Mechanical and Electrical Properties of 3D‐Printed Highly Conductive Reduced Graphene Oxide/Polylactic Acid Composite
Bibliographic record
Abstract
A conductive network of reduced graphene oxide (rGO) overlaying on a lightweight polymeric scaffold can offer notable electrical properties while maintaining the same mechanical properties as a similar feature without rGO layer. However, conventional methods are unable to produce customized architecture with controllable electronic and mechanical properties. Herein, a simple methodology for preparing objects of complex geometries by 3D printing that possesses the capability to exhibit a diverse spectrum of conductivity levels depending upon the dip‐coating process is reported. The versatile two‐step process is beneficial to create highly conductive objects as low as 100 Ω sq−1 and lightweight rGO networks. Alternative to inkjet printing and direct fluid dispensing methods, the fabrication method for 3D rGO networks provides the opportunity to combine material selection and advanced printing techniques, thus achieving desired performance criteria at a low cost. Simple fabrication techniques for robust 3D rGO networks hold promise for designing objects with unique properties, offering both high resistance to external mechanical force and uniform internal electronic properties.
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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".