Plasmonic Nanopipette Biosensor
Bibliographic record
Abstract
Integrating a SERS immunoassay on a plasmonic "patch clamp" nanopipette enabled nanobiosensing for the detection of IgG. A SERS response was obtained using a sandwich assay benefiting from plasmon coupling between a capture Au nanoparticle (AuNP) on a nanotip and a second AuNP modified with a Raman active reporter and an antibody selective for IgG. The impact of nanoparticle shape and surface coverage was investigated alongside the choice of Raman active reporter, deposition pH, and plasmonic coupling, in an attempt to fully understand the plasmonic properties of nanopipettes and to optimize the nanobiosensor for the detection of IgG. These probes will find applications in various fields due to their nanoscale size leading to the possibility of spatially and temporally addressing their location near cells to monitor secretion of biomolecules.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
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; both teacher heads agree on what is shown here.
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".