MétaCan
Menu
Back to cohort
Record W2002134930 · doi:10.1115/micronano2008-70218

High Sensitive Sensor for Micro and Nano Particles Detection Based on DeFET

2008· article· en· W2002134930 on OpenAlexaff
Mohamed F. Ibrahim, Fahmi Elsayed, Yehya H. Ghalab, Wael Badawy

Bibliographic record

Venue2008 Second International Conference on Integration and Commercialization of Micro and Nanosystems · 2008
Typearticle
Languageen
FieldEngineering
TopicGas Sensing Nanomaterials and Sensors
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsElectric fieldNano-TransistorField (mathematics)Computer scienceLogic gateElectrical engineeringNanotechnologyElectronic engineeringMaterials scienceOptoelectronicsEngineeringPhysicsVoltage

Abstract

fetched live from OpenAlex

This paper presents a new configuration of an electric field sensor, named “Differential Electric-Field Sensitive Field Effect Transistor” (DeFET). The new configuration which has different gate distances of DeFET is designed to detect tiny particles. The new design can be used in environmental applications such as one can detect carbon dioxide concentration in air. This paper also reviews the DeFET’s theory of operation and presents simulation which confirms the basic idea of the DeFET.

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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

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 communication0.0000.001
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 head0.025
GPT teacher head0.228
Teacher spread0.203 · 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

Citations1
Published2008
Admission routes1
Has abstractyes

Explore more

Same venue2008 Second International Conference on Integration and Commercialization of Micro and NanosystemsSame topicGas Sensing Nanomaterials and SensorsFrench-language works237,207