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Record W2753894799 · doi:10.1021/acs.jafc.7b03094

Solvent-Free Biodiesel Production Catalyzed by Crude Lipase Powder from Seeds: Effects of Alcohol Polarity, Glycerol, and Thermodynamic Water Activity

2017· article· en· W2753894799 on OpenAlexfundno aff
Paul Alain Nanssou Kouteu, Joël Blin, Bruno Baréa, Nathalie Barouh, Pierre Villeneuve

Bibliographic record

VenueJournal of Agricultural and Food Chemistry · 2017
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicEnzyme Catalysis and Immobilization
Canadian institutionsnot available
FundersCentre de Coopération Internationale en Recherche Agronomique pour le DéveloppementAgence Universitaire de la FrancophonieEuropean Commission
KeywordsLipaseGlycerolChemistryAlcoholBiodieselEthanolBiodiesel productionOrganic chemistryCatalysisSolventTriacylglycerol lipaseFatty acidMonoglycerideYield (engineering)MethanolOleic acidNuclear chemistryChromatographyBiochemistryEnzymeMaterials science

Abstract

fetched live from OpenAlex

The aim of this work was to evaluate the potential of crude lipase powders made from Adansonia grandidieri and Jatropha mahafalensis seeds for the synthesis of fatty acid alkyl esters in a solvent-free system. The influence of the nature of the alcohol, the amount of glycerol, and hydration of the powder was investigated. Results showed that the activity of these crude lipase powders was inversely proportional to the alcohol polarity and the amount of the glycerol in the reaction medium. To ensure optimum activity, A. grandidieri and J. mahafalensis powders must be conditioned to a water activity of 0.33 and 0.66. To obtain a fatty acid ethyl ester yield greater than 95% with A. grandidieri, ethanol should be introduced at an amount corresponding to a triacylglycerol to ethanol molar ratio of 2:1 every 15 h for 96 h and use 25% of preconditioned crude lipase powders (2 additions of 12.5%).

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 score0.038
Threshold uncertainty score0.343

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 head0.006
GPT teacher head0.208
Teacher spread0.202 · 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

Citations12
Published2017
Admission routes1
Has abstractyes

Explore more

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