Self-assembly of gradient copolymers synthesized in semi-batch mode by nitroxide mediated polymerization
Bibliographic record
Abstract
The effect of diffuse compositional interfaces on copolymer self-assembly was studied via gradient copolymers (GCP). Poly(methyl methacrylate)-grad-(styrene) (PMMA-grad-PSt) copolymers were synthesized in semi-batch mode using nitroxide-mediated polymerization (NMP) with varied monomer injection protocols to produce varied diffuse interfaces (number average molecular weights (Mn) ranged from 62 000 g mol−1 to 94 000 g mol−1 with dispersities (Đ) between 1.35 and 1.59). The GCPs were spun into thin films on substrates made neutral by (St-ran-MMA-ran-hydroxyethyl methacrylate) terpolymers and annealed at elevated temperature to produce vertically oriented microphase-separated domains. The GCPs were found to have domain spacing larger than equivalent monodisperse BCPs, due to their polydisperse nature. This effect was partially offset by the decrease in χ due to the gradient. GCPs synthesized with a single-injection protocol (i.e. less diffuse interfaces) were found to self-assemble into ordered domains. However, GCPs synthesized with long injection times (i.e. more diffuse interfaces) exhibited poor self-assembly attributed to their predicted statistical-copolymer-like middle sequence, which caused a reduction of the effective enthalpic interaction parameter.
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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 | 0.000 |
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".