Advances in three dimensional metal enhanced fluorescence based biosensors using metal nanomaterial and nano‐patterned surfaces
Bibliographic record
Abstract
Metal enhanced fluorescence (MEF) is a phenomenon that increases fluorescence signal through placement of metal near a fluorophore. For biosensing applications, MEF-based biosensors are becoming increasingly popular as it enables highly sensitive detection of molecules, important for early diagnosis. The structure and size of the metal influence the optical properties through enhancing the fluorophore photostability and light absorption and emission. In recent years, many metal nanostructures have been fabricated and examined for their effectiveness in developing MEF-based biosensors. This review focuses on the latest applications of three-dimensional nanostructures and nano-patterned surfaces used to develop and improve fluorescence sensing via MEF. Current reviews mostly discussed the applications of two dimensional MEF and metal-nanoparticles-based MEF with a focus on fabrication of nanoparticles and metal substrates. In this article, we focused more on the effect of the metal nanostructure and size on MEF and then provided an in-depth summary of the performance of the state-of-the-art three dimensional MEF-based biosensors. While more work is needed to demonstrate applicability for complex samples, it is evident that with the use of metal nanoparticles and three dimensional nano-patterns, the assay sensitivity of fluorescence-based detection can be greatly improved, making it suitable for use in early disease diagnostics.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".