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Record W4206298237 · doi:10.1088/1361-6528/ac4c16

Experiment and simulation of flexible CNT/SA/PDMS electromagnetic shielding composite

2022· article· en· W4206298237 on OpenAlexaff
Jingjing Pang, Ying Chen, Jiaqi Li, Shen Gong, Lei Xing, Chanyuan Wu, Zheng Zhu, Zhou Li

Bibliographic record

VenueNanotechnology · 2022
Typearticle
Languageen
FieldMaterials Science
TopicElectromagnetic wave absorption materials
Canadian institutionsYork University
FundersNational Defense Pre-Research Foundation of ChinaNatural Science Foundation of Hunan ProvinceChina Postdoctoral Science FoundationNatural Science Foundation of Hainan ProvinceNational Natural Science Foundation of China
KeywordsMaterials scienceComposite materialCarbon nanotubeElectromagnetic shieldingComposite numberPolydimethylsiloxaneNanoparticlePercolation thresholdConductivityElectrical resistivity and conductivityNanotechnology

Abstract

fetched live from OpenAlex

Flexible electromagnetic shielding composites have a great potential for wide range applications. In this study, two flexible composites were produced by plating Ni nanoparticles on carbon nanotubes (CNTs) or infiltrating carbon nanofibers/polydimethylsiloxane (CNF/PDMS) polymer into CNT/sodium alginate (CNT/SA) sponge skeleton (CNT/SA/CNF/PDMS composites). The composites are tested under the X band in the frequency range of 8.2 - 12.4 GHz, the electromagnetic interference shielding effectiveness (EMI-SE) values of the above two composites are almost as twice as that of CNT/SA/PDMS composite at a same CNT loading. Introducing nano-sized Ni particles on CNT improved the microwave absorption capacity of the composite, while adding CNF on the PDMS matrix enhanced the conductivity of these composites. Under 10% strain, both flexible composites show stable conductivity. Simulation and calculation results shown that increasing the cladding rate of Ni nanoparticles on the surface of CNT, reducing the average size of Ni particles, and increasing the loading of CNF in PDMS matrix can significantly improve conductivity and then EMI performance of the materials. All of these could benefit for the design of flexible electromagnetic shielding composites.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
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 score0.003
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

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)0.0030.000

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 head0.013
GPT teacher head0.260
Teacher spread0.247 · 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

Citations4
Published2022
Admission routes1
Has abstractyes

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