Application of carbon dots in food preservation: a critical review for packaging enhancers and food preservatives
Bibliographic record
Abstract
Carbon dots (CDs) have two unique advantages: one is ease of synthesis at low price, the other is desirable physical and chemical properties, such as ultra-small size, abundant surface functional groups, nontoxic/low-toxicity, good biocompatibility, excellent antibacterial and antioxidant activities etc. These advantages provide opportunities for the development of new food packaging enhancers and food preservatives. This paper systematically reviews the studies of CDs used to strengthen the physical properties of food packaging, including strengthen mechanical strength, ultraviolet (UV) barrier properties and water barrier properties. It also reviews the researches of CDs used to fabricate active packaging with antioxidant and/or antibacterial properties and intelligent packaging with the capacity of sensing the freshness of food. In addition, it analyzes the antioxidant and antibacterial properties of CDs as preservatives, and discusses the effect of CDs applied as coating agents and nano-level food additives for extension the shelf life of food samples. It also provides a brief review on the security and the release behavior of CDs.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".