Development of the “phase separation” strategy: addressing dilution effects in macrocyclization
Bibliographic record
Abstract
The “phase separation” strategy exploits aggregated solvent mixtures of poly(ethylene)glycol (PEG) solvents and hydrophilic organic solvents to control the effective molarity of substrates and catalysts in macrocyclization reactions. The slow diffusion of acyclic precursors from the PEG aggregate “phase” to the organic solvent/catalyst “phase” mimics the slow addition processes typically employed in the synthesis of macrocycles. With the aid of mechanistic studies, the strategy has been applied to copper-catalyzed Glaser–Hay couplings, copper-catalyzed azide–alkyne cycloadditions, copper-catalyzed azide-iodoalkyne cycloadditions, and ruthenium-catalyzed olefin metathesis processes. “Phase separation” has been used to form macrocycles of interest in aromachemicals, lipid-based macrocycles, bioactive natural products, macrocyclic peptides, and bioactive pharmaceuticals. The “phase separation” strategy has been proven to promote transition metal-catalyzed macrocyclization processes with increases in concentration up to three orders of magnitude larger than traditional methods like high dilution or pseudo high dilution/slow addition.
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 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".