Conductometric Immunosensor for Escherichia coli O157:H7 Detection Based on Polyaniline/Zinc Oxide (PANI/ZnO) Nanocomposite
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 conductometric immunosensor was developed for the detection of one of the most common foodborne pathogens, Escherichia coli O157:H7 (E. coli O157:H7), by conductometric sensing. The sensor was built based on a polyaniline/zinc oxide (PANI/ZnO) nanocomposite film spin-coated on a gold electrode. Then, it was modified with a monoclonal anti-E. coli O157:H7 antibody as a biorecognition element. The fabricated nanostructured sensor was able to quantify the pathogens under optimal detection conditions, within 30 min, and showed a good detection range from 101 to 104 CFU/mL for E. coli O157:H7 and a minimum detection limit of 4.8 CFU/mL in 0.1% peptone water. The sensor efficiency for detecting bacteria in food matrices was tested in ultra-heat-treated (UHT) skim milk. E. coli O157:H7 was detected at concentrations of 101 to 104 CFU/mL with a minimum detection limit of 13.9 CFU/mL. The novel sensor was simple, fast, highly sensitive with excellent specificity, and it had the potential for rapid sample processing. Moreover, this unique technique for bacterial detection could be applicable for food safety and quality control in the food sector as it offers highly reliable results and is able to quantify the target bacterium.
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.
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 | 0.001 |
| 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