Inexpensive and Flexible SERS Substrates on Adhesive Tape Based on Biosilica Plasmonic Nanocomposites
Bibliographic record
Abstract
We demonstrate a simple method to prepare porous biosilica plasmonic composites on an inexpensive flexible substrate. The method does not require any chemical modification of the materials, and it allows the deposition of the nanocomposite on regular office-grade adhesive tape. This material was further characterized via scanning electron microscopy and optical microscopy, revealing unique properties such as pore size, plasmon resonance, and Raman enhancement factors suitable for biosensing applications. To demonstrate the usability of these strips in SERS-based sensing applications, we performed measurements on several proteins and bacteria of interest. Because of the porous nature of the nanocomposite, smaller proteins and nanostructures disperse within the material and present a reduced particle density for optical detection, which limits the ability to measure low concentrations of the analyte. On the other hand, particles that are larger than ∼100 nm concentrate at the top surface of the material and will be easier to detect via focused optical beams. We demonstrate that SERS can help detect and identify bacteria on this nanocomposite and believe that other applications are possible as well, in particular for the chemical characterization of biological particles of nano- to micrometer sizes.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".