Flexible Surface-Enhanced Raman Scattering Tape Based on Ag Nanostructured Substrate for On-Site Analyte Detection
Bibliographic record
Abstract
Surface-enhanced Raman scattering (SERS) has emerged as a powerful surface analytical technique that amplifies Raman scattering signals of molecules adsorbed onto metal nanostructured surfaces. The droplet reaction method has recently been employed to fabricate large-scale microring patterns of silver (Ag) nanostructures on rigid substrates, which enables sensitive detection within the ring area. However, these rigid substrates present limitations for direct on-site detection of analyte residues on irregular sample surfaces. There is a need to develop soft and flexible SERS substrates that can intimately conform to arbitrary surfaces. In this study, we presented a SERS substrate using flexible and adhesive tape as the supporting material. This SERS tape was fabricated by repeatedly transferring presynthesized Ag nanostructures from a rigid substrate to the tape. For a model compound adenine, our SERS tape exhibited a good linear response from 5 × 10 –4 M to 5 × 10 –5 M with a low limit of detection (LOD) of 5 × 10 –7 M and displayed a SERS enhancement factor (EF) of 3.2 × 10 5 . The relative standard deviation (RSD) of SERS intensity achieved was as low as 1.93%, indicating its outstanding uniformity. The as-prepared SERS tape was used for in situ detection of pesticide residue on an apple surface and dye residue on human hair. Leveraging the large surface area of Ag nanostructure patterns from the droplet reaction, the developed SERS tape demonstrates excellent performance in terms of sensitivity and uniformity. The successful detection of analyte residues on arbitrary surfaces of apple and human hair highlights the potential of this flexible SERS tape for real-world applications across various industries for enhanced diagnostic accuracy.
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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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".