A novel sponge-like 2D Ni/derivative heterostructure to strengthen microwave absorption performance
Bibliographic record
Abstract
One of the major hurdles of Ni-based microwave absorbing materials is the preparation of two-dimensional (2D) Ni flakes that can improve magnetic anisotropy to tune complex permeability. In this study, we successfully synthesized porous 2D sponge-like Ni/derivative heterostructures composed of Ni, NiO and Ni(OH)2 through a controllable hydrogen reduction method. Thanks to the larger grain size of the Ni/derivative heterostructure prepared at 600 °C (Ni-600) under hydrogen flow, good magnetic properties and high magnetic loss could be obtained, which is beneficial for the enhancement of microwave absorption properties. For the Ni-600 samples, the minimal reflection loss (RL) is -37.3 dB at 7.1 GHz and the effective bandwidth (RL < -10 dB, 90% microwave dissipation) could be tuned in the range of 4.5-18.0 GHz with the thickness of 1.5-4.5 mm. High attenuation ability, including dielectric loss and magnetic loss, and good impedance matching are the requirements for excellent microwave absorption properties. In addition, the porous 2D heterostructure flake structure also significantly contributes to microwave absorption. Multiple reflections and scattering caused by the porous flakes, interfacial polarizations in the heterostructures, tunable impedance matching in the porous structure, strong natural resonance induced by the 2D flakes and plentiful micro-capacitors in the separate flakes account for the enhanced microwave absorption performance. This study demonstrates a fresh exploration of designing novel electromagnetic wave 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".