MétaCan
Menu
Back to cohort
Record W4415014054 · doi:10.1002/admt.202501314

Synergistic EMI Shielding: The Role of Nanoparticle Size and Alternating Bi‐Layer Coatings in Hybrid Fabrics

2025· article· en· W4415014054 on OpenAlexaff
Hamed Mohammadi Mofarah, Mutalifu Abulikemu, Hyung Woo Choi, Mehr Khalid Rahmani, Jihane Karib, Ghassan E. Jabbour

Bibliographic record

VenueAdvanced Materials Technologies · 2025
Typearticle
Languageen
FieldMaterials Science
TopicElectromagnetic wave absorption materials
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsElectromagnetic shieldingEMIElectromagnetic interferenceCoatingNanoparticleParticle sizeAir permeability specific surface

Abstract

fetched live from OpenAlex

Abstract This study presents a hybrid knitted fabric engineered for high‐performance electromagnetic interference shielding effectiveness (EMI SE), achieving a maximum shielding effectiveness of 59.4 dB. The functionalized fabric is made from copper‐cotton core‐spun yarn with alternating bi‐layer of silver nanoparticles (AgNPs), MXene to enhance both conductivity and reflection/absorption. By employing a reactive in situ synthesis process, AgNPs are successfully synthesized directly onto the fabric, achieving particle sizes that range from 15 to 38 nm. Notably, smaller nanoparticles showed improved shielding performance by 7 dB, highlighting the important role of particle size in enhancing EMI shielding effectiveness. In addition, increasing the number of coating layers enhances the shielding effectiveness. In order to improve the durability of the fabric, (3‐Aminopropyl)triethoxysilane (APTES) treatment is performed, which contributed to the preservation of stable EMI shielding properties, even after 90 min of washing. Beyond EMI shielding performance, the APTES‐treated fabrics demonstrate good air permeability and moisture vapor transmission rates, ensuring they are breathable and comfortable, making them potential candidates for wearable applications where EMI exposure is a concern.

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)0.003
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 score0.053
Threshold uncertainty score0.673

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
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 science0.0010.001
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 head0.006
GPT teacher head0.243
Teacher spread0.237 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

Explore more

Same venueAdvanced Materials TechnologiesSame topicElectromagnetic wave absorption materialsFrench-language works237,207