96-Well Plasmonic Sensing with Nanohole Arrays
Bibliographic record
Abstract
Abstract A multiwell plasmonic reader was designed and validated for higher throughput analysis of biological interactions with a platform of the same size as standard 96-well plates. While the plasmonic sensor can be read with standard 96-well plate readers, a custom 96-well plate reader was designed to analyze nanohole arrays at high incident angles required for higher sensitivity. Gold nanohole arrays were manufactured on a 4 in. glass wafer using a photolithographic process. In comparison to single channel measurements with nanohole arrays fabricated with nanosphere lithography, the nanohole array sensors greatly enhanced the signal-to-noise ratio of the plasmonic signal and precision of the measurements with the multiwell plate system. As proof of concept, the detection of IgG in the low nanomolar range was achieved with the multiwell plate reader. The multiwell plasmonic plate reader was also applied to the screening of several prostate specific (PSA) antibodies for secondary detection of PSA and for the analysis of an anticancer drug through a competitive assay between methotrexate (MTX) and folic acid Au nanoparticle (FaNP) for human dihydrofolate reductase (hDHFR). The multiwell plasmonic reader based on nanohole array technology offers the rapid, versatile, sensitive, and simple high throughput detection of biomolecules.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.008 |
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".