Biosensing using straight long-range surface plasmon waveguides
Bibliographic record
Abstract
Straight long-range surface plasmon waveguides are demonstrated as biosensors for the detection of cells, proteins and changes in the bulk refractive index of solutions. The sensors consist of 5 μm wide 22 nm thick Au stripes embedded in polymer (CYTOP™) with microfluidic channels etched into the top cladding. Bulk sensing is demonstrated by sequentially injecting six solutions of different refractive indices in 2 × 10(-3) RIU increments; such index steps were detected with a signal-to-noise ratio of ~1000. Selective capture of cells is demonstrated using Au waveguides functionalized with antibodies against blood group A, and red blood cells of group A and O in buffer as positive and negative analyte. Bovine serum albumin in buffer was used to demonstrate protein sensing. A monolayer of bovine serum albumin physisorbed on a carboxyl-terminated self-assembled monolayer on Au was detected with a signal-to-noise ratio of ~300. Overall, the biosensor demonstrated a good capability for detecting bulk changes in solution and for sensing analyte over a very wide range of mass (from cells to proteins). The biosensors are compact, inexpensive to fabricate, and may find use over a wide range of cost-sensitive sensing and detection applications.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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; 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".