SARS-CoV-2 Receptor Binding Domain as a Stable-Potential Target for SARS-CoV-2 Detection by Surface—Enhanced Raman Spectroscopy
Bibliographic record
Abstract
In this work, we report a new approach for detecting SARS-CoV-2 RBD protein (RBD) using the surface-enhanced Raman spectroscopy (SERS) technique. The optical enhancement was obtained thanks to the preparation of nanostructured Ag/Au substrates. Fabricated Au/Ag nanostructures were used in the SERS experiment for RBD protein detection. SERS substrates show higher capabilities and sensitivity to detect RBD protein in a short time (3 s) and with very low power. We were able to push the detection limit of proteins to a single protein detection level of 1 pM. The latter is equivalent to 1 fM as a detection limit of viruses. Additionally, we have shown that the SERS technique was useful to figure out the presence of RBD protein on antibody functionalized substrates. In this case, the SERS detection was based on protein-antibody recognition, which led to shifts in the Raman peaks and allowed signal discrimination between RBD and other targets such as Bovine serum albumin (BSA) protein. A perfect agreement between a 3D simulated model based on finite element method and experiment was reported confirming the SERS frequency shift potential for trace proteins detection. Our results could open the way to develop a new prototype based on SERS sensitivity and selectivity for rapid detection at a very low concentration of virus and even at a single protein level.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".