MXene‐Based Ultrathin Electromagnetic Wave Absorber with Hydrophobicity, Anticorrosion, and Quantitively Classified Electrical Losses by Intercalation Growth Nucleation Engineering
Bibliographic record
Abstract
Abstract MXene, a highly regarded material, has garnered significant attention within the electromagnetic wave (EMW) absorption field. However, the practical application of MXene is limited in harsh environments. Herein, a magic technique strategy of intercalation growth nucleation engineering is employed to prepare multifunctional MXene‐based EMW absorption materials. By regularly introducing different metal ions between the layers of MXene, an ultrathin absorber can be achieved by annealing after reacting in specific positions. The synthesized MCFC‐69‐8 shows hydrophobicity properties and exhibits a large charge‐transfer resistance of 18112 Ω cm 2 with a low corrosion rate and corrosion rate, indicating a good anti‐corrosion property. Through applying a series of mathematical methods, MCFC‐69‐8 shows 25% relaxation polarization loss and 75% conduction loss, and its relaxation time is linked with the specific type of relaxation polarization loss, resulting in an effective absorption bandwidth (EAB) of 4.9 GHz with an ultra‐thin optimal matching thickness of 1.48 mm. Finally, an absorber is built using CST to attain an ultrabroad EAB covering 2–18 GHz. This engineering not only simplifies the intercalation process but also achieves a high‐performance and anti‐corrosion EMW absorber, providing a valuable perception for realizing thinner MXene‐based EMW absorbers in the future.
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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".