Nanoparticle-Enhanced Acoustic Wave Biosensor Detection of Pseudomonas aeruginosa in Food
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
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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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 | 0.001 |
| 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