Size-Dependent Oxidation-Induced Phase Engineering for MOFs Derivatives Via Spatial Confinement Strategy Toward Enhanced Microwave Absorption
Bibliographic record
Abstract
Precisely reducing the size of metal-organic frameworks (MOFs) derivatives is an effective strategy to manipulate their phase engineering owing to size-dependent oxidation; however, the underlying relationship between the size of derivatives and phase engineering has not been clarified so far. Herein, a spatial confined growth strategy is proposed to encapsulate small-size MOFs derivatives into hollow carbon nanocages. It realizes that the hollow cavity shows a significant spatial confinement effect on the size of confined MOFs crystals and subsequently affects the dielectric polarization due to the phase hybridization with tunable coherent interfaces and heterojunctions owing to size-dependent oxidation motion, yielding to satisfied microwave attenuation with an optimal reflection loss of -50.6 dB and effective bandwidth of 6.6 GHz. Meanwhile, the effect of phase hybridization on dielectric polarization is deeply visualized, and the simulated calculation and electron holograms demonstrate that dielectric polarization is shown to be dominant dissipation mechanism in determining microwave absorption. This spatial confined growth strategy provides a versatile methodology for manipulating the size of MOFs derivatives and the understanding of size-dependent oxidation-induced phase hybridization offers a precise inspiration in optimizing dielectric polarization and microwave attenuation in theory.
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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".