Bacteriophage-Modified Microarrays for the Direct Impedimetric Detection of Bacteria
Bibliographic record
Abstract
A novel method is presented for the specific and direct detection of bacteria using bacteriophages as recognition receptors immobilized covalently onto functionalized screen-printed carbon electrode (SPE) microarrays. The SPE networks were functionalized through electrochemical oxidation in acidic media of 1-ethyl-3-(3-dimethylaminopropyl)carbodiimide (EDC) by applying a potential of +2.2 V to the working electrode. Immobilization of T4 bacteriophage onto the SPEs was achieved via EDC by formation of amide bonds between the protein coating of the phage and the electrochemically generated carboxylic groups at the carbon surface. The surface functionalization with EDC, and the binding of phages, was verified by time-of-flight secondary ion mass spectrometry. The immobilized T4 phages were then used to specifically detect E. coli bacteria. The presence of surface-bound bacteria was verified by scanning electron and fluorescence microscopies. Impedance measurements (Nyquist plots) show shifts of the order of 10(4) Omega due to the binding of E. coli bacteria to the T4 phages. No significant change in impedance was observed for control experiments using immobilized T4 phage in the presence of Salmonella. Impedance variations as a function of incubation time show a maximum shift after 20 min, indicating onset of lysis, as also confirmed by fluorescence microscopy. Concentration-response curves yield a detection limit of 10(4) cfu/mL for 50-microL samples.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".