Tailoring cellulose nanocrystals rheological behavior in aqueous suspensions through surface functionalization with polyethyleneimine
Bibliographic record
Abstract
This paper reports the surface modification of commercially available cellulose nanocrystals (CNCs) using polyethyleneimine (PEI) by means of non-covalent electrostatic interaction between the negatively charged sulfate groups of CNCs and positively charged amine functionalities of PEI. The modification, carried out in an aqueous medium, results in a stable CNC-PEI suspension with no phase separation that exhibits interesting rheological behavior due to bridging-type inter-particle interactions. The Newtonian 3% (w/w) CNC suspension evolves into a non-Newtonian gel system after modification with PEI with a consequent increase of almost three decades in complex viscosity. Pre-shearing of the 3% (w/w) CNC-PEI suspension resulted in the loss of the linear viscoelastic properties with increasing shear rate, as would be expected from the breaking of the inter-particle network. However, the system gradually re-established the inter-particle network in less than an hour to give the original rheological parameters. The effect of PEI on the rheological properties was attributed to the physical adsorption of PEI chains on the CNC particles, examined by dynamic light scattering, zeta potential, X-ray photoelectron spectroscopy, elemental analyses, and isothermal adsorption studies. The modified CNC-PEI particles did not show any significant change in the particle morphology compared to the unmodified CNCs, as observed from transmission electron microscope images.
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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) | 0.000 | 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".