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 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".