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Record W2751336004 · doi:10.1039/c7nr03674d

Nanoelectromagnetic of the N-doped single wall carbon nanotube in the extremely high frequency band

2017· article· en· W2751336004 on OpenAlexafffund
Brahim Aïssa, Mourad Nedil, Jens Kroeger, Mohammad Istiaque Hossain, Khaled A. Mahmoud

Bibliographic record

VenueNanoscale · 2017
Typearticle
Languageen
FieldMaterials Science
TopicElectromagnetic wave absorption materials
Canadian institutionsInstitut National de la Recherche Scientifique
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsCarbon nanotubeDopingMaterials scienceNanotubeNanotechnologyCarbon nanotube quantum dotOptoelectronics

Abstract

fetched live from OpenAlex

Materials offering excellent mechanical flexibility, high electrical conductivity and electromagnetic interference (EMI) attenuation with minimal thickness are in high demand, particularly if they can be easily processed into films. Carbon nanotube films deposited on a PDMS substrate combine these requirements. In this work, the potential of single wall carbon nanotubes (SWCNT) deposited on flexible polydimethylsiloxane (PDMS) polymer substrates for EMI attenuation is demonstrated. A 6-micrometer-thick SWCNT film exhibits EMI shielding effectiveness of 24.5 decibels in the extreme high frequency band (EHF), reaching 40 decibels when the SWCNTs are N-doped, which is one of the highest specific EMI attenuation performances optimized with film thickness realized to date. This performance stems from the good electrical conductivity of N-SWCNT films (150 Siemens per centimeter) and possible internal multireflections within the SWCNTs network. The excellent mechanical flexibility and easy coating processing enable them to sheathe complex shaped surfaces while providing high electromagnetic interference attenuation efficiency.

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 score0.097
Threshold uncertainty score0.538

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 science0.0020.000
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 head0.018
GPT teacher head0.232
Teacher spread0.214 · 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

Citations8
Published2017
Admission routes2
Has abstractyes

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