Hydrophobic carbon dots: An overview of the synthesis, purification, cytotoxicity, and potential applications in food safety and analytical chemistry
Bibliographic record
Abstract
Hydrophobic carbon dots (HCDs) represent a burgeoning class of nanomaterials distinguished by their unique physicochemical, antimicrobial, and optical properties. These attributes have propelled HCDs to the forefront of research, particularly in the fields such as composite film production, biological imaging, and antibacterial coatings, with broad implications for industries, such as food safety, medicine, catalysis, and sensor technology. This comprehensive review delves into the diverse synthesis methodologies of HCDs, such as chemical oxidation, hydrothermal/solvothermal techniques, pyrolysis, and microwave irradiation. The comparative benefits and challenges of these methods were analyzed critically. This manuscript presents an in-depth exploration of purification methods, hydrophobicity indices, and cytotoxicity by a thorough examination of current literature. In addition, it highlights the innovative applications of HCDs, from advanced chemosensors, the development of stationary phases for chromatography to bioimaging and diagnostics, and the construction of optoelectric devices for food applications.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".