Non-spherical gold nanoparticles enhanced fluorescence of carbon dots for norovirus-like particles detection
Bibliographic record
Abstract
Abstract Background Norovirus is a common pathogen that causes foodborne outbreaks every year and the increasing number of deaths caused by it has become a substantial concern in both developed and underdeveloped countries. To date, no vaccines or drugs are able to control the outbreak, highlighting the importance of finding specific, and sensitive detection tools for the viral pathogen. Current diagnostic tests are limited to public health laboratories and/or clinical laboratories and are time-consuming. Hence, a rapid and on-site monitoring strategy for this disease is urgently needed to control, prevent and raise awareness among the general public. Results The present study focuses on a nanohybridization technique to build a higher sensitivity and faster detection response to norovirus-like particles (NLPs). Firstly, the wet chemical-based green synthesis of fluorescent carbon quantum dots and gold nanoparticles (Au NPs) has been reported. Then, a series of characterization studies were conducted on the synthesized carbon dots and Au NPs, for example, high-resolution transmission emission microscopy, fluorescence spectroscopy, fluorescence life-lime measurement, UV–visible spectroscopy, and X-ray diffraction (XRD). The fluorescence emission of the as-synthesized carbon dots and the absorption of Au NPs were located at 440 nm and 590 nm, respectively. Then, the plasmonic properties of Au NPs were utilized to enhance the fluorescence emission of carbon dots in the presence of NLPs in human serum. Here, the enhanced fluorescence response was linearly correlated up to 1 μg mL−1. A limit of detection (LOD) value was calculated to be 80.3 pg mL−1 demonstrating that the sensitivity of the proposed study is 10 times greater than that of the commercial diagnostic kits. Conclusions The proposed exciton-plasmon interaction-based NLPs-sensing strategy was highly sensitive, specific, and suitable for controlling upcoming outbreaks. Most importantly, the overall finding in the article will take the technology a step further to applicable point-of-care (POC) devices.
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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 | 0.001 | 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".