Plasmonic and Hybrid Whispering Gallery Mode–Based Biosensors: Literature Review
Bibliographic record
Abstract
BACKGROUND: The term "plasmonic" describes the relationship between electromagnetic fields and metallic nanostructures. Plasmon-based sensors have been used innovatively to accomplish different biomedical tasks, including detection of cancer. Plasmonic sensors also have been used in biochip applications and biosensors and have the potential to be implemented as implantable point-of-care devices. Many devices and methods discussed in the literature are based on surface plasmon resonance (SPR) and localized SPR (LSPR). However, the mathematical background can be overwhelming for researchers at times. OBJECTIVE: This review article discusses the theory of SPR, simplifying the underlying physics and bypassing many equations of SPR and LSPR. Moreover, we introduce and discuss the hybrid whispering gallery mode (WGM) sensing theory and its applications. METHODS: A literature search in ScienceDirect was performed using keywords such as "surface plasmon resonance," "localized plasmon resonance," and "whispering gallery mode/plasmonic." The search results retrieved many articles, among which we selected only those that presented a simple explanation of the SPR phenomena with prominent biomedical examples. RESULTS: SPR, LSPR, tilted fiber Bragg grating, and hybrid WGM phenomena were explained and examples on biosensing applications were provided. CONCLUSIONS: This minireview presents an overview of biosensor applications in the field of biomedicine and is intended for researchers interested in starting to work in this field. The review presents the fundamental notions of plasmonic sensors and hybrid WGM sensors, thereby allowing one to get familiar with the terminology and underlying complex formulations of linear and nonlinear optics.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".