Block copolymer self-assembly controlled by the “green” gas stimulus of carbon dioxide
Bibliographic record
Abstract
Stimuli-responsive macromolecules have inspired much interest in polymer science. Inputting an external stimulus to these polymers can modulate their chain structures and self-assembled architectures for functional outputs. This appealing feature has made this class of polymer materials promising for many emerging applications. In order to apply these polymer systems in organisms and further make them adaptive to physiological environments, it is important to explore new stimulation modes. In this Feature Article, we review the recent development of using carbon dioxide (CO2) as a stimulus for tuning or controlling block copolymer (BCP) self-assembly. We show that a series of CO2-responsive functionalities can easily be incorporated into BCP structures, and that rationally designed BCPs can have their self-assembled structures undergo drastic changes in size, shape, morphology and function, controlled by the amount of CO2 in aqueous solution. This gas stimulus has some distinct advantages over other conventional stimuli: it is truly "green" for the environment of the target polymer system without any chemical contaminations; the stimulating strength or magnitude can be precisely adjusted with the continuous gas flow; and, being a key metabolite in cells, it provides a convenient physiological signal to allow synthetic polymer systems to mimic certain properties of organelles and act as intelligent macromolecular machines and devices.
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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".