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Record W2012835664 · doi:10.1063/1.4918959

Thermal conductivity prediction of magnetic composite sheet for near-field electromagnetic absorption

2015· article· en· W2012835664 on OpenAlexaff
Joonsik Lee, Baekil Nam, Frank Ko, Ki Hyeon Kim

Bibliographic record

VenueJournal of Applied Physics · 2015
Typearticle
Languageen
FieldMaterials Science
TopicElectromagnetic wave absorption materials
Canadian institutionsUniversity of British Columbia
FundersMinistry of Trade, Industry and Energy
KeywordsMaterials scienceThermal conductivityComposite materialComposite numberMagnetic nanoparticlesShell (structure)Magnetic fieldNanoparticleNanotechnology

Abstract

fetched live from OpenAlex

The magnetic composite sheets were designed by using core-shell structured magnetic fillers instead of uncoated magnetic fillers to resolve concurrently the electromagnetic interference and thermal radiation problems. To predict the thermal conductivity of composite sheet, we calculated the thermal conductivity of the uncoated magnetic fillers and core-shell structured fillers. And then, the thermal conductivity of the magnetic composites sheet filled with core-shell structured magnetic fillers was calculated and compared with that of the uncoated magnetic fillers filled in composite sheet. The magnetic core and shell material are employed the typical Fe-Al-Si flake (60 μm × 60 μm × 1 μm) and 250 nm-thick AlN with high thermal conductivity, respectively. The longitudinal thermal conductivity of the core-shell structured magnetic composite sheet (2.45 W/m·K) enhanced about 33.4% in comparison with that of uncoated magnetic fillers (1.83 W/m·K) for the 50 vol. % magnetic filler in polymer matrix.

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 score0.013
Threshold uncertainty score0.645

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 science0.0000.000
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 head0.023
GPT teacher head0.246
Teacher spread0.223 · 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

Citations2
Published2015
Admission routes1
Has abstractyes

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