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Record W1969420289 · doi:10.1049/iet-cds:20070162

Analytic modelling of biotransistors

2008· article· en· W1969420289 on OpenAlexafffund
M. Waleed Shinwari, M. Jamal Deen, P. Ravi Selvaganapathy

Bibliographic record

VenueIET Circuits Devices & Systems · 2008
Typearticle
Languageen
FieldChemical Engineering
TopicAnalytical Chemistry and Sensors
Canadian institutionsMcMaster University
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsNoise (video)Emphasis (telecommunications)VoltageTransistorSIGNAL (programming language)Computer scienceField-effect transistorField (mathematics)Electronic engineeringElectrical engineeringMathematicsEngineeringTelecommunicationsArtificial intelligence

Abstract

fetched live from OpenAlex

The possibility of using devices based on field-effect principles for detecting DNA hybridisation events is an attractive and low-cost alternative to optical reading techniques. Experiments have shown that a change in the threshold voltage of a few millivolts can be attributed to successful hybridisation of targets to probe oligonucleotides tethered on the oxide of a field-effect transistor. Many different phenomena give rise to this voltage shift and some of these phenomena are described. Several justifiable approximations are made to develop an analytic solution of this biosensor that allow for a closed-form expression of the flatband voltage to be derived. Finally, a small-signal model for the BioFET is given, with emphasis on its design and operating conditions for optimum signal-to-noise ratio.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.004
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0020.001
Open science0.0020.001
Research integrity0.0030.001
Insufficient payload (model declined to judge)0.0040.002

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 head0.053
GPT teacher head0.225
Teacher spread0.171 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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

Citations16
Published2008
Admission routes2
Has abstractyes

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