Eliminating Bimolecular Decomposition to Address Sustainability in Cross-Coupling: Supported Pd–PEPPSI–IPent<sup>Cl</sup>
Bibliographic record
Abstract
Fine-chemical manufacturing, with its dismal E-factors, has been known for decades as being one of the worst contributors to the well-being of the environment. Further, mining practices that pursue precious metals used in catalysis lead to considerable destruction of the environment. Further contributing to this is the necessity for high catalyst loads due to the limited mortality of organometallic complexes in solution. Bimolecular decomposition (BD), in particular, is a significant contributor to this problem. Assisting in the sustainability of chemical synthesis is flow chemistry, whose “just-in-time” nature produces chemicals as needed, eliminating vast stockpiles of chemicals associated with batch manufacturing. In this work, Pd–PEPPSI–IPent Cl, a high-reactivity, high-selectivity Pd catalyst, has been mounted onto the surface of silica, of which the spacing has eliminated BD. This material has been loaded into packed beds and used in Negishi coupling and Buchwald–Hartwig amination, where the active catalyst has shown tremendous resiliency while producing valuable small-molecule products with deft selectivity and speed with residence time in the order of minutes under mild conditions (e.g., Negishi couplings conducted at room temperature).
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".