Tailoring the Properties of 2D Nanomaterial‐Polymer Composites for Electromagnetic Interference Shielding and Energy Storage by 3D Printing—A Review
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 it