Towards Point-of-Care Single Biomolecule Detection Using Next Generation Portable Nanoplasmonic Biosensors: A Review
Bibliographic record
Abstract
Over the past few years, nanoplasmonic biosensors have gained widespread interest for early diagnosis of diseases thanks to their simple design, low detection limit down to the biomolecule level, high sensitivity to even small molecules, cost-effectiveness, and potential for miniaturization, to name but a few benefits. These intrinsic natures of the technology make it the perfect solution for compact and portable designs that combine sampling, analysis, and measurement into a miniaturized chip. This review summarizes applications, theoretical modeling, and research on portable nanoplasmonic biosensor designs. In order to develop portable designs, three basic components have been miniaturized: light sources, plasmonic chips, and photodetectors. There are five types of portable designs: portable SPR, miniaturized components, flexible, wearable SERS-based, and microfluidic. The latter design also reduces diffusion times and allows small amounts of samples to be delivered near plasmonic chips. The properties of nanomaterials and nanostructures are also discussed, which have improved biosensor performance metrics. Researchers have also made progress in improving the reproducibility of these biosensors, which is a major obstacle to their commercialization. Furthermore, future trends will focus on enhancing performance metrics, optimizing biorecognition, addressing practical constraints, considering surface chemistry, and employing emerging technologies. In the foreseeable future, these trends will be merged to result in portable nanoplasmonic biosensors offering detection of even a single biomolecule.
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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) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".