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Record W2335261231 · doi:10.1109/nano.2014.6968021

Analysis of sprayed Carbon nanotube films on rigid and flexible substrates

2014· article· en· W2335261231 on OpenAlexafffund
Valentina Robbiano, Alaa Abdellah, Luca Santarelli, Aniello Falco, Sara El-Molla, Lyubov V. Titova, David N. Purschke, Frank A. Hegmann, Franco Cacialli, Paolo Lugli

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMaterials Science
TopicCarbon Nanotubes in Composites
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaDeutsche ForschungsgemeinschaftEuropean CommissionAlberta Innovates - Technology Futures
KeywordsCarbon nanotubeMaterials sciencePhotoconductivitySubstrate (aquarium)Deposition (geology)PicosecondComposite materialThin filmWork functionSpectroscopyMorphology (biology)OptoelectronicsCarbon fibersCarbon filmNanotechnologyChemical engineeringOpticsLayer (electronics)

Abstract

fetched live from OpenAlex

We report on the morphological, electrical and optical characteristics of Carbon nanotube (CNT) films on different transparent substrates, obtained using spray deposition. The effect of different substrate materials on the characteristics of spayed CNT films with varying thickness is investigated. While film morphology is shown to depend on the underlying surface characteristics, no significant changes in work function are observed. These results are compared to films deposited onto flexile substrates. Further, time-resolved THz spectroscopy reveals picosecond transient photoconductivity dynamics in sprayed CNT films on flexible PET substrates.

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 communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.010
GPT teacher head0.244
Teacher spread0.234 · 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

Citations0
Published2014
Admission routes2
Has abstractyes

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