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Record W3094906062 · doi:10.14447/jnmes.v23i3.a02

Enterotoxigenic Escherichia Coli Detection Using the Design of a Biosensor

2020· article· en· W3094906062 on OpenAlexvenueno aff
V. Lakshman Narayana, Anjana Gopi

Bibliographic record

VenueJournal of New Materials for Electrochemical Systems · 2020
Typearticle
Languageen
FieldEngineering
TopicAdvanced Chemical Sensor Technologies
Canadian institutionsnot available
Fundersnot available
KeywordsMicroelectrodePlasma-enhanced chemical vapor depositionMaterials scienceBiosensorBiocompatibilityEscherichia coliEnterotoxigenic Escherichia coliNanotechnologySilaneOptoelectronicsThin filmChemistryElectrodeComposite materialBiochemistry

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.229
Threshold uncertainty score0.477

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.034
GPT teacher head0.243
Teacher spread0.209 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2020
Admission routes1
Has abstractyes

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