A functionalized microwave biosensor for rapid, reagent-free detection of E. coli in water samples
Bibliographic record
Abstract
Escherichia coli (E. coli) O157:H7 (O157), one of the most common Shiga toxin-producing E. coli, can contaminate water systems causing severe illnesses often accompanied with diarrhea and sometimes life threatening. Frequent monitoring of E. coli in water systems is critical to protect public health. Most traditional methods for E. coli detection are slow in responding to E. coli outbreaks due to the need for sample transportation from the site to the lab, expensive equipment, and highly trained personnel for the detection. This work presents a novel reagent-free detection method that employs a microwave biosensor functionalized with an antibody specific to E. coli to offer rapid and sensitive E. coli detection. By monitoring the resonance frequency shift caused by the binding between the E. coli in the water sample and the antibody coated on the sensor using a vector network analyzer (VNA), this microwave-based biosensor achieved a limit of detection (LOD) of 647 CFU/ml. This LOD can be further reduced to 6.47 CFU/ml with a simple preconcentration step prior to the sensing procedure. The sensor has also been tested to detect E. coli in natural water systems with a low-cost, palm-sized portable VNA, suggesting its excellent feasibility for real-time on-site E.coli detection.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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 | 0.000 | 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 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".