The synergistic effect of hybridization-micro/nano-structural design on the Ti₃C₂Tx MXene@CoFe-MOF@chitosan heterojunction enhances the absorption of electromagnetic waves
Bibliographic record
Abstract
The development of wave-absorbing materials is of particular crucialness in dealing with electromagnetic wave (EMWs) pollution. Especially in the fields of aerospace and highly integrated electronic devices, where the demand for lightweight, high-efficiency, broadband and multi-functional EMWs absorbing materials is increasing gradually. In this study, through the synergistic regulation of hybrid fillers and micro-nano structures, an EMWs absorbing material with abundant heterogeneous interfaces and micro-nano pore structures has been successfully formed. The prepared Ti₃C₂Tₓ MXene@CoFe-MOF@chitosan carbon aerogel significantly enhances the absorption performance of EMWs by stimulating heterogeneous interface polarization relaxation and electromagnetic synergy effects. Under the condition of low density (35.09 mg/cm 3 ), TMC 900 exhibits a minimum reflection loss of -50.95 dB and an effective absorption bandwidth (EAB) of 6.1 GHz. In addition, TMC 900 also possesses excellent heat insulation and flame-retardant characteristics, enabling it to be applied in extreme environments. This research clarifies the synergistic regulation mechanism of hybrid fillers and micro-nano structures, opening a new pathway for the design of heterogeneous structures in EMWs absorbing materials.
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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".