Structure–function relationships in aryl diazirines reveal optimal design features to maximize C–H insertion
Bibliographic record
Abstract
Diazirine reagents allow for the ready generation of carbenes upon photochemical, thermal, or electrical stimulation. Because carbenes formed in this way can undergo rapid insertion into any nearby C-H, O-H or N-H bond, molecules that encode diazirine functions have emerged as privileged tools in applications ranging from biological target identification and proteomics through to polymer crosslinking and adhesion. Here we use a combination of experimental and computational methods to complete the first comprehensive survey of diazirine structure-function relationships, with a particular focus on thermal activation methods. We reveal a striking ability to vary the activation energy and activation temperature of aryl diazirines through the rational manipulation of electronic properties. Significantly, we show that electron-rich diazirines have greatly enhanced efficacy toward C-H insertion, under both thermal and photochemical activation conditions. We expect these results to lead to significant improvements in diazirine-based chemical probes and polymer crosslinkers.
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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) | 0.002 | 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".