Biosensors based on the plasmonic properties of Au microhole arrays
Bibliographic record
Abstract
The plasmonic properties of metallic nanoparticles and macroscopic Au film have been thoroughly investigated for the development of biosensors based on surface plasmon resonance (SPR). Nanoparticle based localized surface plasmon resonance (LSPR) is a technique extremely sensitive to molecular adsorbate, whilst conventional SPR based on the Kretschmann configuration (macroscopic smooth Au film) is especially sensitive to bulk refractive index. SPR currently provides the best RI resolution, a measure typically used for comparison of the potential of plasmonic sensor. A technique that could combine high bulk refractive index resolution and high sensitivity to molecular adsorbate would increase the scope of SPR-based technique by providing lower detection limits. A potential solution may exploit micro-structured Au films. However, the plasmonic properties of micropatterned metallic films are still relatively unknown. We have undertaken the study of the plasmonic properties from Au film with features on the order of 1 to 3 μm. Microtriangle and microhole arrays were fabricated by modified nanosphere lithography, consisting of a polymer microsphere mask deposited in a close-packed hexagonal monolayer, etched by oxygen plasma. Etch time controls the diameter of the microhole and the initial microsphere diameter sets the periodicity. Investigation of the SPR properties in the Kretschmann configuration was undertaken using a SPR with a dove prism and a multi-wavelength scanning angle SPR. The sensitivity of SPR with microhole arrays exhibits an improvement by a factor of 3 in comparison to SPR using a smooth Au film. This is accomplished by tuning the angle to near 73 degrees (with a BK7 glass prism). Moreover, the sensitivity to the immobilization of an antibody was improved by at least a factor of 4 as demonstrated with the kinetics of immobilization for IgY, without employing secondary amplification techniques. No modification to the instrumentation is required and microhole arrays improve resolution of the SPR response.
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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 | 0.001 |
| 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 | 0.001 | 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".