Self-illumination of Carbon Dots by Bioluminescence Resonance Energy Transfer
Bibliographic record
Abstract
Carbon-dots (CDs), the emerging fluorescent nanoparticles, show special multicolor properties, chemical stability, and biocompatibility, and are considered as the new and advanced imaging probe in replacement of molecular fluorophores and semiconductor quantum dots. However, the requirement of external high power light source limits the application of fluorescent nanomaterials in bio-imaging. The present study aims to take advantage of bioluminescence resonance energy transfer mechanism (BRET) in creating self-illuminating C-dots. Renilla luciferase (Rluc) is chosen as the BRET donor molecule. Conjugation of Renilla luciferase and C-dots is necessary to keep their distance close for energy transfer. The optimal condition for achieving BRET is investigated by studying the effects of different factors on the performance of BRET, including the type of conjugation, concentration of carbon dots, and conjugation time. The linear relationship of BRET efficiency as a function of the amount of C-dots in the range of 0.20-0.80 mg/mL is observed. The self-illuminating carbon dots could be applied in bioimaging avoiding the tissue damage from the external high power light source.
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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".