Gold nanostructure-integrated silica-on-silicon waveguide for the detection of antibiotics in milk and milk products
Bibliographic record
Abstract
Antibiotics are extensively used in veterinary medicine for the treatment of infectious diseases. The use of antibiotics for the treatment of animals used for food production raised the concern of the public and a rapid screening method became necessary. A novel approach of detection of antibiotics in milk is reported in this work by using an immunoassay format and the Localized Surface Plasmon Resonance property of gold. An antibiotic from the penicillin family that is, ampicillin is used for testing. Gold nanostructures deposited on a glass substrate by a novel convective assembly method were heat-treated to form a nanoisland morphology. The Au nanostructures were functionalized and the corresponding antibody was absorbed from a solution. Solutions with known concentrations of antigen (antibiotics) were subsequently added and the spectral changes were monitored step by step. The Au LSPR band corresponding to the nano-island structure was found to be suitable for the detection of the antibody antigen interaction. The detection of the ampicillin was successfully demonstrated with the gold nano-islands deposited on glass substrate. This process was subsequently adapted for the integration of gold nanostructures on the silica-on-silicon waveguide for the purpose of detecting antibiotics.
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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 | 0.000 |
| 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".