Tailoring Rheological Properties of Thermoresponsive Hydrogels through Block Copolymer Adsorption to Cellulose Nanocrystals
Bibliographic record
Abstract
This study investigates the adsorption of a block copolymer composed of a poly(2-(dimethylamino)ethyl methacrylate) (PDMAEMA) cationic polyelectrolyte and a poly(di(ethylene glycol) methyl ethermethacrylate) (PDEGMA) on oxidized cellulose nanocrystals (TO-CNCs) to produce hydrogels. PDMEAMA- b-PDEGMA was synthesized by atom-transfer radical polymerization. The extent and dynamics of the adsorption of PDMAEMA- b-PDEGMA on TO-CNCs were determined by electromechanical microbalance and optical techniques. Electrostatic adsorption was identified on TO-CNCs with the quaternized block copolymer. Small-angle neutron scattering experiments were performed to investigate the polymer behavior on the TO-CNC surfaces. Depending on the temperature, block copolymer induces the aggregation of nanocrystals after adsorption by connecting CNCs bundles with block copolymer chains. A reversible liquid-to-gel transition, triggered by temperature, was clearly detected by rheological measurements for the copolymer-CNC mixtures. At the optimal copolymer to CNC ratio the viscosity increased by 4 orders of magnitude at low shear rates. These stimuli-responsive CNC-based materials could be used as injectable biomedical 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".