Handheld nanohole array surface plasmon resonance sensing platform
Bibliographic record
Abstract
Extraordinary optical transmission through nanohole arrays in metal films shows enhanced performance in surface plasmon resonance sensing, and efforts to develop this technology have been undertaken by many research groups worldwide. The challenge is to integrate a nanohole array sensor into a handheld design that is compact, cost effective, and capable of multiplexing. A number of implementations have been suggested, using components such as lasers and spectrometers, but these designs are often bulky, expensive and unacceptably noisy. We have developed an approach that is simple, inexpensive and reliable: an integrated handheld SPR imaging sensing platform using the nanohole array chip as the sensing element, a two-color LED source for spectral diversity, and a CCD module for multiplexed detection. A PDMS microfluidic chip made by conventional photolithographic techniques is assembled with the nanohole arrays and incorporated into the integrated module in order to transport the testing solutions, which offers the flexibility for future multiplexing. Results of preliminary tests show surface binding detection and have been promising.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.005 |
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".