Spinodal decomposition of chemically fueled polymer solutions
Bibliographic record
Abstract
Out-of-equilibrium phase transitions driven by dissipation of chemical energy are a common mechanism for morphological organization and temporal programming in biology. Inspired by this, dissipative self-assembly utilizes chemical reaction networks (CRNs) that consume high-energy molecules (chemical fuels) to generate transient structures and functionality. While a wide range of chemical fuels and building blocks are now available for chemically fueled systems, so far little attention has been paid to the phase-separation process itself. Herein, we investigate the chemically fueled spinodal decomposition of poly(norbornene dicarboxylic acid) (PNDAc) solution, which is driven by a cyclic chemical reaction network. Our analysis encompasses both the molecular level in terms of the CRN, but also the phase separation process. We investigate the morphology of formed domains, as well as the kinetics and mechanism of domain growth, and develop a kinetic/thermodynamic hybrid model to not only rationalize the dependence of the system on fuel concentration and pH, but also open pathways towards predictive design of future fueled polymer systems.
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".