Flexible microsphere‐coupled surface‐enhanced Raman spectroscopy (McSERS) by dielectric microsphere cavity array with random plasmonic nanoparticles
Bibliographic record
Abstract
Abstract Surface‐enhanced Raman spectroscopy (SERS) is a powerful tool for nondestructive and ultrasensitive optical trace‐detection. However, the sophisticated fabrication processes and performance degradation on flexible substrates block SERS for practical uses. Here, we report a facile flexible microsphere‐coupled SERS (McSERS) substrate composed of a dielectric microsphere cavity array (MCA) and random gold nanoparticles (AuNPs) capping on a polydimethylsiloxane (PDMS) film (MCA/AuNPs/PDMS) for giant Raman enhancement. The random distribution of AuNPs provides a hydrophilic surface against to the coffee‐ring effect for uniform localized surface plasmon resonance (LSPR) response. The MCA capped on the AuNPs boosts the Raman intensity via the multiple optical manipulation processes, in which the photonic nanojet (PNJ) confines the excitation intensity near the AuNPs, whispering‐gallery mode (WGM) facilitates the energy transfer from microsphere cavities to AuNP gaps for LSPR boosting, and directional antenna effect converts near‐field Raman signals into far‐field with a small divergence. Therefore, the Raman scattering is dramatically improved with the enhancement factor (EF) to 107 for the limit of detection (LoD) of 4‐nitrobenzenethiol (4‐NBT) molecules down to 0.1 nM, two orders of magnitude higher via MCA coupling. Moreover, the flexible McSERS substrate exhibits outstanding durability and compatibility as an ultrasensitive Raman test strip, by which the thiram concentration is detectable down to 2.42 ng/cm2 on apple peels. The present work provides a facile strategy to fabricate SERS substrates with high flexibility for optical trace‐detection in real‐world 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) | 0.001 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".