Flexible microsphere‐coupled surface‐enhanced Raman spectroscopy (McSERS) by dielectric microsphere cavity array with random plasmonic nanoparticles
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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 (Au NP s) capping on a polydimethylsiloxane (PDMS) film (MCA/Au NP s/PDMS) for giant Raman enhancement. The random distribution of Au NP s provides a hydrophilic surface against to the coffee‐ring effect for uniform localized surface plasmon resonance (LSPR) response. The MCA capped on the Au NP s boosts the Raman intensity via the multiple optical manipulation processes, in which the photonic nanojet (PNJ) confines the excitation intensity near the Au NP s, whispering‐gallery mode (WGM) facilitates the energy transfer from microsphere cavities to Au NP 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 10 7 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/cm 2 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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.003 | 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 it