Dual stimuli-responsive coating designed through layer-by-layer assembly of PAA-b-PNIPAM block copolymers for the control of protein adsorption
Bibliographic record
Abstract
In this paper, we describe the successful construction, characteristics and interaction with proteins of stimuli-responsive thin nanostructured films prepared by layer-by-layer (LbL) sequential assembly of PNIPAM-containing polyelectrolytes and PAH. PAA-b-PNIPAM block copolymers were synthesized in order to benefit from (i) the ionizable properties of PAA, to be involved in the LbL assembly, and (ii) the sensitivity of PNIPAM to temperature stimulus. The impact of parameters related to the structure and size of the macromolecules (their molecular weight and the relative degree of polymerization of PAA and PNIPAM), and the interaction with proteins under physico-chemical stimuli, such as pH and temperature, are carefully investigated. The incorporation of PAA-b-PNIPAM into multilayered films is shown to be successful whatever the block copolymer used, resulting in slightly thicker films than the corresponding (PAA/PAH)n film. Importantly, the protein adsorption studies demonstrate that it is possible to alter the adsorption behavior of proteins on (PAA-b-PNIPAM/PAH)n surfaces by varying the temperature and/or the pH of the medium, which seems to be intimately related to two key factors: (i) the ability of PNIPAM units to undergo conformational changes and (ii) the structural changes of the film made of weak polyelectrolytes. The simplicity of construction of these PNIPAM block copolymer-based LbL coatings on a large range of substrates, combined with their highly tunable features, make them ideal candidates to be employed for various biomedical applications requiring the control of protein adsorption.
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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".