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Record W2774124825 · doi:10.1002/slct.201702080

Fabrication of Porous Silicon Carbide Ceramics with High Electromagnetic Interference Shielding Effectiveness

2017· article· en· W2774124825 on OpenAlexaff
Chenyu Liu, Zechao Qiu, Dawei Yu, Donald W. Kirk, Yongjun Xu

Bibliographic record

VenueChemistrySelect · 2017
Typearticle
Languageen
FieldMaterials Science
TopicElectromagnetic wave absorption materials
Canadian institutionsUniversity of Toronto
FundersChina Scholarship Council
KeywordsMaterials scienceSilicon carbidePorosityElectromagnetic shieldingCeramicFabricationComposite materialPorous siliconElectromagnetic interferenceMicrowaveScanning electron microscopeSiliconTransmission electron microscopyOptoelectronicsNanotechnologyElectronic engineering

Abstract

fetched live from OpenAlex

Abstract Porous SiC ceramics were synthesized for its potential electromagnetic interference (EMI) shielding application, with the utilization of a biomass material as the porous template and carbon precursor. A silicon infiltration process was conducted in the temperature range of 1400–1600 °C with the absence of any catalyst to produce the porous SiC ceramics. The porous SiC ceramics were characterized using scanning electron microscope, X‐ray diffraction and microwave vector network analyses. It was found that significantly higher EMI shielding effectiveness can be achieved using a higher silicon infiltration temperature. The porous SiC ceramics synthesized at 1600 °C possess the highest shielding value, reaching 60 dB at a high frequency range from 10 GHz to 18 GHz. It was proposed that the porous structure of the SiC can increase EM shielding performance by attenuating EM waves via multiple internal reflection and transmission of the incident and re‐reflected waves through the SiC walls of the porous network.

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 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 score0.048
Threshold uncertainty score0.900

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 science0.0010.000
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 head0.244
Teacher spread0.233 · 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

Citations7
Published2017
Admission routes1
Has abstractyes

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