Nanoparticle-Enhanced Acoustic Wave Biosensor Detection of Pseudomonas aeruginosa in Food
Bibliographic record
Abstract
A biosensor was designed for detecting Pseudomonas aeruginosa (P. aeruginosa) bacteria in whole milk samples. The sensing layer involved the antifouling linking molecule 3-(2-mercaptoethanoxy)propanoic acid (HS-MEG-COOH), which was covalently linked to an aptamer for binding P. aeruginosa. The aptasensor uses the thickness shear mode (TSM) system for mass-sensitive acoustic sensing of the bacterium. High concentrations (105 CFU mL−1) of nonspecific bacteria, E. coli, S. aureus, and L. acidophilus, were tested with the aptasensor and caused negligible frequency shifts compared to P. aeruginosa. The aptasensor has high selectivity for P. aeruginosa, with an extrapolated limit of detection (LOD) of 86 CFU mL−1 in phosphate-buffered saline (PBS) and 157 CFU mL−1 in milk. To improve the sensitivity of the sensor, gold nanoparticles (AuNPs) were functionalized with the same aptamer for P. aeruginosa and flowed through the sensor following bacteria, reducing the extrapolated LOD to 68 CFU mL−1 in PBS and 46 CFU mL−1 in milk. The frequency variations in the aptasensor are proportional to various concentrations of P. aeruginosa (102–105 CFU mL−1) with and without AuNPs, respectively. The low and rapid mass-sensitive detection demonstrates the ability of the aptasensor to quantitatively identify bacterial contamination in buffer and milk.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 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".