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Record W2047347521 · doi:10.1109/tbcas.2013.2276013

Rapid Detection of E. coli Bacteria Using Potassium-Sensitive FETs in CMOS

2013· article· en· W2047347521 on OpenAlex
Nasim Nikkhoo, P.G. Gulak, Karen L. Maxwell

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueIEEE Transactions on Biomedical Circuits and Systems · 2013
Typearticle
Languageen
FieldChemical Engineering
TopicAnalytical Chemistry and Sensors
Canadian institutionsUniversity of Toronto
FundersCanadian Institutes of Health Research
KeywordsCMOSISFETBacteriaTransistorMaterials scienceField-effect transistorNanotechnologyOptoelectronicsPotassiumBiosensorVoltageElectrical engineeringBiologyEngineering

Abstract

fetched live from OpenAlex

A novel integrated system for the detection of live bacteria in less than 10 minutes is presented. It utilizes the specificity of bacteriophages as biological detection elements with the sensitivity of integrated ion-selective field-effect transistors (ISFETs) implemented in conventional 0.18 μm CMOS with additional post-processes PVC-based potassium-sensitive membrane to provide a rapid, low-cost bacteria detection platform. Experimental methods to cancel ISFET non-idealities as well as data processing techniques to enhance detection capability of the bacteria sensor are demonstrated. Three groups of experimental results are provided using four strains of E. coli with two bacteriophages at two different temperatures. Measurements incorporating positive and negative control experiments are presented that successfully exhibit sensor specificity as well detection capability.

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 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 score0.408
Threshold uncertainty score0.597

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 head0.023
GPT teacher head0.228
Teacher spread0.205 · 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