Injectable Shear-Thinning Fluorescent Hydrogel Formed by Cellulose Nanocrystals and Graphene Quantum Dots
Bibliographic record
Abstract
In the search for new building blocks of nanofibrillar hydrogels, cellulose nanocrystals (CNCs) have attracted great interest because of their sustainability, biocompatibility, ease of surface functionalization, and mechanical strength. Making these hydrogels fluorescent extends the range of their applications in tissue engineering, bioimaging, and biosensing. We report the preparation and properties of a multifunctional hydrogel formed by CNCs and graphene quantum dots (GQDs). We show that although CNCs and GQDs are both negatively charged, hydrogen bonding and hydrophobic interactions overcome the electrostatic repulsion between these nanoparticles and yield a physically cross-linked hydrogel with tunable mechanical properties. Owing to their shear-thinning behavior, the CNC-GQD hydrogels were used as an injectable material in 3D printing. The hydrogels were fluorescent and had an anisotropic nanofibrillar structure. The combination of these advantageous properties makes this hybrid hydrogel a promising material and fosters the development of new manufacturing methods such as 3D printing.
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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".