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Record W4391815130 · doi:10.1002/cjce.25177

Structural modulation of ionic liquids as efficient catalysts for esterification reaction

2024· article· en· W4391815130 on OpenAlexvenueno aff
Ping Xie, Tianhao Zhong, Tao Li, Yiwu Lu, Yingmin Yu, Qingshan Zhao, Zhongtao Li

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2024
Typearticle
Languageen
FieldChemistry
TopicChemical Synthesis and Reactions
Canadian institutionsnot available
FundersNational Natural Science Foundation of China
KeywordsCatalysisIonic liquidChemistryAcetic acidCorrosionYield (engineering)Activation energyImidazoleTransesterificationInorganic chemistryOrganic chemistryNuclear chemistryMaterials scienceMetallurgy

Abstract

fetched live from OpenAlex

Abstract There is an urgent need to develop green catalytic transesterification technology to reduce liquid waste and instrument corrosion. Ionic liquids with controlled activity, easy recovery, and corrosion resistance have great potential as catalysts for transesterification reactions. In this paper, a series of ionic liquids were prepared using imidazole and ammonium compounds as cations, and sulphuric acid, p ‐toluenesulfonate, and bistrifluoromethanesulfonate anions as anions. In comparison, the ionic liquid made from imidazole with H 2 SO 4 (IMIHS) was found to be the best catalyst for the esterification reaction with high speed, high activity, low corrosion, and high stability, and its catalytic performance was superior to that of sulphuric acid. The optimum reaction conditions were as follows: the molar ratio of ethanol to acetic acid was 1:1, the catalyst amount was 5 wt.%, the reaction temperature was 80°C, and the reaction time was 120 min. The maximum yield of ethyl acetate was 66.2% under the optimum reaction conditions. The catalytic activity was not declined obviously by reusing six times. The activation energy of the reaction is 48.748 kJ/mol.

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 score0.066
Threshold uncertainty score0.238

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 head0.010
GPT teacher head0.218
Teacher spread0.208 · 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

Citations2
Published2024
Admission routes1
Has abstractyes

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