What Limits the Chain Growth from Flat Surfaces in Surface‐Initiated ATRP: Propagation, Termination or Both?
Bibliographic record
Abstract
Surface‐initiated controlled radical polymerization, such as ATRP, has been proven to be a powerful method for preparation of well‐controlled functional grafted polymers. There are thousands of experimental works published, by varying polymers, surfaces, and applications. In comparison, theoretical developments are very lacking. Many fundamental questions still remain to be answered. These questions include, but not limited to: What determine the surface initiator efficiency? How many chains per square nanometer can be fully grown? What is the maximum thickness can polymer grow to? If chains are simultaneously grown from surface and solution, which has higher molecular weight? Answers to these questions are most helpful for innovation and further development in this important area. In this work, we employed recently developed theories to explain experimentally observed kinetic profiles of polymer layer thickness growth. What limits the growth of surface chains? Is it the monomer starvation in grafting layer that slow down propagation? Or is it the termination of radicals that stop the chain growth? It was found that no existing models could fully explain the often contradictory experimental observations. It is our hope that this work will provoke further discussions and inspire more effort in resolving the fundamental issues of this area.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 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".