Materials Perspectives of Integrated Plasmonic Biosensors
Bibliographic record
Abstract
With the growing need for portable, compact, low-cost, and efficient biosensors, plasmonic materials hold the promise to meet this need owing to their label-free sensitivity and deep light-matter interaction that can go beyond the diffraction limit of light. In this review, we shed light on the main physical aspects of plasmonic interactions, highlight mainstream and future plasmonic materials including their merits and shortcomings, describe the backbone substrates for building plasmonic biosensors, and conclude with a brief discussion of the factors affecting plasmonic biosensing mechanisms. To do so, we first observe that 2D materials such as graphene and transition metal dichalcogenides play a major role in enhancing the sensitivity of nanoparticle-based plasmonic biosensors. Then, we identify that titanium nitride is a promising candidate for integrated applications with performance comparable to that of gold. Our study highlights the emerging role of polymer substrates in the design of future wearable and point-of-care devices. Finally, we summarize some technical and economic challenges that should be addressed for the mass adoption of plasmonic biosensors. We believe this review will be a guide in advancing the implementation of plasmonics-based integrated biosensors.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".