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Record W4409885821 · doi:10.3390/molecules30091955

A Review on Multifunctional Polymer–MXene Hybrid Materials for Electronic Applications

2025· review· en· W4409885821 on OpenAlexafffund
Fatemeh Morshedi Dehaghi, Mohammad Aberoumand, Uttandaraman Sundararaj

Bibliographic record

VenueMolecules · 2025
Typereview
Languageen
FieldMaterials Science
TopicMXene and MAX Phase Materials
Canadian institutionsUniversity of Calgary
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMXenesMaterials scienceNanotechnologyNanomaterialsNanocompositeElectromagnetic shieldingComposite material

Abstract

fetched live from OpenAlex

MXenes, a family of two-dimensional (2D) transition metal carbides, carbonitrides, and nitrides, have emerged as a promising class of nanomaterials for interdisciplinary applications due to their unique physiochemical properties. The large surface area, excellent electrical conductivity, superior mechanical properties, and abundant possible functional groups make this layered nanomaterial an ideal candidate for multifunctional hybrid materials for electronic applications. This review highlights recent progress in MXene-based hybrid materials, focusing on their electrical, dielectric, and electromagnetic interference (EMI) shielding properties, with an emphasis on the development of multifunctionality required for advanced electronic devices. The review explores the multifunctional nature of MXene-based polymer nanocomposites and hybrid materials, covering the coexistence of a diverse range of properties, including sensory capabilities, electromagnetic interference shielding, energy storage, and the Joule heating phenomenon. Finally, the future outlook and key challenges are summarized, offering insights to guide future research aimed at improving the performance and functionality of MXene-polymer nanocomposites.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.831
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.028
GPT teacher head0.340
Teacher spread0.312 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreReview

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

Citations20
Published2025
Admission routes2
Has abstractyes

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