A Cell-Imprinted Polymer Capacitive Biosensor for the Detection of Escherichia coli
Bibliographic record
Abstract
Food-borne bacteria contamination in food and water has become an important topic for food industries as well as common people. Escherichia coli is one of the common infectious bacteria and the maximum acceptance of E. coli in drinking water in Canada is 0/100 mL. Current traditional detection techniques such as ELISA and PCR are time-consuming, tedious and expensive. To develop more rapid and effective detection methods, several studies had reported the preference of cell imprinting technique in the biosensor applications (2,4). However, the sensitivity and the accessibility of the biosensors remain a challenge. In this regard, we have developed a capacitive biosensor based on a layer by layer, boronic acid doped polyaniline methacrylic based molecular imprinted polymers (MIPs) on a carbon nanotube conductive film. The tailor-made MIPs have been found to be highly selective affinity to E. coli. Highly miniaturized (the size of 1.5 x 1.5 cm2) and inexpensive, the fabricated sensor schematically depicted below is currently being hyphenated to cellular phones through a blue tooth, can be deployed for on site remote analysis of pathogens in drinking water and food analysis. The sensor performance was impressive with a wide dynamic range of four orders of magnitude and low limit of detection of 36 CFU/mL for E. coli detection. Moreover, by simply tailoring the molecular receptors, the sensor can be employed in the detection of other pathogenic bacteria such as Staphylococcus spp. and Salmonella spp. in food and water.
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 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 | 0.001 | 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 | 0.000 |
| Research integrity | 0.002 | 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".