Gradient layered MXene/Fe3O4@CNTs/TOCNF ultrathin nanocomposite paper exhibiting effective electromagnetic shielding and multifunctionality
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it