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Record W2045767183 · doi:10.1021/ie800009u

A New Coated Catalyst for the Production of Diacetone Alcohol via Catalytic Distillation

2008· article· en· W2045767183 on OpenAlexafffund
Greg P. Dechaine, Flora T. T. Ng

Bibliographic record

VenueIndustrial & Engineering Chemistry Research · 2008
Typearticle
Languageen
FieldMaterials Science
TopicLayered Double Hydroxides Synthesis and Applications
Canadian institutionsUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsCatalysisAmberliteDistillationCalcinationChemistryAcetoneAldol condensationMagnesiumIon-exchange resinChemical engineeringBatch reactorCeramicCondensationMethyl acetateCoatingChromatographyInorganic chemistryOrganic chemistry

Abstract

fetched live from OpenAlex

A method has been developed for the production of catalytically active coated ceramic distillation saddles for use in catalytic distillation (CD) columns. A thin layer of magnesium acetate was applied to ceramic Norton saddles using a sol−gel dip-coating method. The magnesium acetate coating was converted to the catalytically active magnesium oxide (MgO) via a temperature-ramped calcination program. The kinetic performance of the MgO-coated saddles for the aldol condensation of acetone was determined in a batch reactor and a batch CD column. Although the coated saddles provided lower yields to DAA than Amberlite IRA-900 ion exchange resins in a batch reactor, their improved mass transfer characteristics provided higher yields than the same ion exchange resins held inside fiberglass bags in a CD column at low flow rates. The coated saddles also showed significant improvements in selectivity to DAA compared to the resin catalysts for the aldol condensation reaction carried out both in the batch reactor and in the CD column.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.137
GPT teacher head0.332
Teacher spread0.195 · 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 source (direct Gemma or distilled Codex), 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

Citations18
Published2008
Admission routes2
Has abstractyes

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