Self-Assembly of Strain-Adaptable Surface-Enhanced Raman Scattering Substrate on Polydimethylsiloxane Nanowrinkles
Bibliographic record
Abstract
Flexible surface-enhanced Raman scattering (SERS) substrates adaptable to strains enable effective sampling from irregular surfaces, but the preparation of highly stable and sensitive flexible SERS substrates is still challenging. This paper reports a method to fabricate a high-performance strain-adaptable SERS substrate by self-assembly of Au nanoparticles (AuNPs) on polydimethylsiloxane (PDMS) nanowrinkles. Nanowrinkles are created on prestrained PDMS slabs by plasma-induced oxidation followed by the release of the prestrain, and self-assembled AuNPs are transferred onto the nanowrinkles to construct the high-performance SERS substrate. The results show that the nanowrinkled structure can improve the surface roughness and enhance the SERS signals by ∼4 times compared to that of the SERS substrate prepared on flat PDMS substrates. The proposed SERS substrate also shows good adaptability to dynamic bending up to ∼|0.4| 1/cm with excellent testing reproducibility. Phenolic pollutants, including aniline and catechol, were quantitatively tested by the SERS substrate. The self-assembled flexible SERS substrate proposed here provides a powerful tool for chemical analysis in the fields of environmental monitoring and food safety inspection.
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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 | 0.000 | 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".