Physico-Chemical Investigation of Multiple Asymmetric Amphiphilic Diblock Copolymer Morphologies in Solution
Bibliographic record
Abstract
Asymmetric amphiphilic diblock copolymers are able to form self-assembled crewcut aggregates of multiple morphologies in selective solvents. Micelles are formed by the slow addition of a poor solvent, for the core-forming block, to a solution of the copolymer dissolved in a solvent suitable for both polymer segments. The morphology of the aggregates is affected by a number of factors, such as the solvent composition, the block lengths, the presence of additives, and the temperature. Some of the resulting structures are considered to be equilibrium or near-equilibrium morphologies, which can be kinetically frozen using a number of different techniques. The thermodynamics of micelle formation has been investigated, and equations for the standard Gibbs free energy, enthalpy, and entropy have been formulated. Partial phase diagrams have been constructed, giving the morphological phase boundaries. Knowledge of the location of the boundaries has paved the way for the study of the kinetics and mechanisms of the sphere to rod and rod to vesicle transitions. Both processes have two-step mechanisms, with the second step being rate determining in each case.
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 | 0.000 |
| 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".