Polymer-Grafted Cellulose Nanocrystals as pH-Responsive Reversible Flocculants
Bibliographic record
Abstract
Cellulose nanocrystals (CNCs) are a sustainable nanomaterial with applications spanning composites, coatings, gels, and foams. Surface modification routes to optimize CNC interfacial compatibility and functionality are required to exploit the full potential of this material in the design of new products. In this work, CNCs have been rendered pH-responsive by surface-initiated graft polymerization of 4-vinylpyridine with the initiator ceric(IV) ammonium nitrate. The polymerization is a one-pot, water-based synthesis carried out under sonication, which ensures even dispersion of the cellulose nanocrystals during the reaction. The resultant suspensions of poly(4-vinylpyridine)-grafted cellulose nanocrystals (P4VP-g-CNCs) show reversible flocculation and sedimentation with changes in pH; the loss of colloidal stability is visible by eye even at concentrations as low as 0.004 wt %. The presence of grafted polymer and the ability to tune the hydrophilic/hydrophobic properties of P4VP-g-CNCs were characterized by Fourier transform infrared spectroscopy, elemental analysis, electrophoretic mobility, mass spectrometry, transmittance spectroscopy, contact-angle measurements, thermal analysis, and various microscopies. Atomic force microscopy showed no observable changes in the CNC dimensions or degree of aggregation after polymer grafting, and a liquid crystalline nematic phase of the modified CNCs was detected by polarized light microscopy. Controlled stability and wettability of P4VP-g-CNCs is advantageous both in composite design, where cellulose nanocrystals generally have limited dispersibility in nonpolar matrices, and as biodegradable flocculants. The responsive nature of these novel nanoparticles may offer new applications for CNCs in biomedical devices, as clarifying agents, and in industrial separation processes.
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".