Tailoring the Properties of 2D Nanomaterial‐Polymer Composites for Electromagnetic Interference Shielding and Energy Storage by 3D Printing—A Review
Bibliographic record
Abstract
3D‐printed 2D nanomaterials‐based polymer composites, with their exceptional electrical conductivity and structural functionalities, have become leading‐edge engineering materials for electromagnetic interference (EMI) shielding, sensors, and energy storage applications. This review begins with a brief introduction to various types of 2D nanomaterials and their fabrication techniques, specifically different types of 3D printing. The subsequent sections highlight key factors such as rheological properties, surface tension, additives, and binders that influence the printability of 2D nanomaterials‐based polymer composites. The advancements in 2D nanomaterials‐based polymers, including MXene, graphene, and graphene derivatives, are then presented. The interaction, dispersion, and/or network formation of 2D nanomaterials in the polymer matrix is a crucial factor in determining the electrical performance of the composites. This review also discusses surface modification strategies for 2D nanomaterials to enhance their sensing, EMI shielding, and energy storage capabilities. Finally, the impact of various 3D‐printed polymer composite geometries, such as rectangular, cylinder, and circular, on shielding performance is thoroughly examined, engaging the reader in the exploration of these 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".