Propagating Surface Plasmon Resonance on Microhole Arrays
Bibliographic record
Abstract
Metallic thin films patterned with micrometer size triangle or hole arrays present plasmonic properties when excited in the Kretschmann configuration, that are improved in comparison to conventional thin film surface plasmon resonance (SPR). These optical properties can be tuned by varying the physical aspects of the microplasmonic structures. Triangles and microhole arrays were prepared with modified nanosphere lithography (NSL) using latex spheres of 0.65, 0.82, 1.0, 1.5, or 3.2 microm in diameter. This allowed the preparation of triangles with edge lengths between 275 to 2000 nm and microhole arrays of various periodicities, diameters, and hole depths. These microstructures were studied to understand the relationship between the physical aspects and the optical properties, such as the sensitivity, working refractive index range, spectral width of the plasmonic peaks, spectral noise, and refractive index resolution. Microhole arrays with a hole diameter equal to half the periodicity were found to combine the advantages of both localized surface plasmon resonance (LSPR) on nanoparticles and SPR on a thin film. These microhole arrays exhibited high sensitivity to refractive index (>3000 nm/RIU), sensitivity to monolayer formation (2-fold improvement compared to thin films), and excellent refractive index resolution (10(-6) RIU). Finally, a biosensor for the detection of 10 nM of immunoglobulin G (IgG) exhibited a greater response with microplasmonic materials compared to conventional thin Au films. Hence, these novel plasmonic materials exhibit a strong potential as an SPR sensing platform. They can be implemented on existing instrumentation and use detection protocols developed for current SPR sensors.
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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) | 0.000 | 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 | 0.000 | 0.000 |
| 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".