Plasmonic sensors for the competitive detection of testosterone
Bibliographic record
Abstract
The ability to detect small molecules in a rapid and sensitive manner is of great importance in the field of clinical chemistry, and the advancement of novel biosensors is key to realising point-of-care analysis for essential targets. Testosterone is an example of such a small molecule, the detection of which is important in both clinical analysis, and in the sporting industry to prevent doping. As such, a portable, rapid and sensitive test for testosterone would be of great use across a variety of analytical fields. Here we report on a novel method of testosterone analysis, based on a competitive inhibition assay utilising functionalized gold nanoparticles. Two sensing platforms are directly compared for the detection of testosterone based on both classical SPR and LSPR. We provide an in-depth discussion on the optimum surface chemistries needed to create a stable detection conjugate before successfully detecting testosterone using our newly developed portable 4-channel SPR instrument. We provide the first detailed study into the comparison of SPR and LSPR for the analysis of a small molecule, and provide a simple and effective method of testosterone detection that could potentially be extended to a variety of different analytes.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".