Manipulating Highly Ordered MXene Porous Composites by Directional Freezing for Absorption Effectiveness-Enhanced Electromagnetic Interference Shielding
Bibliographic record
Abstract
Ti 3 C 2 T x MXene, as a 2D conductive material, has broad application prospects in electromagnetic interference (EMI) shielding fields. Nevertheless, excessive conductivity will destroy impedance matching, resulting in serious electromagnetic wave secondary reflection pollution. Herein, we developed a kind of highly ordered MXene-based porous composite material (PCM) for absorption effectiveness-enhanced EMI shielding via introducing magnetic Co–C@multiwalled carbon nanotubes (MWCNTs) by directional freezing. Furthermore, sodium alginate was introduced to form hydrogen bonds, thereby enhancing the interaction between adjacent MXene nanosheets to increase the mechanical properties of the PCMs. Co–C@MWCNTs in the PCMs can construct a three-dimensional conductive network with MXene nanosheets and promote electron migration and transition, which provide magnetic loss and hence promote the absorption of electromagnetic waves. As expected, the PCMs exhibit a highly ordered and long-range order porous structure. Additionally, when MXene and Co–C@MWCNTs are in a mass ratio of 1:3, the MXene/sodium alginate (SA)/Co–C@MWCNTs PCMs deliver an ultrahigh conductivity of 849 S m –1, a high EMI shielding efficiency of 41.7 dB, and a compressive stress of 50.9 kPa under 80% strain. This work offers a novel approach to construct high-strength and high-EMI shielding materials with a highly ordered pore structure based on enhanced absorption effectiveness.
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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".