Surface-Grafted Stimuli-Responsive Block Copolymer Brushes for the Thermo-, Photo- and pH-Sensitive Release of Dye Molecules
Bibliographic record
Abstract
We present a general approach for using surface-grafted stimuli-responsive diblock copolymer brushes as stimuli-sensitive and controllable release systems. Surface-initiated ATRP was used to grow sequentially a first block serving as an inner reservoir for loading and a second block that acts as a stimuli-responsive outer layer controlling the closure or opening of the brush in water. We show that the release kinetics of loaded model dyes (hydrophobic or hydrophilic) could be controlled by the second block switchable between the collapsed and extended brush chain states in response to temperature or pH change or exposure to light. On the one hand, diblock copolymer brushes of polystyrene- b -poly( N -isopropylacrylamide) (PS- b -PNIPAM) and poly( N,N′ -dimethylacrylamide)- b -poly( N -isopropylacrylamide) (PDMA- b -PNIPAM) were synthesized to demonstrate the thermosensitive release of dyes based on the LCST-determined solubility switching between swollen and collapsed PNIPAM chains. On the other hand, a diblock copolymer brush of polystyrene- b -poly(4,5-dimethoxy-2-nitrobenzyl methacrylate) (PS- b -PNBA) was designed to investigate the possibility of tuning the dye release kinetics with light. The photocontrol was achieved by controlling the photocleavage degree of photolabile o -nitrobenzyl groups, which determines the number of hydrophilic methacrylic acid (MA) groups in the outer layer. Moreover, complete photocleavage of o -nitrobenzyl groups converted the photosensitive PS- b -PNBA brush into a pH-sensitive PS- b -PMA brush with which pH-dependent dye release was observed due to the water solubility switching of PMA chains with protonated or ionized carboxylic acid groups. The interest, the versatility, and the generality of the approach were demonstrated in this study with three different stimuli, namely, temperature, pH, and light.
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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 | 0.001 |
| 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".