2299 Organic cyclic carbonatescyclic carbonates synthesis under mild conditions
Bibliographic record
Abstract
Organic cyclic carbonates may be produced by reacting an epoxide with carbon dioxide over a suitable catalytic system. Much attention is currently focused on these materials as they may play a strategic role in carbon capture and utilization processes. They may be seen as key components in the production of large-scale green polymers, including polycarbonates and polyurethanes. In this chapter, we placed ourselves in the position of an engineering staff in charge of designing a commercial process for large-scale synthesis of cyclic carbonates, whose first task is the selection of a proper catalyst. This is obviously a crucial step as this choice will determine the working conditions and performance of a chemical reactor and therefore profitabity. This text shows how to navigate through the jungle of literature dealing with the development of catalysts for this reaction. Starting from the specicifications of the gaseous CO 2 feed produced by novel enzymatic absorber designed by CO2 Solutions Inc., catalyst selection criteria were first established. An exhaustive analysis of more than 600 references reported in the Supporting Information of this chapter allowed finding the 25 catalysts that meet these criteria and published between January 2017 and October 2019. Also, some discussion of industrial research still required to facilitate process design for this reaction is proposed.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.005 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".