A Review on Multifunctional Polymer–MXene Hybrid Materials for Electronic Applications
Bibliographic record
Abstract
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 machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".