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Record W4401226671 · doi:10.1039/d4nr02654c

Multifunctional phase-change composites for green electromagnetic interference shielding and thermal response prepared under the guidance of an impedance matching strategy

2024· article· en· W4401226671 on OpenAlexaff
Jie He, Jiaozu Wu, Chul B. Park, Pengjian Gong, Chaobo Liang, Guangxian Li

Bibliographic record

VenueNanoscale · 2024
Typearticle
Languageen
FieldMaterials Science
TopicElectromagnetic wave absorption materials
Canadian institutionsUniversity of Toronto
FundersState Key Laboratory of Polymer Materials EngineeringNational Natural Science Foundation of China
KeywordsSchematicMaterials scienceElectromagnetic shieldingComposite numberComposite materialDissipationElectromagnetic interferenceImpedance matchingElectrical impedanceThermal management of electronic devices and systemsInterference (communication)Phase (matter)ThermalMechanical engineeringElectronic engineeringElectrical engineeringEngineeringPhysics

Abstract

fetched live from OpenAlex

) of 0.04. In CST simulations, the intrinsic mechanism of electromagnetic energy loss within the material is revealed by energy loss density cloud maps. In addition, heat from high-temperature objects is transferred through the highly thermally conductive AgNW-PI membrane to the long-channel Ni@C/r-GO backbone. Therefore, the composites prepared on the basis of impedance matching will accelerate the use of EMI shielding materials for the thermal management of portable electronic devices and battery heat dissipation packaging.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

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)0.0020.000

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 head0.036
GPT teacher head0.312
Teacher spread0.276 · 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 source (direct Gemma or distilled Codex), 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

Citations9
Published2024
Admission routes1
Has abstractyes

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