The Application of Carbon Dots in Crops for Sustainable Agriculture
Bibliographic record
Abstract
Sustainable agricultural systems face numerous challenges, including the need for sustainable resources, enhanced crop productivity, and improved postharvest management. Due to their unique physicochemical properties, such as tunable luminescence range, biocompatibility, ease of synthesis, facile surface functionality, water solubility, and low toxicity, carbon dots (CDs) have emerged as promising nanomaterials for sustainable agriculture. These features enable their broad application throughout the agricultural life cycle, from promoting plant growth and development to enhancing the preservation and detection of agricultural products. CDs can be synthesized from a wide variety of organic waste, including crop residues, facilitating their reintegration into a closed‐loop agricultural system. Here, recent progress in the use of CDs in key stages of the crop growth cycle, including seed germination, vegetative development, and crop protection, is summarized. In addition, their role in postharvest preservation, antimicrobial activity, and safety assessment of agricultural commodities is discussed. Finally, current limitations and future directions for the application of CDs in sustainable agriculture are critically evaluated.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".