Regenerable ZnO/GaAs Bulk Acoustic Wave Biosensor for Detection of Escherichia coli in “Complex” Biological Medium
Bibliographic record
Abstract
A regenerable bulk acoustic wave (BAW) biosensor is developed for the rapid, label-free and selective detection of Escherichia coli in liquid media. The geometry of the biosensor consists of a GaAs membrane coated with a thin film of piezoelectric ZnO on its top surface. A pair of electrodes deposited on the ZnO film allows the generation of BAWs by lateral field excitation. The back surface of the membrane is functionalized with alkanethiol self-assembled monolayers and antibodies against E. coli. The antibody immobilization was investigated as a function of the concentration of antibody suspensions, their pH and incubation time, designed to optimize the immunocapture of bacteria. The performance of the biosensor was evaluated by detection tests in different environments for bacterial suspensions ranging between 103 and 108 CFU/mL. A linear dependence between the frequency response and the logarithm of E. coli concentration was observed for suspensions ranging between 103 and 107 CFU/mL, with the limit of detection of the biosensor estimated at 103 CFU/mL. The 5-fold regeneration and excellent selectivity towards E. coli detected at 104 CFU/mL in a suspension tinted with Bacillus subtilis at 106 CFU/mL illustrate the biosensor potential for the attractive operation in complex biological media.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".