Amination with PdNHC Complexes: Rate and Computational Studies Involving Substituted Aniline Substrates
Bibliographic record
Abstract
The amination of aryl chlorides with various aniline derivatives using the N-heterocyclic carbene-based Pd complexes Pd-PEPPSI-IPr and Pd-PEPPSI-IPent (PEPPSI=pyridine, enhanced precatalyst, preparation, stabilization, and initiation; IPr=diisopropylphenylimidazolium derivative; IPent= diisopentylphenylimidazolium derivative) has been studied. Rate studies have shown a reliance on the aryl chloride to be electron poor, although oxidative addition is not rate limiting. Anilines couple best when they are electron rich, which would seem to discount deprotonation of the intermediate metal ammonium complex as being rate limiting in favour of reductive elimination. In previous studies with secondary amines using PEPPSI complexes, deprotonation was proposed to be the slow step in the cycle. These experimental findings relating to mechanism were corroborated by computation. Pd-PEPPSI-IPr and the more hindered Pd-PEPPSI-IPent catalysts were used to couple deactivated aryl chlorides with electron poor anilines; while the IPr catalysis was sluggish, the IPent catalyst performed extremely well, again showing the high reactivity of this broadly useful catalyst.
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 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) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".