Self-organization of hydrophobic-capped triblock copolymers with a polyelectrolyte midblock: a coarse-grained molecular dynamics simulation study
Bibliographic record
Abstract
We present the results of a Langevin dynamics simulation study of micellar organization and hydrogel formation in the solutions of coarse-grained ABA copolymer chains. Polymer chains are modeled as bead-spring chains of Lennard-Jones particles by explicit treatment of ionic species in implicit solvent. The studied copolymer is composed of a polyelectrolyte midblock flanked by two hydrophobic endblocks. We explore the self-assembly of copolymer solutions at a fixed polymer concentration and temperature upon systematic variation of the midblock charge fraction, valency of neutralizing counterions, and the stiffness and length of hydrophobic endblocks. Minimization of the surface energy, conformational entropy of the midblock chains, electrostatic repulsion of midblock charges, and the translational entropy of counterions are found to play central roles in controlling the self-organization features of copolymer solutions. Flower-like micelles with A-blocks forming the core of spherical aggregates and B-blocks constituting the micelle corona are established for the neutral midblocks. Increasing the charge content of B chains lowers the fraction of loop conformations and yields a spanning hydrogel network with midblocks bridging the hydrophobic clusters. Counterion valence is shown to exert a strong effect on the micelle size and network structure. The increase in the rigidity of terminal A-blocks increases the fraction of bridging chains and results in the formation of a hydrogel network with bundle-like hydrophobic domains. Longer endblocks are shown to increase the hydrophobic cluster size and enhance the bridged midblock fraction. The qualitative agreement between the experimental and theoretical studies is also discussed. The comprehensive molecular picture provides a framework for the future studies of stimuli-responsive copolymer systems.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".