MOF‐Based Electromagnetic Shields Multiscale Design: Nanoscale Chemistry, Microscale Assembly, and Macroscale Manufacturing
Bibliographic record
Abstract
Abstract The effects of electromagnetic (EM) radiation have received increased attention, closely associated with the widespread use of electronics and wireless communication. A significant development in the area is the recent adoption of metal‐organic frameworks (MOFs) to effectively enable electromagnetic interference (EMI) shielding. MOF tunable molecular scaffold architecture offers numerous pathways to generate customizable magnetic and electrical properties, which are prerequisite materials characteristics for efficient EMI shielding performance. Their flexibility in terms of structural design, accompanied by high porosity and large specific surface area, makes MOFs excellent candidates to shield EM waves at multiple scales. Herein, the crucial role of molecular‐, nano‐, micro‐, and macro‐scale structural design is reviewed in accordance with the shielding performance of MOFs. The current design strategies of MOF‐based EMI shields are systematically outlined, and the shielding mechanisms are also expounded based on their structural features. The factors that hinder the widespread utilization of functional MOF‐derived EMI shields are also examined. Future research directions are unveiled for the rational design of the next‐generation MOF‐based EMI shields to address the pressing EM radiation concerns.
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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".