Macrocycles from the Photochemical Coupling of Preassociated Terminal Blocks of Block Copolymers
Bibliographic record
Abstract
Cyclization of long polymer chains is difficult because polymer chain ends have a low probability to contact one another and intramolecular cyclization has to compete with interchain coupling. To minimize intermolecular coupling, macrocycles are prepared traditionally under high dilution conditions, which limit the amount of polymer obtainable per volume of solvent. Reported in this paper is a new methodology for synthesizing polymer macrocycles. Instead of using polymers with one pair of reactive groups at the ends of a polymer chain, we use a block copolymer, poly[(2-cinnamoyloxyethyl methacrylate)- ran -(2-trifluoroacetoxyethyl methacrylate)]- block -poly(solketal methacrylate)- block -poly( tert -butyl acrylate)- block -poly(solketal methacrylate)- block -poly[(2-cinnamoyloxyethyl methacrylate)- ran -(2-trifluoroacetoxyethyl methacrylate)] or P(CEMA- r -TFAEMA)- b -PSMA- b -PtBA- b -PSMA- b -P(CEMA- r -TFAEMA), with reactive P(CEMA- r -TFAEMA) end blocks to increase the efficiency of end coupling. In our method, a micellar solution is first prepared in a solvent selectively poor for the end CEMA units. This micellar solution is then slowly pumped into a solvent reservoir or reactor under constant stirring and irradiation. In the reactor, where the polymer concentration remains low throughout the preparation for its conversion into macrocycles, the micelles dissociate quickly into end-associated rings or unimolecular micelles, and the rings then get covalently linked photochemically. Since the TFAEMA units in the end blocks are soluble in the solvent used and probably segregate preferentially on the surface of the “balls” formed from the aggregation of the end CEMA units, they help deter the chemical coupling of different macrocycles. Using this methodology, we can prepare large macrocycles in high purity and at high concentrations.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".