Bibliographic record
Abstract
The potential of surface plasmon waveguides for bulk and surface (bio)chemical sensing was assessed theoretically, anticipating their use in an integrated optics sensor such as a Mach–Zehnder interferometer (MZI). The performance of a generic MZI implemented with attenuating waveguides was assessed initially, revealing that attenuating waveguides constrain the sensing length to an optimal length equal to the propagation length of the mode used. The MZI sensitivities for bulk and surface sensing were found to be proportional to the ratio of the waveguide sensitivity to its normalized attenuation: H =(∂ n eff /∂ n c )/ k eff for bulk sensing and G =(∂ n eff /∂ a )/ k eff for surface sensing. Maximizing H or G maximizes the corresponding MZI sensitivity and minimizes its detection limit, leading to preferred waveguide designs and operating wavelengths. The propagation constant, the sensitivities, and the H and G parameters were then determined for the surface plasmon in the single interface, the s b mode in the metal–insulator–metal (MIM) waveguide and the s b mode in three variants of the insulator–metal–insulator (IMI) waveguide, as a function of dimensions, for wavelengths spanning 600⩽λ 0 ⩽1600 nm, assuming Au and H 2 O as the materials and adlayers representative of biochemical matter. The principal findings are: (i) the surface sensitivity in the thin MIM can be 100 × larger than in the single interface, whereas that in the thin IMI is up to 5 × smaller; (ii) the bulk sensitivity in the thin MIM can be 3 × larger than in the single interface, whereas that in the IMI is slightly smaller; (iii) G in the thin MIM can be 3 × larger than in the single interface, whereas G in the IMI is about 10 × larger; and (iv) H in the thin MIM can be 10 × smaller than in the single interface, whereas H in the thin IMI is about 10 × larger. The IMI and the MIM both offer an improvement in sensitivity and detection limit for surface sensing over the single interface in an integrated MZI (or Kretschmann–Raether) configuration, despite the fact that they are at opposite ends of the confinement–attenuation trade-off. Preferred wavelengths for surface sensing were found to be near the short wavelength edge of the Drude region, where detection limits of about 0.1 pg mm −2 are predicted. With regard to bulk sensing, only the IMI offers an improvement over the single interface. The results are collected in a form that should be useful for investigating other sensor architectures implemented with these waveguides or variants thereof.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".