Effects of polydopamine-passivation on the optical properties of carbon dots and its potential use <i>in vivo</i>
Bibliographic record
Abstract
Passivation of carbon dots via heteroatom doping has been shown to enhance their optical properties and tune their fluorescence signature. Additionally, the incorporation of polymeric precursors in carbon dot synthesis has gained considerable interest with benefits to biological applications namely bioimaging, drug delivery and sensing, among others. In order to combine the desirable attributes of both, fluorescence enhancement and increased biocompatibility, polymers composed of high aromaticity and nitrogen content can be used as efficient carbon dot passivating agents. Here, the synthesis of fluorescent polymer-passivated carbon dots was developed through a microwave-assisted pyrolysis reaction of galactose, citric acid and polydopamine. Passivation of the dots with polydopamine induces a 90 nm red-shift in the fluorescence maxima from 420 to 510 nm. Moreover, passivation results in excitation-independent fluorescence and a 3.5-fold increase in fluorescence quantum yield, which increases from 1.3 to 4.6%. The application of the carbon dots as imaging probes was investigated in in vitro and in vivo model systems. Cytotoxicity studies in J774 and CHO-K1 cell lines revealed reduced cell toxicity for the polydopamine-passivated carbon dots in comparison to their unpassivated counterpart. In BALB/c mice, biodistribution studies demonstrated that regardless of surface passivation, the dots predominantly remained in the circulatory system 90 minutes post inoculation suggesting their potential use for cardiovascular therapies.
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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".