Maximizing Driving Force in the Design of N-oxyl Hydrogen Atom Transfer Catalysts
Bibliographic record
Abstract
Increasing the bond dissociation enthalpy (BDE) of potential hydrogen atom transfer (HAT) catalysts has the potential to un-lock a greater substrate scope for radical C-H functionalization reactions. For the archetype N-oxyl catalyst phthalimide-N-oxyl (PINO), tuning the BDEO-H of its precursor N-hydroxyphthalimide (NHPI) by substitution of the the aryl ring has minimal effects, limiting meaningful advances in catalyst development by modifications of PINO. Herein, we demonstrate that inserting a heteroatom between one of the carbonyl groups of PINO and the aryl ring significantly increases the BDEO-H. For example, an N-phenyl moiety, O-atom or S-atom raises the BDEO-H by 6.5, 6.9 and 8.1 kcal/mol, respectively, relative to NHPI – which translates to an increased kHAT of 4, 36.3 and 24.3, respectively. Our studies of these compounds and a panel of analogs thereof highlight three advantages of this strategy: 1) high synthetic accessibility of catalyst candidates; 2) simultaneous optimization across multiple parameters; and 3) effective activity tuning. These new scaffolds are promising for the development of next-generation HAT catalysts and C-H functionalization reactions.
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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 | 0.001 |
| 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".