Toward Sustainable 3D-Printed Sensor: Green Fabrication of CNT-Enhanced PLA Nanocomposite via Solution Casting
Bibliographic record
Abstract
The current study explores, for the first time, an eco-friendly solution casting method using a green solvent, ethyl acetate, to prepare feedstock/filaments from polylactic acid (PLA) biopolymer reinforced with carbon nanotubes (CNTs), followed by 3D printing and surface activation for biosensing applications. Comprehensive measurements of thermal, electrical, rheological, microstructural, and mechanical properties of developed feedstock and 3D-printed parts were performed and analyzed. Herein, adding 2 wt.% CNTs to the PLA matrix marked the electrical percolation, achieving conductivity of 8.3 × 10−3 S.m−1, thanks to the uniform distribution of CNTs within the PLA matrix facilitated by the solution casting method. Rheological assessments paralleled these findings; the addition of 2 wt.% CNTs transitioned the nanocomposite from liquid-like to a solid-like behavior with a percolated network structure, significantly elevating rheological properties compared to the composite with 1 wt.% CNTs. Mechanical evaluations of the printed samples revealed improvement in tensile strength and modulus compared to virgin PLA by a uniform distribution of 2 wt.% CNTs into PLA, with an increase of 14.5% and 10.3%, respectively. To further enhance the electrical conductivity and sensing capabilities of the developed samples, an electrochemical surface activation treatment was applied to as-printed nanocomposite samples. The field-emission scanning electron microscopy (FE-SEM) analysis confirmed that this surface activation effectively exposed the CNTs to the surface of 3D-printed parts by removing a thin layer of polymer from the surface, thereby optimizing the composite’s electroconductivity performance. The findings of this study underscore the potential of the proposed eco-friendly method in developing advanced 3D-printed bio-nanocomposites based on carbon nanotubes and biopolymers, using a green solution casting and cost-effective material extrusion 3D-printing method, for electrochemical-sensing applications.
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 | 0.000 |
| 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".