Studying the significance of the parameters involved in the synthesis of Y-UiO-66 to improve product yield
Bibliographic record
Abstract
The significance of the parameters involved in the synthesis of rare-earth metal–organic frameworks (RE-MOFs) has not been studied in depth. Design of experiments (DoE) is used herein to determine the impact of seven synthetic parameters on the yield and surface area of Y-UiO-66 (UiO = University of Oslo). The seven synthetic parameters are evaluated through 16 experiments, leading to a new synthetic procedure for Y-UiO-66 that increases the yield from 30 % to 84 % while maintaining high crystallinity and surface area. These results show that a simple screening design, using DoE, is useful for the improvement of MOF synthesis. The new procedure is successfully adapted to scale-up the reaction twenty times, as well as to improve the yield of Ho- and Yb-UiO-66, demonstrating that it can be extended to lanthanoid analogues of UiO-66. The results obtained from the design were used to improve the yield of Tb-CU-27 (CU = Concordia University) with only one experiment, suggesting that the synthetic parameters identified as being significant for Y-UiO-66 have a similar effect on other RE cluster-based MOFs.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".