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Record W3179957816 · doi:10.1139/cjc-2021-0097

Vapour phase hydrogenation of cinnamaldehyde using cobalt supported inside and outside hollow carbon spheres

2021· article· en· W3179957816 on OpenAlexvenueno aff
Pumza Mente, Victor Mashindi, Alice Magubane, Tumelo N. Phaahlamohlaka, Prakash M. Gangatharan, Roy P. Forbes, Neil J. Coville

Bibliographic record

VenueCanadian Journal of Chemistry · 2021
Typearticle
Languageen
FieldEngineering
TopicCatalysis for Biomass Conversion
Canadian institutionsnot available
Fundersnot available
KeywordsCinnamaldehydeChemistryCatalysisSelectivityCobaltCinnamyl alcoholCarbon fibersPhase (matter)Chemical engineeringSinteringDecompositionInorganic chemistryOrganic chemistryMaterials scienceComposite material

Abstract

fetched live from OpenAlex

The hydrogenation of cinnamaldehyde is usually performed in the liquid phase in batch mode. In this study, a vapour phase flow system has been used to evaluate the use of cobalt catalysts supported inside and outside hollow carbon spheres (HCSs). The influence of temperature, hydrogen flow rate, and catalyst mass on the hydrogenation reaction was investigated. The catalysts generally showed modest conversion to the required products, hydrocinnamaldehyde, 3-phenyl propanol, cinnamyl alcohol, together with formation of various decomposition products. The data revealed that the Co@HCS showed better conversion and product selectivity compared with the Co/HCS. The catalysts with smaller particle sizes (ca. 6 nm) were more efficient than those with larger particles (30–40 nm). An increase in reaction temperature (200–300 °C) resulted in a lower cinnamaldehyde conversion and a poor product selectivity. TPR studies revealed that the Co@HCSs had a stronger metal-support interaction than the Co/HCSs catalysts. Catalyst recycling studies revealed that only the Co/HCSs could be regenerated (four cycles) and post reaction analysis of the catalysts revealed that this was due to HCS pore blockage and not Co sintering.

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 score0.008
Threshold uncertainty score0.514

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 head0.208
Teacher spread0.198 · 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

Citations1
Published2021
Admission routes1
Has abstractyes

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