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Record W4400666163 · doi:10.1002/cjce.25414

Transesterification reactions as a means to produce esters: A critical review

2024· review· en· W4400666163 on OpenAlexvenueno aff
Reshma R. Devale, Yogesh S. Mahajan

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2024
Typereview
Languageen
FieldEngineering
TopicBiodiesel Production and Applications
Canadian institutionsnot available
Fundersnot available
KeywordsTransesterificationOrganic chemistryCatalysisBiodiesel productionBiodieselChemistryRaw materialIon-exchange resinReusabilityMaterials scienceComputer science

Abstract

fetched live from OpenAlex

Abstract Esters are important chemicals used in fine and bulk chemical industry with numerous applications: solvents, paints, varnishes, dyes, and cosmetics. Ester formation is dominated mainly by Fischer esterification and transesterification. Fischer reaction is generally used for ester production, but in certain cases, transesterification can be used with advantage. It is useful when the acid is less soluble in the alcohol or in the solvent used, thus forming two layers. Water formation creates purification problems during esterification reactions due to azeotrope formation and transesterification can be useful in such cases. Commercially, cheaply available methyl and ethyl esters can be conveniently used as raw materials for value added ester production by transesterification. Transesterification is also useful when the parent acids are highly reactive and pose difficulty in separation. Transesterifications are slow reactions and a catalyst is used: acids and bases, ion exchange resins, zeolites, and clays. Homogeneous catalysts were used in the past which are now replaced by their heterogeneous counterparts. Heterogeneous catalysts offer added advantages like reusability, lesser corrosion, and ease of separation. Transesterification can be commercially used to produce a number of esters of industrial importance like acrylics and biodiesel. This review considers all these aspects in considerable detail. A large literature set was scanned and its judicious extract is presented.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.873
Threshold uncertainty score0.880

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
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 head0.052
GPT teacher head0.300
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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations11
Published2024
Admission routes1
Has abstractyes

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