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Record W2086974201 · doi:10.1063/1.3050329

Sensitivity of field-effect biosensors to charge, pH, and ion concentration in a membrane model

2008· article· en· W2086974201 on OpenAlexaff
W. R. McKinnon, D. Landheer, G. C. Aers

Bibliographic record

VenueJournal of Applied Physics · 2008
Typearticle
Languageen
FieldChemical Engineering
TopicAnalytical Chemistry and Sensors
Canadian institutionsInstitute for Microstructural Sciences
Fundersnot available
KeywordsBiomoleculeBiosensorSurface chargeIonSensitivity (control systems)ChemistryMembraneField-effect transistorMoleculePoisson's equationPoisson–Boltzmann equationCharged particleCharge (physics)Chemical physicsOxideAnalytical Chemistry (journal)TransistorMaterials scienceNanotechnologyPhysical chemistryPhysicsChromatographyVoltageElectronic engineering

Abstract

fetched live from OpenAlex

In field-effect transistors used to detect charged biomolecules (BioFETs), the biomolecules form a charged membrane on the transistor surface. In this paper, the one-dimensional Poisson–Boltzmann equation is used to calculate the charge sensitivity (the sensitivity of the BioFET to changes in biomolecule charge), ion sensitivity (to changes in ion concentration of the solution), or pH sensitivity (to changes in pH of the solution), both analytically and numerically, and the results are compared to models where the charged molecules are represented as an infinitely thin plane. Complexation of ions with the oxide surface is shown to have a negligible effect on parameters typical of devices, but the layer used to tether the charged molecules to the surface could modify the sensitivity considerably.

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)0.001
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
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 head0.012
GPT teacher head0.225
Teacher spread0.213 · 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 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

Citations28
Published2008
Admission routes1
Has abstractyes

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