Ce cooperated layered double oxide with enhanced base sites activity for the synthesis of polycarbonate diols
Bibliographic record
Abstract
Abstract The development of highly efficient alkaline catalysts with abundant base sites is of paramount importance for the synthesis of polycarbonate diols (PCDLs). And the application of heterogeneous catalysts is an effective strategy to address the effect of residual catalysts on the quality of PCDLs. Here, Ce cooperated layered double oxide (LDO‐Ce) was used as a catalyst for the preparation of PCDLs via transesterification between dimethyl carbonate (DMC) and 1,4‐butanediol (BDO). CO2 temperature‐programmed desorption (CO2‐TPD) profiles demonstrated that the introduction of Ce led to an increase in strong base sites of LDO‐Ce, thus endowing LDO‐Ce with excellent catalytic performance. Besides, LDO‐Ce possessed satisfactory specific surface area and pore size. A possible catalytic mechanism was proposed to illustrate the transesterification process. The effects of the reaction conditions on the hydroxyl value, yield, and BDO conversion were further investigated in detail. The yield of PCDLs with a hydroxyl value of 112.2 mg KOH/g (corresponding to a number average molecular weight [Mn] of 1000 g/mol) was 92.44% under its optimum reaction conditions (w (catalyst) = 0.5%, n(DMC)/n(BDO) = 1.25, T‐transesterification = 130°C, t‐transesterification = 5 h, T‐polycondensation = 170°C, t‐polycondensation = 4 h, P‐polycondensation = 10 kPa). Moreover, LDO‐Ce was easily removed after the transesterification process (Step 1), ensuring the quality of PCDLs, and it was recycled three times without significant loss of catalytic activity.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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".