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Record W2505387953 · doi:10.1021/acs.iecr.6b02468

Levulinic Acid Production from Starch Using Microwave and Oil Bath Heating: A Kinetic Modeling Approach

2016· article· en· W2505387953 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueIndustrial & Engineering Chemistry Research · 2016
Typearticle
Languageen
FieldEngineering
TopicCatalysis for Biomass Conversion
Canadian institutionsMcGill University
FundersFonds de recherche du Québec – Nature et technologiesNatural Sciences and Engineering Research Council of Canada
KeywordsAmylopectinAmyloseLevulinic acidStarchChemistryYield (engineering)MicrowaveKineticsMicrowave heatingHydrolysisChemical engineeringMaterials scienceOrganic chemistryCatalysis

Abstract

fetched live from OpenAlex

This work examines the role played by starch composition and heating media in the synthesis of the promising biorefinery chemical levulinic acid (LA). Three corn starches with different amylose/amylopectin ratios were converted to LA using both microwave and conventional oil bath heating. The results obtained for the different reaction temperatures and times were used to calculate kinetic parameters using a multireaction model. It was observed that the long preheating and cooling times employed in microwave heating led to equivalent reaction temperatures below the set temperatures, and this in turn affected the product distributions via changes in the reaction kinetics. At low reaction temperatures, high amylopectin waxy corn starch gave higher LA yields than normal or high-amylose corn starch. Similarly, LA yields were higher at lower temperatures for oil bath heating than for microwave heating. The maximum LA yield obtained was around 53–55% for all substrates and for both heating media, but was obtained for a shorter reaction time and at a lower equivalent temperature in the case of microwave heating.

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.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.330
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.112
GPT teacher head0.288
Teacher spread0.176 · 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