screening studies. The utility of the reactor is demonstrated through a study of the hydroconversion of diphenylmethane (DPM) at 445 °C and 13.8 MPa and the hydrodeoxygenation of 4-methylphenol (4-MP) at 375 °C and 4.8 MPa, both conducted using an unsupported MoS 2 catalyst. A high-speed video is used to identify 2000 rpm as the optimum mixing speed for the microreactor. Conversion data for DPM and 4-MP in the microreactor is used to determine rate constants for the reactions and, hence, quantify MoS 2 catalytic activity and thermal/reactor wall activity. The MoS 2 activity is found to be in good agreement with published stirred batch reactor results using the same catalyst.

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: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.140
Threshold uncertainty score0.569

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 head0.046
GPT teacher head0.293
Teacher spread0.247 · 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 designNot applicable
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

Citations2
Published2015
Admission routes2
Has abstractyes

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