Metallaphotoredox Decarboxylative Arylation of Natural Amino Acids via an Elusive Mechanistic Pathway
Bibliographic record
Abstract
The merger of photoredox and nickel catalysis for the decarboxylative arylation of carboxylic acids has evolved into an effective strategy to forge C–C bonds from readily available feedstock. Despite its rapid industrial adoption, the mechanism of this dual-catalyzed cross-coupling reaction has remained unclear and under-studied. Here, we propose an alternative mechanism for the photoredox–Ni dual-catalyzed decarboxylative arylation of α-amino acids based on control experiments with Ni II ArBr complexes, cyclic voltammetry (CV), and computational studies. Our mechanistic studies revealed that a Ni 0 –Ni II –Ni I –Ni II –Ni 0 cycle is feasible in the dual-catalyzed C sp 2 –C sp 3 cross-coupling. Distinct from previous mechanism proposals, we show with a series of CV studies and density functional theory (DFT) calculations that a single electron transfer reduction of Ni II ArBr to Ni I Ar by Ir II is thermodynamically favorable. Reductive elimination via a Ni II -species rather than via a Ni III -species is also supported by DFT calculations. Those mechanistic insights allowed for the reaction scope to be extended to encompass α-amino acids bearing pharmacophoric elements, which were previously unexplored coupling partners. α-Amino acids bearing broad functional groups, including heterocycles, successfully underwent decarboxylative arylation with a diverse set of aryl bromides. This strategy represents an advance in photoredox and Ni-catalysis and broadens its industrial applicability as well as mechanistic understanding.
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 | 0.000 | 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 | 0.001 |
| 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".