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Record W2116673553 · doi:10.1109/icsens.2007.4388482

A Low Voltage Gas Ionization Sensor based on Sparse Gold Nanorods

2007· article· en· W2116673553 on OpenAlexafffund
Ramin Banan Sadeghian, Mojtaba Kahrizi

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicGas Sensing Nanomaterials and Sensors
Canadian institutionsConcordia University
FundersMcGill University
KeywordsNanorodIonizationCathodeAnalytical Chemistry (journal)Materials scienceNanotechnologyPhysicsChemistryPhysical chemistryOrganic chemistryIon

Abstract

fetched live from OpenAlex

We report fabrication and characterization of a gas ionization sensor using a sparse array of vertically aligned gold nanorods (AuNR). Our device displayed improved sensitivity compared to the carbon nanotube (CNT) film counterpart, since the room temperature breakdown voltages (Vb) of the tested gases were further reduced. With AuNRs configured as the cathode, Vbwas unaffected by gas pressure within a wide range (10-5≪ P ≪ 0.1 torr), suggesting that the breakdown process does not follow Townsend's criterion. At higher and lower pressures, Vbincreased. The field-emission (FE) and field-ionization (FI) properties of the AuNRs were also studied, and the latter showed measurable pressure dependency in a certain voltage range. The lowering of Vbas well as enhanced FE and FI actions, were attributed to the high field-amplification factor of atomically sharp features on AuNR tips covered by a thin alumina scale.

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 score0.002

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 integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.008
GPT teacher head0.196
Teacher spread0.187 · 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

Citations16
Published2007
Admission routes2
Has abstractyes

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