Exploiting plasmonics in biosensing and bioimaging: monitoring cell receptors with surface enhanced spectroscopy and microscopy
Bibliographic record
Abstract
The plasmon resonance of noble metal nanoparticles (NP) manifests itself in a variety of extraordinary optical properties. Resonant excitation of the conduction electrons by incident radiation generates a localized surface plasmon resonance (LSPR) that is responsible for a variety of surface enhanced optical phenomena. This unique optical property coupled with well-established surface chemistry allows us to utilize both Ag and Au NP as optical contrasting agents to probe and monitor the surface receptors of cells. We have employed two plasmon-assisted optical techniques (namely, surface enhanced Raman scattering, and resonant Rayleigh scattering) to monitor the adrenergic receptors in mammalian cardiomyocyte cells that have been labeled with functionalized Ag NPs. In this study, a unique Raman reporter molecule, 4-(mercaptomethyl)benzonitrile, was developed to provide an easily identifiable vibration, the C≡N stretch, in a spectral window free from Raman bands of cell constituents and other biomolecules used in receptor crosslinking and surface passivation. Successfully labeled cells were then monitored with both optical techniques. Both techniques are related through the plasmonic properties of the noble metal NP and combined with high resolution imaging techniques; we outline the importance that different NP architectures play in the different imaging techniques. Furthermore, we will discuss the instrumentation and plasmonic implications in the design of NP best suited for such multimodal imaging approaches.
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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 | 0.001 | 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".