Enterotoxigenic Escherichia Coli Detection Using the Design of a Biosensor
Bibliographic record
Abstract
The food industry and clinical analysis, among other sectors, require the development of techniques and devices that detect pathogens, while the development of implantable devices needs biocompatible materials with low degradation in biological environment to increase the lifetime of the device. Throughout this work, hydrogenated amorphous silicon-carbon alloy is proposed, obtained, characterized and incorporated into the development of a proposed interdigitated microelectrode array (PIMA) to capture the bacteria of enterotoxigenic Escherichia coli (E. coli, ETEC). a-SixC1-x:H is obtained by the technique of plasma-enhanced chemical vapor deposition (PECVD) using methane and silane as precursor gases under high hydrogen dilution and low power density in order to improve its biocompatibility. Functionally the PIMA is a transducer based on electrical impedance, namely the capture of E. coli bacteria causes changes in the electrical properties of the medium between and on the microelectrodes of the array, which are associated with changes in electrical impedance. The simulations were made with the purpose of knowing the operation that the PIMA would have under operating conditions (with bacterial environment) and of analyzing the design aspects that could affect or increase the sensitivity of the array.
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 | 0.000 | 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".