Gradient layered MXene/Fe3O4@CNTs/TOCNF ultrathin nanocomposite paper exhibiting effective electromagnetic shielding and multifunctionality
Bibliographic record
Abstract
As wearable electronic devices are rapidly developing, there is an urgent need for lightweight, flexible, and ultrathin multifunctional electromagnetic interference (EMI) shielding materials. However, the flexible ultrathin paper that combines efficient shielding and multifunctional integration remains a considerable challenge. Here, a novel MXene/Fe 3 O 4 @CNTs/TOCNF (MCT, MXene = transition metal carbide/carbonitride, CNTs = carbon nanotubes, TOCNF = TEMPO-oxidized cellulose nanofiber, TEMPO = 2,2,6,6-tetramethylpiperidine-1-oxyl radical) nanocomposite paper with a multilayer electromagnetic gradient structure and electromagnetic dual losses was successfully prepared by a simple filtration strategy. Benefiting from effective gradient design and adjusting the proportion of TOCNF, the composite paper (only 18 µm) exhibits outstanding shielding effectiveness (SE) of 66 dB in the X-band, ultrahigh thickness-specific SE and surface-specific SE values of 3300 dB·mm −1 and 31,428 dB·cm 2 ·g −1 respectively. Furthermore, dehydroxylation treatment improves MCT paper’s hydrophobicity, environmental stability, and mechanical strength, expanding its range of use. Excitingly, the highly efficient Joule heating properties and hydrophobicity provide MCT additional de-icing capabilities. We also simulated the electromagnetic shielding effects of MCT composite paper, which was applied in practice. This study documents an innovative and intriguing material combination, providing a simple and effective manufacturing strategy for developing EMI shielding materials. MCT paper is highly suitable for outdoor portable or wearable electronic devices and has significant application potential in humid/severe cold environments.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".