Self-assembled Star-shaped Chiroplasmonic Gold Nanoparticles for Ultrasensitive Chiro-immunosensor of Viruses
Bibliographic record
Abstract
Near field optics and optical tunneling light-matter interaction in the superstructure of chiral nanostructures and semiconductor quantum dots exhibit strong optical rotation activity that may open a new window for chiral-based bioanalytes detection. Herein we report an ultrasensitive, chiro-immunosensor using superstructure of chiral gold nanohybrids (CAu NPs) and quantum dots (QDs). Self-assembling techniques were employed to create asymmetric plasmonic chiral nanostructures for extending the spectral range of circular dichroism (CD) response for obtaining superior plasmonic resonant coupling with the QDs excitonic state; this may help to achieve lower the limit of detection (LOD) values. As a result, the designed probe exhibited avian influenza A (H5N1) viral concentration at picomolar level, a significant improvement in sensitivity in comparison to a non-assembled CAu NPs based chiroassay. The practicability of the proposed sensing system was successfully demonstrated on several virus cultures including, avian influenza A (H4N6) virus, fowl adenovirus and coronavirus in blood samples. The results of our study highlights that exciton-plasmon interaction changes chirality and the self-assembled nanostructures are an efficient strategy for enhancing the sensitivity of plasmonic nanosensors.
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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) | 0.000 | 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".