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Record W4416024689 · doi:10.1016/j.rineng.2025.108136

Mass transfer and hydrodynamic analysis in triphasic aerobic epoxidation of limonene over Ru/ activated carbon

2025· article· en· W4416024689 on OpenAlexafffund
M. Kaddouri, Dahi Akmach, Serge Kaliaguine

Bibliographic record

VenueResults in Engineering · 2025
Typearticle
Languageen
FieldMaterials Science
TopicMesoporous Materials and Catalysis
Canadian institutionsUniversité Laval
FundersNatural Sciences and Engineering Research Council of CanadaUniversité Euromed de FèsUniversité Laval
KeywordsMass transferMass transfer coefficientCatalysisActivated carbonBubbleParticle (ecology)TurbulenceOxygen

Abstract

fetched live from OpenAlex

• Gas-liquid-solid reaction • External mass transfer • Hydrodynamic analysis • Direct aerobic epoxidation • Biomass derived liquids This study investigates mass transfer in a triphasic stirred tank reactor during the aerobic epoxidation of limonene using Ru supported on activated carbon as a heterogeneous catalyst. The volumetric gas-liquid mass transfer coefficient (k L a) was determined via the dynamic absorption method, based on oxygen uptake, referring to Danckwerts surface renewal model. Hydrodynamic parameters, including power input and Reynolds number were calculated to characterize the flow regimes, while bubble diameter and gas holdup were estimated to evaluate the interfacial area available for mass transfer. The Kolmogorov length scale was found significantly larger than the catalyst particle diameter, indicating negligible contribution of solid phase in the fluid carrier hydrodynamic conditions. Additionally, the Weisz-Prater number was much lower than one, confirming the absence of internal mass transfer limitations within the catalyst. Results demonstrate that increased turbulence enhances interfacial area and improves mass transfer, due to intensified bubble breakup, underscoring the importance of hydrodynamic control in optimizing catalytic aerobic epoxidation.

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)0.000
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 score0.032
Threshold uncertainty score0.519

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.004
GPT teacher head0.207
Teacher spread0.204 · 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

Citations0
Published2025
Admission routes2
Has abstractyes

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