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Record W2339156242 · doi:10.1116/1.4945804

Multiwalled carbon nanotube and graphene–polystyrene nanocomposites for bolometric detection

2016· article· en· W2339156242 on OpenAlexafffund
Ibrahim El-chami, Oberon Dixon-Luinenburg, Behraad Bahreyni

Bibliographic record

VenueJournal of Vacuum Science & Technology B Nanotechnology and Microelectronics Materials Processing Measurement and Phenomena · 2016
Typearticle
Languageen
FieldMaterials Science
TopicTransition Metal Oxide Nanomaterials
Canadian institutionsUniversity of WaterlooSimon Fraser University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMaterials scienceGrapheneCarbon nanotubePolystyreneNanocompositeBolometerFabricationResistorNanotechnologyInfraredPolymerCarbon fibersOptoelectronicsComposite materialOpticsComposite numberDetector

Abstract

fetched live from OpenAlex

In this paper, the authors report on the use of multilayered carbon nanotubes and graphene films for the fabrication of temperature sensitive resistors. Multiwalled carbon nanotubes- and graphene-based films possess moderate temperature coefficients of resistance and wideband infrared absorption efficiency. This work demonstrates that by incorporating these nanoparticles into a thermally and electrically insulating polymer, polystyrene, the temperature sensitivity significantly improves. Experimental results show temperature coefficients of resistances for these films as high as −0.7%/K. The layers were deposited using a spraying setup without further chemical or thermal treatment. The low cost, simple, and versatile deposition process, in addition to the high temperature coefficients of resistances for these films, makes them suitable alternatives for infrared image sensors as well as many other sensing applications.

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)0.004
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.037
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0010.002
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.011
GPT teacher head0.224
Teacher spread0.213 · 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.

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
Published2016
Admission routes2
Has abstractyes

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