Obtaining Properties of Polymeric Filaments for 3D Printing from Dinizia Excelsa Ducke Fiber and Copper Nanoparticles
Bibliographic record
Abstract
With many existing contagious diseases, SARS-CoV-2 exemplifies the dangers of emerging infectious diseases, potentially leading to severe acute respiratory syndrome (SARS). In March 2020, the World Health Organization (WHO) declared COVID-19 a pandemic in response to the rapid increase in infections globally. This situation not only highlighted the vulnerability of populations to dangerous pathogens but also underscored the persistent challenges faced by the public health community in preventing and controlling contagious diseases. Furthermore, it led to excessive use of plastics that harm the environment, such as 70% alcohol due to its low cost and ease of use, which increased the use of plastic packaging and its improper disposal. There are studies on bioplastics reinforced with plant fibers, showing good mechanical properties, and using polymer nanocomposites with metal oxide nanoparticles, such as copper, where their incorporation can achieve optical, electronic, mechanical, and antimicrobial enhancements through the filament extrusion process. Therefore, the matrix is not only a support for the nanoparticles but can also improve antibacterial performance and expand the applications of this material to meet different requirements. The objective of this study is to produce, through extrusion, antimicrobial bioplastic filaments (PLA, plant fiber, and copper nanoparticles) for use in 3D printing and evaluate their tensile mechanical properties, Optical Morphology (OM), and Scanning Electron Morphology (SEM). The filaments produced with a plant fiber particle size of 140 µm exhibited superior quality and better mechanical performance, with tensile strengths of 33.63 and 23.83 MPa and elastic moduli of 2.69 and 5.45 GPa compared to those with a particle size of 30 µm.
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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".