Synthesis of Self‐Assembled Spermidine‐Carbon Quantum Dots Effective against Multidrug‐Resistant Bacteria
Bibliographic record
Abstract
This study reports a two-step method to synthesize spermidine-capped fluorescent carbon quantum dots (Spd-CQDs) and their potential application as an antibacterial agent. Fluorescent carbon quantum dots (CQDs) are synthesized by pyrolysis of ammonium citrate in the solid state and then modified with spermidine by a simple heating treatment without a coupling agent. Spermidine, a naturally occurring polyamine, binds with DNA, lipids, and proteins involved in many important processes within organisms such as DNA stability, and cell growth, proliferation, and death. The antimicrobial activity of the as-synthesized Spd-CQDs (size ≈4.6 nm) has been tested against non-multidrug-resistant E. coli, S. aureus, B. subtilis, and P. aeruginosa bacteria and also multidrug-resistant bacteria, methicillin-resistant S. aureus (MRSA). The minimal inhibitory concentration value of Spd-CQDs is much lower (>25 000-fold) than that of spermidine, indicating their promising antibacterial characteristics. The mechanism of antibacterial activity is investigated, and the results indicate that Spd-CQDs cause significant damage to the bacterial membrane. In vitro cytotoxicity and hemolysis analyses reveal the high biocompatibility of Spd-CQDs. To demonstrate its practical application, in vitro MRSA-infected wound healing studies in rats have been conducted, which show faster healing, better epithelialization, and formation of collagen fibers when Spd-CQDs are used as a dressing material.
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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) | 0.001 | 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".