Nanomaterial‐Based Optical Biosensors for SARS‐CoV‐2 Detection: A Retrospective of the Pandemic
Bibliographic record
Abstract
Abstract From 2020 to 2023, the spread of severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2) caused a global health crisis, as millions of people worldwide contracted the coronavirus disease of 2019 (COVID‐19). Conventional diagnostic techniques, such as reverse transcription‐quantitative polymerase chain reaction (RT‐PCR), struggled to meet increasing testing needs required for a pandemic owing to significant downsides hindering their large‐scale use. In efforts to curb the effects of the pandemic and to meet the increasing demand for fast and accurate point‐of‐care (POC) testing, scientists and industries alike raced to engineer new diagnosis methods and adapt previously developed ones. Now that the COVID‐19 pandemic has passed, the present review aims to provide the reader with an overview of recent advances in biosensing resulting from these efforts and to offer insight for future pandemics. This review focuses on nanomaterial‐based optical biosensors, which are central to multiple emerging diagnostic tools. It covers techniques such as lateral flow immunoassays (LFIA), plasmonic biosensors based on surface plasmon resonance (SPR) and localized SPR (LSPR), surface‐enhanced Raman spectroscopy (SERS), and surface‐enhanced fluorescence (SEF). LFIAs played an important role in the COVID‐19 pandemic and will continue to shape biosensing in future pandemics, while other techniques are yet to reach commercialization despite recent strides.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".