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Record W7116849533 · doi:10.1080/10667857.2025.2602641

Absorption-dominant shielding effectiveness of carbon-based nanocomposites with enhanced dielectric and magnetic losses

2025· article· en· W7116849533 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueMaterials Technology · 2025
Typearticle
Languageen
FieldMaterials Science
TopicElectromagnetic wave absorption materials
Canadian institutionsArtificial Intelligence in Medicine (Canada)
Fundersnot available
KeywordsDielectricNanocompositeElectromagnetic shieldingDielectric lossPermittivityMagnetic field

Abstract

fetched live from OpenAlex

In this study, a series of novel Polyvinyl Alcohol (PVA) polymeric carbon-based composites is proposed for blocking EMI signals to protect sensitive electronic equipment in aircraft control systems. Carbon-based materials without the combination of any metals or ferromagnetic materials are used in the research work. Graphitised nanodiamond (GND), Graphene nanoplatelets (GNPs) and Multiwalled carbon nanotubes (MWCNTs) were uniformly dispersed in PVA matrix, and thin films were fabricated using solution casting method. A Vector Network Analyzer (VNA) operating in X band was utilised for EMI shielding studies. Among the fabricated thin films, the sample 3 in the ratio of 50:5:37.5:7.5 (PVA: MWCNT: GNP: GND) display the highest EMI shielding effectiveness of 46.2 dB. The shielding effectiveness of the samples was verified using COMSOL Multiphysics software. Morphological characterization results were obtained from Scanning Electron Microscopy. Nicolson Ross Weir (NRW) algorithm was used to extract dielectric and magnetic losses.

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.

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 score0.005
Threshold uncertainty score0.982

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
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 head0.003
GPT teacher head0.218
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