Fluorescent Labeling and Characterization of Cellulose Nanocrystals with Varying Charge Contents
Bibliographic record
Abstract
Cotton-source cellulose nanocrystals (CNCs) with a range of surface charge densities were fluorescently labeled with 5-(4, 6-dichlorotriazinyl) aminofluorescein (DTAF) in a facile, one-pot reaction under alkaline conditions. Three CNC samples were labeled: (I) anionic CNCs prepared by sulfuric acid hydrolysis with a sulfur content of 0.47 wt %, (II) a partially desulfated, sulfuric acid-hydrolyzed CNC sample, which was less anionic with an intermediate sulfur content of 0.21 wt %, and (III) uncharged CNCs prepared by HCl hydrolysis. The DTAF-labeled CNCs were characterized by dynamic light scattering, atomic force microscopy, fluorescence spectroscopy and microscopy, and polarized light microscopy. Fluorescent CNCs exhibited similar colloidal stability to the starting CNCs, with the exception of the HCl-hydrolyzed sample, which became less agglomerated after the labeling reaction. The degree of labeling depended on the sulfur content of the CNCs, indicating that the presence of sulfate half-ester groups on the CNC surfaces hindered labeling. The labeling reaction produced CNCs that had detectable fluorescence, without compromising the overall surface chemistry or behavior of the materials, an aspect relevant to studies that require a fluorescent cellulose substrate with intact native properties. The DTAF-labeled CNCs were proposed as optical markers for the dispersion quality of CNC-loaded polymer composites. Electrospun polyvinyl alcohol fibers loaded with DTAF-labeled CNCs appeared uniformly fluorescent by fluorescence microscopy, suggesting that the nanoparticles were well dispersed within the polymer matrix.
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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".