Overcoming a Tight Coil To Give a Random “Co” Polymer Derived from a Mixed Sandwich Cobaltocene
Bibliographic record
Abstract
Reversible addition–fragmentation transfer (RAFT) polymerization of a η 5 -cyclopentadienylcobalt-η 4 -cyclobutadiene (CpCoCb) containing monomer under a wide variety of experimental conditions (e.g., different solvents, temperatures, RAFT agents, concentrations, and [RAFT agent]/[initiator]) was examined. In all cases the results revealed that although the monomer was being consumed over the course of the reaction, there was no significant increase in the molecular weight of the resulting polymer. It was determined that as the polymer chain grows (DP ≈ 10), a tight coil morphology was adopted, which hinders the approach of an additional, sterically demanding CpCoCb-containing monomer. This resulted in premature termination/chain transfer reactions rather than an increase in the polymer chain length. To address this problem, methyl acrylate (MA) with its lower steric demand was copolymerized with the bulky CpCoCb-containing monomer to act as a spacer. This provided the necessary steric relief and an opportunity for the metallopolymer to grow. This copolymerization resulted in dramatic improvements in the polydispersity and molecular weight of the end material. In subsequent experiments, the random copolymer was used as a macro-RAFT agent to prepare diblock copolymers, with good control over the molecular weight, allowing for an examination of the self-assembly behavior of the block copolymer in the solid state.
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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 | 0.000 |
| 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".