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Record W2538006677 · doi:10.3934/energy.2016.6.817

Enzymatic transesterification of lipids from microalgae into biodiesel: a review

2016· review· en· W2538006677 on OpenAlexaff
Maria del Pilar Rodriguez, Ryszard Brzeziński, Nathalie Faucheux, Michèle Heitz

Bibliographic record

VenueAIMS energy · 2016
Typereview
Languageen
FieldEngineering
TopicBiodiesel Production and Applications
Canadian institutionsUniversité de Sherbrooke
Fundersnot available
KeywordsBiodieselTransesterificationBiodiesel productionPulp and paper industryDiesel fuelBiofuelBiodegradationChemistryEnvironmental scienceBiochemical engineeringBiotechnologyWaste managementOrganic chemistryCatalysisEngineeringBiology

Abstract

fetched live from OpenAlex

Biodiesel is considered as a potential interesting alternative diesel substitute due to its many advantages, such as biodegradability and non-toxicity. Biodiesel production worldwide is low and does not meet the needs of society. Therefore, biodiesel production policies have been implemented by various countries. This paper addresses the issues of plant materials containing lipids for biodiesel production and explores also the avenue of microalgae as an alternative. Various conversion technologies of lipids into biodiesel are also described and compared. Enzymatic transesterification would be most appropriate when microalgae oils are used because of their high free fatty acids content. The optimization of the enzymatic process depends on several parameters such as the catalyst, temperature, reaction time and the alcohol: oil molar ratio.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.960
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.025
GPT teacher head0.272
Teacher spread0.247 · 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.

Study designNot applicable
Domainnot available
GenreReview

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

Citations26
Published2016
Admission routes1
Has abstractyes

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