Direct 3D Printing of Hybrid Nanofiber-Based Nanocomposites for Highly Conductive and Shape Memory Applications
Bibliographic record
Abstract
Three-dimensional (3D) printing with conductive polymer nanocomposites provides an attractive strategy for the “on-demand” fabrication of electrical devices. This paper demonstrates a family of highly conductive multimaterial composites that can be directly printed into ready-to-use multifunctional electrical devices using a flexible solvent-cast 3D printing technique. The new material design leverages the high aspect ratio and low contact resistance of the hybrid silver-coated carbon nanofibers (Ag@CNFs) with the excellent 3D printability of the thermoplastic polymer. The achieved nanocomposites are capable of printing in open air under ambient conditions, meanwhile presenting a low percolation threshold (i.e., <6 vol %) and high electrical conductivity (i.e., >2.1 × 10 5 S/m) without any post-treatments. We further find that this hybrid Ag@CNF-based nanocomposite shows a quick and low-voltage-triggered electrical-responsive shape memory behavior, making it a great candidate for printing electroactive devices. Multiple different as-printed Ag@CNF-based highly conductive nanocomposite structures used as high-performance electrical devices (e.g., ambient-printable conductive components, microstructured fiber sensors, flexible and lightweight scaffolds for electromagnetic interference shielding, and low-voltage-triggered smart grippers) are successfully demonstrated herein. This simple additive manufacturing approach combined with the synergic effects of the multimaterial nanocomposite paves new ways for further development of advanced and smart electrical devices in areas of soft robotics, sensors, wearable electronics, etc.
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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) | 0.000 | 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".