“Exhaustive” Baeyer-Villiger Oxidation of Poly(Methyl Vinyl Ketone) and Its Copolymers
Bibliographic record
Abstract
The development of “exhaustive” (nearly quantitative) post-modification reactions relies heavily on the efficiency of their corresponding small-molecule protocols. However, the direct translation of existing small-molecule protocols into post-polymerization modifications were often troublesome due to undesired side reactions. For example, the development of an “exhaustive” BaeyerVilliger (BV) post-modification using existing small-molecule BV protocols suffered from a lack of reactivity or significant chain scission. Herein, we demonstrate that a careful re-optimization of a small-molecule transformation on a polymer substrate allowed us to overcome such challenges, thereby enabling an “exhaustive” BV post-polymerization modification. Furthermore, a one-pot copolymerization/“exhaustive” BV post-modification procedure was developed to produce copolymers of vinyl acetate (VAc) and more activated monomers (MAMs) in a convenient and scalable manner. This user-friendly methodology provides a general access to synthetically challenging poly(VAc-co-MAM)s including both statistical and narrow-dispersed block copolymers and could greatly facilitate the exploration of applications with such materials.
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 | 0.000 |
| 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".