Ultra‐Flyweight Cryogels of MXene/Graphene Oxide for Electromagnetic Interference Shielding
Bibliographic record
Abstract
Abstract MXene and graphene cryogels have demonstrated excellent electromagnetic interference (EMI) shielding effectiveness due to their exceptional electrical conductivity, low density, and ability to dissipate electromagnetic waves through numerous internal interfaces. However, their synthesis demands costly reduction techniques and/or pre‐processing methods such as freeze‐casting to achieve high EMI shielding and mechanical performance. Furthermore, limited research has been conducted on optimizing the cryogel microstructures and porosity to enhance EMI shielding effectiveness while reducing materials consumption. Herein, a novel approach to produce ultra‐lightweight cryogels composed of Ti 3 C 2 T x /graphene oxide (GO) displaying multiscale porosity is presented to enable high‐performance EMI shielding. This method uses controllable templating through the interfacial assembly of filamentous‐structured liquids that are readily converted into EMI cryogels. The obtained ultra‐flyweight cryogels (3–7 mg cm −3 ) exhibit outstanding specific EMI shielding effectiveness (33 000–50 000 dB cm 2 g −1 ) while eliminating the need for chemical or thermal reduction. Furthermore, exceptional shielding is achieved when the Ti 3 C 2 T x /GO cryogels are used as the backbone of conductive epoxy nanocomposites, yielding EMI shielding effectiveness of 31.7–51.4 dB at a low filler loading (0.3–0.7 wt%). Overall, a one‐of‐a‐kind EMI shielding system is introduced that is readily processed while affording scalability and performance.
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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".