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Record W2153200550 · doi:10.1002/aic.14786

Thermodynamic and kinetic studies on alkoxylation of camphene over cation exchange resin catalysts

2015· article· en· W2153200550 on OpenAlexaff
Huiqin Nie, Ruyin Xu, Feng Zhang, Zheng Zhou, Zhibing Zhang, Gaodong Yang

Bibliographic record

VenueAIChE Journal · 2015
Typearticle
Languageen
FieldChemical Engineering
TopicCatalysis and Oxidation Reactions
Canadian institutionsMinistry of Education and Child Care
FundersNatural Science Foundation of Jiangsu ProvinceNational Natural Science Foundation of China
KeywordsUNIFACChemistryCatalysisCampheneThermodynamicsEnthalpyEquilibrium constantSolventAnhydrousActivation energyReaction rate constantPhysical chemistryGibbs free energyOrganic chemistryKineticsActivity coefficientChromatography

Abstract

fetched live from OpenAlex

The alkoxylation of camphene with 2‐methyl‐1,3‐propanediol was studied using anhydrous macroporous and strong acid cation exchange resins as catalysts. The effects of various parameters, such as catalyst type, solvent, molar ratio of reactants, reaction temperature, and reusability of catalysts, were investigated in a 250 mL stirred tank reactor to optimize the reaction conditions. The UNIFAC group contribution method was used to correct liquid nonideality, giving the thermodynamic equilibrium constant at 333–370 K. The enthalpy changes calculated by three different methods (Gaussian 03, constant, and a function of temperature) were compared. The value (−74.6 ± 3.3 kJ/mol) calculated by the last method was closer to the theoretical value (−75.73 kJ/mol) than that given by the second method (−30.2 ±1.2 kJ/mol). A Langmuir–Hinshelwood–Hougen–Watson model based on activity was used to fit experimental data and the activation energy was 29.14 kJ/mol. The optimized reaction conditions were also verified in a 5 L reaction kettle. © 2015 American Institute of Chemical Engineers AIChE J , 61: 1925–1932, 2015

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.003
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0010.000

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 head0.040
GPT teacher head0.299
Teacher spread0.259 · 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 source (direct Gemma or distilled Codex), 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

Citations8
Published2015
Admission routes1
Has abstractyes

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