MétaCan
Menu
Back to cohort
Record W4392816640 · doi:10.1080/25740881.2024.2326129

Multifunctional composite materials for electromagnetic interference shielding

2024· article· en· W4392816640 on OpenAlexaff
Geetanjali Sethi, Annu Malhotra, Sangeeta Sachdeva, Payal Mehrotra, Yoshit Bargla, Shweta Jagtap, Arindam Adhikari, Pawan Kumar, Jatis Kumar Dash, Rajkumar Patel

Bibliographic record

VenuePolymer-Plastics Technology and Materials · 2024
Typearticle
Languageen
FieldMaterials Science
TopicElectromagnetic wave absorption materials
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsElectromagnetic shieldingElectromagnetic interferenceComposite numberMaterials scienceInterference (communication)Composite materialEngineeringElectrical engineering

Abstract

fetched live from OpenAlex

Increasing dependence and usage of electronic devices has raised the concern for electromagnetic (EM) shielding. This review article is an overview of ongoing cutting-edge research on electromagnetic shielding materials, their applications, advantages and shortcomings. The article highlights doping of polypyrrole(PPy) with different components to achieve desired properties. The work focusses on methods of achieving desired properties of PPy through doping. We have summarized results of doping it with Graphene, Nickel, MXene, Iron Oxide and CNT to achieve desired properties like better electrical conductivity, flexibility and lighter material, greater tensile strength, biocompatibility, better microwave absorption and better magnetic properties. The review presents compilation of most recent ongoing research in the field of electromagnetic shielding. We have also discussed existing limitations and possible future prospects in the ongoing research.

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: none
Teacher disagreement score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.010
GPT teacher head0.244
Teacher spread0.234 · 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

Citations2
Published2024
Admission routes1
Has abstractyes

Explore more

Same venuePolymer-Plastics Technology and MaterialsSame topicElectromagnetic wave absorption materialsFrench-language works237,207