Formal Cross-Coupling of Diaryl Ethers with Ammonia by Dual C(Ar)–O Bond Cleavages
Bibliographic record
Abstract
The conversion of renewable resources and inexpensive inorganic chemicals directly into higher value-added organic chemicals is becoming more and more important for our society’s future sustainable development. Lignin, being the second most abundant organic carbon renewable resource on Earth, has been treated as waste in the pulp and paper industry. The 4-O-5 linkage diaryl ether bond is the strongest among the three types of ether linkages in lignins. Selective cleavage of this linkage can potentially generate smaller processable bio-based aromatic polymeric materials and compounds. Furthermore, there has been a long synthetic interest in coupling reactions with aryl ethers via C(Ar)–O bond cleavage, for example, for polyphenylene oxide (PPO) waste recycling. On the other hand, ammonia is a very inexpensive industrial inorganic chemical. Herein, we report a direct conversion of diaryl ethers and ammonia into aniline derivatives and arenes, providing a model for selective lignin 4-O-5 linkage modification and PPO recycling with inexpensive ammonia. Both symmetrical and unsymmetrical diaryl ethers were successfully cross-coupled with ammonia via dual C(Ar)–O bond cleavages, generating the corresponding cyclohexylanilines and arenes.
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) | 0.001 | 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 | 0.001 |
| 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".