MétaCan
Menu
Back to cohort
Record W2781519505 · doi:10.1002/cjce.23127

Kinetics of the esterification between lactic acid and isoamyl alcohol in the presence of silica gel‐supported sodium hydrogen sulphate

2018· article· en· W2781519505 on OpenAlexvenueno aff
Zhongkai Jiang, Jumei Xu, Zuoxiang Zeng, Weilan Xue, Shating Li

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2018
Typearticle
Languageen
FieldChemistry
TopicSurfactants and Colloidal Systems
Canadian institutionsnot available
Fundersnot available
KeywordsChemistryIsoamyl alcoholCatalysisLactic acidAlcoholEnthalpyAdsorptionUNIFACSilica gelReaction rateInorganic chemistryChromatographyOrganic chemistryThermodynamicsActivity coefficientAqueous solution

Abstract

fetched live from OpenAlex

Abstract The esterification reaction between isoamyl alcohol and lactic acid was studied over silica gel‐supported sodium hydrogen sulphate (NaHSO 4 · Silica). The influences of the external mass transfer, internal mass transfer, reaction temperature, catalyst loading, and initial reactant molar ratio were investigated. Kinetic experimental data of this esterification reaction obtained at different temperatures (343–378 K) were correlated with three models (the pseudo‐homogeneous (PH), Eley‐Rideal (ER), and Langmuir‐Hinshelwood (LH) model). The UNIFAC method was employed to estimate the activity coefficients when the non‐ideal thermodynamic behaviour of the reaction mixture was considered. The results show that the ER model is more appropriate for predicting the dynamic data of an esterification reaction. It was found that water and isoamyl alcohol were more strongly adsorbed onto the surface of NaHSO 4 · Silica than lactic acid and isoamyl lactate, by comparing the adsorption equilibrium constants of different components existing in the reaction. The activation energy and standard enthalpy of the reaction were found to be 54.1 and −6.27 kJ mol −1 , respectively.

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 score0.001
Threshold uncertainty score0.210

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 head0.015
GPT teacher head0.213
Teacher spread0.198 · 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

Citations9
Published2018
Admission routes1
Has abstractyes

Explore more

Same venueThe Canadian Journal of Chemical EngineeringSame topicSurfactants and Colloidal SystemsFrench-language works237,207