Photochemical Synthesis of Sericin-Coated Gold Nanorods and Their Antibacterial Activity under Low-Level Near-Infrared Light
Bibliographic record
Abstract
Gold nanorods (GNRs) are unique nanoparticles with easily functionalized surfaces, multiple synthesis methods, photothermal conversion, and surface plasmon resonance effects. These properties make GNRs suitable for various biological applications. However, a rapid synthesis of GNRs using less toxic chemicals is needed. The photochemical method is a viable option that can synthesize GNRs quickly while using fewer chemicals. A photochemical method is reported for the synthesis of GNRs using Irgacure-2959 as a reducing agent. This method could be used to synthesize GNRs with a rod-like shape within 30 min. Additionally, GNRs were coated with sericin (GNRs-SC) to further reduce their toxicity in human dermal fibroblast adult cells. Low-level near-infrared (NIR) light was applied to enhance the photothermal therapy of both GNRs and GNRs-SC. The results showed that GNRs and GNRs-SC under low-level NIR light have enhanced antibacterial activity against Staphylococcus aureus and Escherichia coli, as well as antibiofilm activity against S. aureus . Furthermore, GNRs-SC showed good biocompatibility with antibacterial and antibiofilm activities. These results indicate that GNRs-SC are good candidates for various biological applications.
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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".