Controlling the structure of star-like copolymer microgels through the monomer chain length to modulate softness and deswelling
Bibliographic record
Abstract
HYPOTHESIS: Softness and stimulus responsivity of deformable polymeric particles are at the core of their properties and behaviors in suspension or when interacting with interfaces. Star-like poly(N-isopropylacrylamide) (PNIPAM) microgels crosslinked with ethylene glycol dimethacrylate (EGDMA) are novel soft colloids which present a density profile characterized by a densely crosslinked core surrounded by a fuzzy shell with star-like arms. We propose copolymerization with oligo(ethylene-glycol) methyl ether methacrylates (OEGMA) of varying chain lengths as a tool to finely tune particle softness and thermal response, and thus their phase behavior in dense states. EXPERIMENTS: By integrating light scattering with a recently introduced method that combines neutron scattering experiments and variation of the suspension osmotic pressure, we linked osmotic compression and particle bulk modulus to temperature deswelling, elucidating the complex interplay between network conformation and mechanical properties in response to different stimuli. FINDINGS: We demonstrate that the incorporation of OEGMA chains alters the internal polymer distribution in a complex way that depends on the chain length: short OEGMA chains mitigate the internal inhomogeneities between core and shell of PNIPAM-EGDMA microgels, resulting in particles that are overall softer. For longer OEGMA the morphology changes to an even more compliant star-like structure that does not collapse into a compact sphere even at high temperature. These results show how the internal microstructure and physico-chemical properties of soft colloids can be modulated in great details in copolymer networks, in light of applications exploiting their softness for the fabrication of functional materials.
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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".