Fundamentals and applications of SERS‐based bioanalytical sensing
Bibliographic record
Abstract
Abstract Plasmonics is an emerging field that examines the interaction between light and metallic nanostructures at the metal‐dielectric interface. Surface‐enhanced Raman scattering (SERS) is a powerful analytical technique that uses plasmonics to obtain detailed chemical information of molecules or molecular assemblies adsorbed or attached to nanostructured metallic surfaces. For bioanalytical applications, these surfaces are engineered to optimize for high enhancement factors and molecular specificity. In this review we focus on the fabrication of SERS substrates and their use for bioanalytical applications. We review the fundamental mechanisms of SERS and parameters governing SERS enhancement. We also discuss developments in the field of novel SERS substrates. This includes the use of different materials, sizes, shapes, and architectures to achieve high sensitivity and specificity as well as tunability or flexibility. Different fundamental approaches are discussed, such as label‐free and functional assays. In addition, we highlight recent relevant advances for bioanalytical SERS applied to small molecules, proteins, DNA, and biologically relevant nanoparticles. Subsequently, we discuss the importance of data analysis and signal detection schemes to achieve smaller instruments with low cost for SERS‐based point‐of‐care technology developments. Finally, we review the main advantages and challenges of SERS‐based biosensing and provide a brief outlook.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".