Recent progress on green electromagnetic shielding materials based on macro wood and micro cellulose components from natural agricultural and forestry resources
Bibliographic record
Abstract
Recent research efforts in the field of electromagnetic interference shielding (EMI) materials have focused on biomass as a green and sustainable resource. More specifically, wood and cellulose nano fiber (CNF) have many advantages, some of which include lightweight, porosity, widespread availability, low cost, and easy processing. These favorable properties have led researchers to consider these types of biomass as an EMI shielding material with great potential. At present, while many excellent published works in EMI shielding materials have investigated wood and CNF, this research area is still new, compared with non-biomass EMI shielding materials. More specifically, there is still a lack of in-depth research and summary on the preparation process, pore structure regulation, component optimization, and other factors affecting the EMI shielding of wood and CNF based EMI shielding materials. Thus, this review paper presents a comprehensive summary of recent research on wood and CNF based EMI shielding materials in recent three years in terms of the preparation methods, material structure design, component synergy, and EMI mechanism, and a forward future perspective for existing problems, challenges, and development trend. The ultimate goal is to provide a comprehensive and informative reference for the further development and exploration of biomass EMI shielding 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".