Enhanced SPR sensing based on micro-patterned thin films
Bibliographic record
Abstract
Micro-patterned thin films interrogated in the Kretschmann configuration of SPR could extend the detection range to lower concentrations and small biomolecules due to a greater sensitivity. This was achieved with the same instrumentation and analysis methodologies developed for SPR with continuous films. The plasmonic properties of micro-patterned thin films composed of various layers of Ag and Au were investigated to find an optimal structure for biosensing application. The analytical parameters and biosensing performances were also evaluated for analysis of biological samples. Au microhole arrays of 3.2 μm periodicity and 1.6 μm hole diameter were prepared using a modified nanosphere lithography (NSL) technique. These microstructures showed optimal plasmonic properties for biosensing applications as they exhibit a 50% increase in sensitivity to refractive index changes compared to continuous thin films of the same thickness. Moreover, microhole arrays presented a faster response time to refractive index changes while analytical parameters such as the resolution and the noise in biosensing measurement were comparable to continuous films. When combined to the appropriate surface chemistry, a greater SPR response was measured for proteins using microhole arrays. Although microhole arrays required an additional preparation step, a cleaning step using oxygen plasma allowed multiple measurements with the same metallic surface with great repeatability. Hence, microhole arrays proved to be a simple approach to improve current SPR biosensing technique. Further investigations to understand the plasmonic properties of microhole were performed using an angle scanning SPR instrument.
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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 | 0.001 |
| Open science | 0.001 | 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".