Improved Magneto-Optic Surface Plasmon Resonance Biosensors
Bibliographic record
Abstract
The magneto-optic (MO) characteristics and sensing performance of noble metal (Ag, Au, Cu) or transition metal (Fe, Ni, Co) single layers and Ag/Co or Au/Co bilayers have been studied and compared in both the standard plasmonic and MO plasmonic configurations at two different wavelengths (632.8 nm and 785 nm) and in two different sensing media (air and water). The sensing performance is found to be medium-specific and lower in biosensor-relevant water-based media. The sensitivities of MO-SPR sensors is found to be superior to SPR sensors in all cases. This enhancement in sensitivity means the detection limit of this class of transducers can be substantially improved by tuning Au/Co layer thickness, wavelength, and incident angle of optical radiation. The optimized bilayer showed an enhancement in sensitivity by over 30× in air and 9× in water as compared to the conventional Au SPR configuration. Notably, the best performance is 3× above that of MO-SPR sensors coupled to a photonic crystal previously reported in the literature and is found when the ferromagnetic layer is furthest from the sensing medium, as opposed to typical MO-SPR configurations. This proposed structure is attractive for next-generation biosensors.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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 | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".