Protein capture and SERS detection on multiwavelength rainbow-trapping width-graded nano-gratings
Bibliographic record
Abstract
Abstract Surface-enhanced Raman scattering (SERS) substrates with multiwavelength rainbow-trapping properties hold the potential for a one-size-fits-all platform for rapid and multiplexed disease detection. We present the first report on the utilization of rainbow-trapping width-graded nano-gratings, a new class of chirped metamaterials, to detect protein biomarkers. Using cytochrome c (Cc), a charged analyte with inherent difficulty in adsorbing onto sputtered silver films, we investigated methods of binding Cc on the silver nano-grating in order to improve the SERS signal strength at both 532 and 638 nm excitation. Cc was not detectable on the Ag nano-gratings without surface functionalization at 1 μ M concentration. Upon charge reversal functionalization of the Ag nano-gratings, 1 μ M Cc was detectable albeit not reliably. By further crosslinking 1 μ M Cc to the functionalized Ag nano-gratings, the analyte-capture detection scheme greatly improved the SERS signal strength and reliability at both excitation wavelengths and allowed for quantification of their coefficients of variation with values down to 27%.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".