3D Printed Multifunctional Polymeric Nanocomposite Components With Sensing Capability
Bibliographic record
Abstract
Conventional polymeric 3D printing offers efficient ways to produce 3D structures. In order to provide functional capability of 3D structures, we have incorporated conductive additives such as multi-walled carbon nanotubes (MWCNTs) within the 3D printed structures by utilizing a pellet-based extrusion system. The inclusion of MWCNTs in the nanocomposites allows the creation of semiconductive structures that can detect force or strain due to piezoresistive properties. During the printing process, the shear forces can cause the MWCNTs to partially align, and this alignment is influenced by the chosen path scanning strategies. Our study aimed to determine how the alignment of MWCNTs affects the mechanical, electrical, and thermal properties of 3D-printed nanocomposite objects. We employed various characterization techniques including SEM, resistivity measurements, dynamic mechanical analysis (DMA), three-point bending, and cyclic bending tests at different raster angles (0°, 45°, and 90°) of 3D printed parts. A 3D Random Walk model was developed to simulate the influence of MWCNTs alignment on the piezoresistive behavior. The experimental results showed low electrical resistivity and high flexural strength in 3D printed samples, especially when printed in a longitudinal direction (0°). The highest sensing gauge factor was achieved when printing laterally (90°). To demonstrate the real-world applicability, we designed a self-sensing claw system for grippers with an integrated feedback mechanism using the 3D-printed nanocomposite. This study highlights the potentials of pellet-fed polymeric nanocomposite 3D printing for generating semiconductive structures with sensing capabilities in diverse applications.
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How this classification was reachedexpand
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 | 0.000 | 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) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".