Surface-enhanced mid-infrared absorption spectroscopy using miniaturized-disc metasurface
Bibliographic record
Abstract
Surface-enhanced infrared spectroscopy is an important technique for improving the signal-to-noise ratio of spectroscopic material identification measurements in the mid-infrared fingerprinting region. However, the lower bound of the fingerprinting region receives much less attention due to a scarcity of transparent materials, more expensive sources, and weaker plasmonic effects. In this paper, we present a miniaturized metasurface unit cell for surface-enhanced infrared spectroscopy of the 15-[Formula: see text]m vibrational band of CO[Formula: see text]. The unit cell consists of a gold disc, patterned along the edge with fine gaps/wires to create a resonant metamaterial liner. In simulation, our plasmonic metamaterial-lined disc achieves greater than [Formula: see text] the average field intensity enhancement of a comparable dipole array and a miniaturized size of [Formula: see text] using complex, 100-nm features that are patterned using 100-kV electron-beam lithography. In a simple experiment, the metamaterial-lined disc metasurface shows a high tolerance to fabrication imperfections and enhances the absorption of CO[Formula: see text] at 15 [Formula: see text]m. The resonant wavelength and reflection magnitude can be tuned over a wide range by adjusting the liner feature sizes and the metasurface array pitch to target other vibrational bands. This work is a step toward low-cost, more compact on-chip integrated gas sensors.
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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 | 0.001 |
| 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".