Experimental study of sensitivity enhancement in SPR biosensors by use of zinc oxide intermediary layers
Bibliographic record
Abstract
We report a novel design for the intermediary layer of surface plasmon resonance (SPR) devices that use high refractive index and high-transmittance zinc oxide (ZnO) dielectric layers to enhance the signal quality and improve the full width at half-maximum (FWHM) of the reflectivity curve. We optimized the design of ZnO thin films using different sputtering parameters and performed analytical comparisons with conventional intermediary layers of chromium (Cr) as well as indium tin oxide (ITO). The study is based on application of the Fresnel equation, which provides an explanation and verification for the observed SPR narrow-width curve and optical transmittance spectra displayed by (ZnO/Au)-1, (Cr/Au)-4 and (ITO/Au)-5 devices. On exposure to ethanol, the (ZnO/Au)-1 device showed a two-fold decrease in FWHM and a 4.5-fold larger shift in intensity interrogation. The (ZnO/Au)-1 device exhibits a wider linearity range and much higher sensitivity. They also exhibit a good linear relationship between angle and concentration dependence in the tested range. We show that these advances represent a novel and simple method for preparing high-sensitivity, high-resolution SPR biosensors for accurate and specific bio-molecular detection.
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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 | 0.000 |
| 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 | 0.000 |
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".