Smart Responsive Polymers: Fundamentals and Design Principles
Bibliographic record
Abstract
In this review, we summarize recent theoretical and computational developments in the field of smart responsive materials, together with complementary experimental data. A material is referred to as smart responsive when a slight change in external stimulus can drastically alter its structure, function, or stability. Because of this smart responsiveness, these systems are used for the design of advanced functional materials. The most characteristic properties of smart polymers are discussed, especially polymer properties in solvent mixtures. We show how multiscale simulation approaches can shed light on the intriguing experimental observations. Special emphasis is given to two symmetric phenomena: co-non-solvency and co-solvency. The first phenomenon is associated with the collapse of polymers in two miscible good solvents, whereas the latter is associated with the swelling of polymers in poor solvent mixtures. Furthermore, we discuss when the standard Flory–Huggins-type mean-field polymer theory can (or cannot) be applied to understand these complex solution properties. We also sketch a few examples to highlight possible future directions, that is, how smart polymer properties can be used for the design principles of advanced functional materials.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".