Computational Optimizing the Electromagnetic Wave Reflectivity of Double‐Layered Polymer Nanocomposites
Bibliographic record
Abstract
Double-layered absorption-dominated electromagnetic interference (EMI) shielding composites are highly desirable to prevent secondary electromagnetic wave pollution. However, it is a tremendous challenge to optimize the shielding performance via the trial-and-error method due to the low efficiency. Herein, a novel approach of computation-aided experimental design is proposed to efficiently optimize the reflectivity of the double-layered composites. A normalized input impedance (NII) method is presented to calculate the electromagnetic wave reflectivity of multilayered EMI shielding composites. The calculated results are a good match with the experimental results. Then, the NII method is utilized to design polyvinylidene difluoride/MXene/carbon nanotube (PVDF/MXene/CNT) composites. According to the optimization of the NII method, the prepared PVDF/MXene/CNT composite has an ultralow reflectivity of 0.000057, which outperforms that reported in current work and satisfies the requirement of electromagnetic wave absorbing material. Additionally, its average EMI shielding effectiveness is 30 dB, demonstrating that PVDF/MXene/CNT composite simultaneously achieves shielding and absorption. The ultralow reflection mechanism can be ascribed to the ideal impedance match. Both the PVDF/MXene and the PVDF/CNT layers can attenuate electromagnetic energy, which subverts the traditional cognition of double-layered absorption-dominated EMI shielding composites. The NII method opens a way for the practical fabrication of double-layered absorption-dominated EMI shielding composites.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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".