Rapid, Selective, and Ultra-Sensitive Electronic Field Effect Detection of <em>Escherichia coli</em>
Bibliographic record
Abstract
We report the detection of live Escherichia coli (E. coli) in liquid samples using a biosensor based on a field effect transistor (FET) biosensor with B/N co-coped graphene oxide (GO) gel (BN-GO) as the transducer material. The FET was functionalized with antibodies to detect E. coli K12 O-antigens in phosphate-buffered saline (PBS). The biosensor detected the presence of planktonic E. coli bacterial cells within a mere 2 minutes. The biosensor exhibited a limit of detection (LOD) of 10 cells per sample, which can be extrapolated to a limit of detection at the level of a single cell per sample and a detection range of at least 10&ndash; 108 CFU/mL. The selectivity of the biosensor for E. coli was demonstrated using Bacillus thuringiensis (B. thuringiensis) as a sample contaminant. We also present a comparison of our functionalized BN-GO FET biosensor with established detection methods of E. coli k12 bacteria, as well as with state-of-the-art detection mechanisms.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| 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".