Biosensing using straight long-range surface plasmon waveguides
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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 it