Ultra‐Flyweight Cryogels of MXene/Graphene Oxide for Electromagnetic Interference Shielding
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
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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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.004 | 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 it