Three-Dimensional Printed and Biocompatible Conductive Composites Comprised of Polyhydroxybutyrate and Multiwalled Carbon Nanotubes
Bibliographic record
Abstract
As the field of 3D printing continues to enable the fabrication of biomedical materials and devices, there is increasing demand for the development of biocompatible functional materials with tailorable properties. Here, we utilized a desktop 3D printer to fabricate porous structures of electrically conductive polymer composites comprised of multiwalled carbon nanotubes (MWCNTs) in a matrix of polyhydroxybutyrate (PHB). PHB is a biocompatible, biodegradable, and piezoelectric polymer. The MWCNTs were melt-mixed in amounts from 0.25 to 5 wt % in PHB from two different suppliers with slightly different physical properties. The nanomaterial dispersion, morphology, electrical, thermal, and mechanical properties, and the crystallization behavior of both types of composites were investigated. A good dispersion at the macro- and microscale was observed in both types of composites. Electrical percolation threshold ranges of 0.25–0.5 wt % and 0.5–0.75 wt % were found for composites made with the two different types of PHB. The addition of MWCNTs resulted in an increase of Young’s modulus and decrease of strain at break for both composites. The processability of the materials was demonstrated by 3D printing both stretchable meandering conductive traces and well-defined pore structure scaffolds. Biocompatibility tests were performed with MRC-5 cells and showed that the materials lack cytotoxicity. These results show the potential of these electrically conductive materials for use in biomedical electronic devices or as electro-active scaffolds for tissue regeneration applications, which require biocompatible, porous materials with microscaled architectures.
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 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 | 0.001 |
| 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".