Integrated Optics Polarized Light and Evanescent Wave Surface-Enhanced Raman Scattering to detect ligand Interactions at nanoparticle surfaces
Bibliographic record
Abstract
The orientation of sensing molecules on the surface of biosensors is crucial for effective interaction with target analytes, and Raman spectroscopy is a versatile and non-invasive technique used to study molecular configurations at the sub-nanoscale level. This study explores the sensing abilities of an integrated optics construct called an Optical Chemical Bench (OCB) for the detection of molecular orientation, ion binding, and nanoparticle binding. The OCB consists of plasmonic gold-silver nanoparticles bound to the surface of a multimode slab waveguide. This design offers controlled plasmonic excitation in both position and polarization, increasing the interfacial mean square electric field relative to the incident field, and allowing for polarization-dependent surface-enhanced Raman scattering (SERS) on a chip. The experiments gave insight into how the TE and TM polarization modes interact with adsorbates that are built up as hierarchical structures on the OCB, providing an inexpensive yet effective molecular probing technology at the interface.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".