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Record W3145668723 · doi:10.18494/sam.2009.549

Subtorr Operation of a Miniature Gas Ionization Sensor Based on Gold Nanowires

2009· article· en· W3145668723 on OpenAlex
Ramin Banan Sadeghian, Nicoleta Chivu, Mojtaba Kahrizi

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

VenueSensors and Materials · 2009
Typearticle
Languageen
FieldEngineering
TopicGas Sensing Nanomaterials and Sensors
Canadian institutionsConcordia University
FundersConcordia UniversityNatural Sciences and Engineering Research Council of CanadaMcGill University
KeywordsNanowireIonizationMaterials scienceNanotechnologyOptoelectronicsChemistryIon

Abstract

fetched live from OpenAlex

Vertically aligned freestanding gold nanowires (AuNWs) were synthesized and deposited on one of the electrodes of a capacitor-like gas ionization cell, whereas the counterelectrode was a polished silicon wafer coated with aluminum on both sides. The field enhancement property of high-aspect-ratio AuNWs was employed to reduce the gaseous breakdown voltage (V b ) at room temperature. The device was characterized in low-pressure air, and tested in subtorr argon where it demonstrated a considerable reduction in V b compared with uniform field conditions and with its earlier counterparts. The dependences of V b and prebreakdown currents on the polarity of applied voltage were studied. A particle-in-cell/Monte-Carlo-collision (PIC/MCC) model for the device was also developed to simulate the breakdown process within the same pressure range in which measurements were carried out. The simulated V b -P curve showed good agreement with the measured characteristics.

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 score0.004
Threshold uncertainty score0.677

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 head0.006
GPT teacher head0.189
Teacher spread0.184 · 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