Recent progress and perspective in additive manufacturing of EMI shielding functional polymer nanocomposites
Bibliographic record
Abstract
Because of rapid progress in the electronics industry, the market has faced a huge demand for novel materials in the field of electromagnetic interference (EMI) shielding. Conductive functional polymer composites have demonstrated great potential to fulfill this requirement. To synthesize the polymeric composites, functional conductive nanoadditives such as graphene, carbon nanotubes, and MXene are commonly added to polymeric matrices, and the conductive polymer nanocomposites exhibit promising electrical conductivity as well as EMI shielding performance. Additive manufacturing (AM), also referred to as three-dimensional (3D) printing, has been increasingly employed to fabricate complicated geometry components in the medical, aerospace, and automotive industries. AM has also been used to fabricate advanced EMI shielding materials for sensors, supercapacitors, energy storage devices, and flexible electronics. This review aims at introducing the different 3D printing methods applied for the fabrication of EMI shielding polymer nanocomposites. The impact of the AM process on the functionality of the samples is also reviewed. Additionally, the influence of the nanofiller type and amount on the microstructure and performance of the fabricated nanocomposites is discussed. Finally, the prospects and recommended works for future study are outlined.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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".