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Record W4297198886 · doi:10.1002/cjce.24680

Preparation of propylene carbonate by alcoholysis of urea from the perspective of homogeneous reaction: Influence of dissolution and precipitation of metal oxide catalysts

2022· article· en· W4297198886 on OpenAlexvenueno aff
Fei Yang, Weihua Shen, Yunjin Fang

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2022
Typearticle
Languageen
FieldChemical Engineering
TopicCarbon dioxide utilization in catalysis
Canadian institutionsnot available
FundersNational Natural Science Foundation of China
KeywordsCatalysisDissolutionInorganic chemistryChemistryUreaPropylene carbonatePropylene oxideAmmoniaOxideDecompositionIsocyanateCarbonatePrecipitationMetalFourier transform infrared spectroscopyYield (engineering)Organic chemistryMaterials scienceChemical engineeringPhysical chemistryCopolymerPolymer

Abstract

fetched live from OpenAlex

Abstract The dissolution process of metal oxide catalysts in the alcoholysis of urea by 1, 2‐propylene glycol (PG) to the synthesis of propylene carbonate (PC) was investigated. It was found that the reaction was mainly catalyzed by homogeneous catalysts. The metal oxide reacted with ammonia and isocyanate acid, the decomposition products of urea, to form a complex and dissolved into the reaction system. The dissolved amount of catalyst was positively correlated with the PC yield by using different oxides and salts as catalysts. The Fourier‐transform infrared spectroscopy (FTIR) result of the catalysts reacting with urea showed that the dissolution‐precipitation process of the catalysts was related to the decomposition of urea. The results of catalytic test indicated that both the dissolved amount of catalysts and the structure of the complex affected PC yields. A reaction mechanism was proposed based on the results. From the mechanism, a complex catalyst with at least two ammonia ligands is better for this reaction.

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)0.001
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 score0.225
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

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)0.0000.000

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 head0.006
GPT teacher head0.215
Teacher spread0.209 · 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
Published2022
Admission routes1
Has abstractyes

Explore more

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