Magneto-Optic Surface Plasmon Resonance Ti/Au/Co/Au/Pc Configuration and Sensitivity
Bibliographic record
Abstract
Magneto-optic surface plasmon resonance (MOSPR) based sensors are highly attractive as next generation biosensors. However, these sensors suffer from oxidation leading to degradation of performance, reproducibility of the sensor surface because of the difficulty of removing adsorbed materials, and degradation of sensor surface during surface cleaning, and these limit their applications. In this paper, I propose MOSPR-based biosensors with 0 to 15 nm thick inert polycarbonate laminate plastic as a protective layer and theoretically demonstrate the practicability of our approach in water-medium for three different probing samples: ethanol, propanol, and pentanol. I also investigate microstructure and magnetic properties. The chemical composition and layered information of the sensor are investigated using X-ray reflectivity and X-ray diffraction analyses and these show distinct fcc-Au (111) phases, as dominated by the higher density of conduction electrons in Au as compared to Co. The magnetic characterization measured with the in-plane magnetic field to the sensor surface for both the as-deposited and annealed multilayers showed isotropic easy axis magnetization parallel to the multilayer interface at a saturating magnetic field of <100 Oe. The sensor showed a maximum sensitivity of 5.5 × 104%/RIU for water-ethanol media and the highest detection level of 2.5 × 10−8 for water-pentanol media as the protective layer is increased from 0 to 15 nm.c
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How this classification was reachedexpand
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.003 |
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 teacher head, 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".