Construction of Hierarchical Yolk–Shell Co/N-Dope C@void@C@MoS<sub>2</sub> Composites with Multiple Heterogeneous Interfaces toward Broadband Electromagnetic Wave Absorption
Bibliographic record
Abstract
The synthesis of materials with a multicomponent hierarchical structure is an essential strategy for achieving high-performance electromagnetic wave (EMW) absorption. However, conventional design strategies face challenges in terms of the rational construction of specific architecture. In this study, we employ a combined space-restricted and hierarchical construction strategy to surface-plant MoS 2 nanosheets on yolk–shell structural carbon-modified Co-based composites, leading to the development of high-performance Co/NC@void@C@MoS 2 absorbers with advanced architecture. The surface-planted MoS 2 nanosheets, the Co/NC magnetic yolk, and the dielectric carbon shell work together to enhance the impedance matching characteristics and synergistic loss capabilities in the composites. Experimental results indicate that Co/NC@void@C-700@MoS 2 exhibited the best absorption performance with an effective absorption bandwidth of 7.54 GHz (at 2.05 mm) and a minimum reflection loss of −60.88 dB (at 1.85 mm). Furthermore, radar cross-section simulation results demonstrate that Co/NC@void@C-700@MoS 2 effectively suppresses the scattering and transmission of EMWs on perfect electric conductor substrates, implying its superior practical application value. This study provides inspiration and experimental basis for designing and optimizing EMW absorption materials with hierarchical yolk–shell architecture.
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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) | 0.000 | 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".