Plasmon-free polymeric nanowrinkled substrates for surface-enhanced Raman spectroscopy of two-dimensional materials
Bibliographic record
Abstract
We report plasmon-free polymeric nanowrinkled substrates for surface-enhanced Raman spectroscopy (SERS). Our simple, rapid and cost-effective fabrication method involves depositing a poly(ethylene glycol) diacrylate (PEGDA) prepolymer solution droplet on a fully polymerized, flat PEGDA substrate, followed by drying the droplet at room conditions and plasma treatment, which polymerizes the deposited layer. The thin polymer layer buckles under axial stress during plasma treatment due to its different mechanical properties from the underlying soft substrate, creating hierarchical wrinkled patterns. We demonstrate the variation of the wrinkling wavelength with the drying polymer molecular weight and concentration (direct relations are observed). A transition between micron to nanosized wrinkles is observed at 5 v% concentration of the lower molecular weight polymer solution (PEGDA Mn 250). The wrinkled substrates are observed to be reproducible, stable (at room conditions) and, specially, homogeneous at and below the transition regime, where nanowrinkles dominate, making them suitable candidates for SERS. As a proof-of-concept, the enhanced SERS performance of micro/nano-wrinkled surfaces in detecting graphene and hexagonal boron nitride (h-BN) is illustrated. Compared to the SiO2/Si surfaces, the wrinkled PEGDA substrates significantly enhanced the signature Raman bands intensities of graphene and h-BN by a factor of 8 and 50, respectively.
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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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".