Promoting Electromagnetic Wave Absorption Performance by Integrating MoS<sub>2</sub>@Gd<sub>2</sub>O<sub>3</sub>/MXene Multiple Hetero‐Interfaces in Wood‐Derived Carbon Aerogels
Bibliographic record
Abstract
Abstract Multi‐component composite materials with a magnetic‐dielectric synergistic effect exhibit satisfactory electromagnetic wave absorption performance. However, the effective construction of the structure for these multi‐component materials to fully exploit the advantages of each component remains a challenge. Inspired by natural biomass, this study utilizes wood as the raw material and successfully prepares high‐performance MoS 2 @Gd 2 O 3 /Mxene loaded porous carbon aerogel (MGMCA) composite material through a one‐pot hydrothermal method and carbonization treatment process. With a delicate structural design, the MGMCA is endowed with abundant heterogeneous interface structures, favorable impedance matching characteristics, and a magnetic‐dielectric synergistic system, thus demonstrating multiple electromagnetic wave loss mechanisms. Benefiting from these advantages, the obtained MGMCA exhibits outstanding electromagnetic wave absorption performance, with a minimum reflection loss of −57.5 dB at an ultra‐thin thickness of only 1.9 mm. This research proposes a reliable strategy for the design of multi‐component composite materials, providing valuable insight for the design of biomass‐based materials as electromagnetic wave absorbers.
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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".