Cicada Wing Inspired Template-Stripped SERS Active 3D Metallic Nanostructures for the Detection of Toxic Substances
Bibliographic record
Abstract
This article introduces a bioinspired, cicada wing-like surface-enhanced Raman scattering (SERS) substrate based on template-stripped crossed surface relief grating (TS-CSRG). The substrate is polarization-independent, has tunable nanofeatures and can be fabricated in a cleanroom-free environment via holographic exposure followed by template-stripping using a UV-curable resin. The bioinspired nanostructures in the substrate are strategically designed to minimize the reflection of light for wavelengths shorter than their periodicity, promoting enhanced plasmonic regions for the Raman excitation wavelength at 632.8 nm over a large area. The grating pitch that enables an effective SERS signal is studied using Rhodamine 6G, with enhancement factors of the order of 1 × 104. Water contact angle measurements reveal that the TS-CSRGs are equally hydrophobic to cicada wings, providing them with potential self-cleaning and bactericidal properties. Finite-difference time-domain simulations are used to validate the nanofabrication parameters and to further confirm the polarization-independent electromagnetic field enhancement of the nanostructures. As a real-world application, label-free detection of melamine up to 1 ppm, the maximum concentration of the contaminant in food permitted by the World Health Organization, is demonstrated. The new bioinspired functional TS-CSRG SERS substrate holds great potential as a large-area, label-free SERS-active substrate for medical and biochemical sensing applications.
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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) | 0.000 | 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 | 0.001 | 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".