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Record W2042492459 · doi:10.1088/0957-4484/25/5/055208

Metallic nanoparticles functionalizing carbon nanotube networks for gas sensing applications

2014· article· en· W2042492459 on OpenAlexfundno aff
Ahmed Abdelhalim, Alaa Abdellah, Giuseppe Scarpa, Paolo Lugli

Bibliographic record

VenueNanotechnology · 2014
Typearticle
Languageen
FieldMaterials Science
TopicCarbon Nanotubes in Composites
Canadian institutionsnot available
FundersUniversity of Alberta
KeywordsMaterials scienceSurface modificationCarbon nanotubeEvaporationNanoparticleSurface plasmon resonanceMetalFabricationNanotechnologyChemical engineeringEthanolDeposition (geology)Organic chemistryMetallurgy

Abstract

fetched live from OpenAlex

We report the fabrication of carbon nanotube (CNT) based gas sensors functionalized with different metallic nanoparticles (NPs) (Au, Pd, Ag) with exceptionally high responses towards four test gases (NH3, CO2, CO and ethanol). The CNT networks were fabricated through a low cost spray deposition process while the NPs were deposited by a thermal evaporation process. CNT based gas sensors functionalized with Au with a nominal thickness of 1.0 nm showed superior response towards NH3, CO and ethanol. The sensors' normalized responses reached 92%, 22% and 32% with concentrations of 100 ppm, 50 ppm and 100 ppm for NH3, CO and ethanol respectively. CNT based gas sensors functionalized with Pd with a nominal thickness of 1.5 nm showed the best performance with CO2. The normalized response reached 3%, 6%, 12% and 17% with concentrations of 500 ppm, 1000 ppm, 2500 ppm and 5000 ppm of CO2 respectively. We also investigated the morphological and optical changes that occur to the NPs upon thermal treatment. Functionalization of CNT films deposited on glass with Au and Ag showed surface plasmon resonance effects that are dependent on the nominal thickness of the functionalization layer.

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 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)0.001
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 score0.117
Threshold uncertainty score0.960

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.012
GPT teacher head0.240
Teacher spread0.228 · 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 teacher head, 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

Citations80
Published2014
Admission routes1
Has abstractyes

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