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Record W2050828167 · doi:10.1021/am400162h

Polythiophene Biosensor for Rapid Detection of Microbial Particles in Water

2013· article· en· W2050828167 on OpenAlexaff
Marie-Pier Plante, Ève Bérubé, Luc Bissonnette, Michel G. Bergeron, Mario Leclerc

Bibliographic record

VenueACS Applied Materials & Interfaces · 2013
Typearticle
Languageen
FieldEngineering
TopicBiosensors and Analytical Detection
Canadian institutionsCentre hospitalier de l'Université LavalUniversité Laval
Fundersnot available
KeywordsBiosensorPolythiopheneMaterials scienceCationic polymerizationContaminationWater qualityPolymerNanotechnologyParticle (ecology)Environmental chemistryConductive polymerChemistryEcologyPolymer chemistryBiology

Abstract

fetched live from OpenAlex

Most microbial particles have a negatively charged surface and in this work, we describe a water quality monitoring application of a cationic polythiophene derivative (AH-35) for the rapid assessment of microbial contamination of water. Using E. coli as a prototype microbial particle, we demonstrate that the AH-35 polymer can provide a qualitative assessment of water if exposed to more than 500 CFU/mL, thereby paving the way to a new family of biosensors potentially useful for monitoring drinking water distribution systems.

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 score0.005
Threshold uncertainty score0.432

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 head0.007
GPT teacher head0.186
Teacher spread0.179 · 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

Citations36
Published2013
Admission routes1
Has abstractyes

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