Rapid Detection of E. coli Bacteria Using Potassium-Sensitive FETs in CMOS
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 novel integrated system for the detection of live bacteria in less than 10 minutes is presented. It utilizes the specificity of bacteriophages as biological detection elements with the sensitivity of integrated ion-selective field-effect transistors (ISFETs) implemented in conventional 0.18 μm CMOS with additional post-processes PVC-based potassium-sensitive membrane to provide a rapid, low-cost bacteria detection platform. Experimental methods to cancel ISFET non-idealities as well as data processing techniques to enhance detection capability of the bacteria sensor are demonstrated. Three groups of experimental results are provided using four strains of E. coli with two bacteriophages at two different temperatures. Measurements incorporating positive and negative control experiments are presented that successfully exhibit sensor specificity as well detection capability.
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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 | 0.000 |
| 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 it