Design and Construction of Fluorescent Cellulose Nanocrystals for Biomedical Applications
Bibliographic record
Abstract
Abstract Cellulose nanocrystals (CNCs) are featured with low toxicity, non‐trivial biocompatibility, and cell membrane penetration capability, which allow the constructions of nanoplatforms for biosensing and in vivo imaging. Interfacing CNCs and fluorescent materials into sensors/probes is thus highly topical and has received tremendous interest. This review covers the development of CNC extraction methods and, in particular, their impacts on the surficial properties of CNCs. Whereafter, recently reported strategies for fluorescent functionalizations of CNCs are summarized based on chemical modification, physical adsorption, or in situ growth. Choosing the most suitable strategy, according to the properties of both CNCs and fluorophores, for constructing fluorescent CNCs is also emphasized. With regard to applications of the fluorescent CNCs, this work focuses on the studies which have involved but not been limited to metal ion sensing, physiological pH detection, cell imaging, and tumor antiproliferation. Being aware of the highly flexible construction, appealing structural/optical properties, and outstanding performances in analysis/imaging, it is believed that CNCs are bound to be increasingly investigated in the future and widely applied in the biomedical area.
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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".