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A New Field-Effect Transistor Based Sensor for Biosensing Applications

2022· article· en· W4292068810 on OpenAlexafffund
Abbas Panahi, Ebrahim Ghafar‐Zadeh

Bibliographic record

Venue2022 20th IEEE Interregional NEWCAS Conference (NEWCAS) · 2022
Typearticle
Languageen
FieldChemical Engineering
TopicAnalytical Chemistry and Sensors
Canadian institutionsYork University
FundersCMC Microsystems
KeywordsJFETBiosensorTransistorField-effect transistorMaterials scienceOptoelectronicsSiliconNanotechnologyElectrical engineeringEngineeringVoltage

Abstract

fetched live from OpenAlex

This paper presents a new field-effect based sensor for biosensing applications. The sensor composed of a new structure that is based on the junction field effect transistor or JFET that is a well-known electronic transistor. To prepare the JFET for biosensing and solution-based sensing, the p-type JFET is opened from one side (the n-type layer is removed) allowing applying a solution containing bio-chemical substances on top. The opened-gate area consists of silicon with a very thin layer of SiO2, which is naturally grown on silicon when exposed to the environment. The p-type channel length between a source and drain is about 100 μm and its thickness is 1.6 μm with 1000 μm wideness. A common source design is introduced containing seven channels that further improves the sensing area (≈ 0.7 mm2). To demonstrate the biomolecular sensing capability of the open-gate junction field-e ffect transistor (OG-JFET), dried-DNA is used. Experimental analysis showing a sensitivity of 30 μA/DNA-Concentration (ng/uL) for OG-JFET toward dry DNA.

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)0.000
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: Methods · Consensus signal: Methods
Teacher disagreement score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.035
GPT teacher head0.279
Teacher spread0.244 · 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
GenreMethods

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

Citations0
Published2022
Admission routes2
Has abstractyes

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