Multimaterial 3D printing of structured surfaces for increased hydrophobicity of biocomposite materials
Bibliographic record
Abstract
Material extrusion 3D printing is an up-and-coming additive manufacturing method that is continuously being explored for its many benefits including rapid prototyping, high degree of customizability, and low material waste production, among others. One of the most widely used materials in material extrusion 3D printing is polylactic acid (PLA) due to its ease of printability and bio-origins. Recently, new biofiller reinforced PLA biocomposite filaments have begun being sold commercially, but the introduction of the biofiller creates problems of increased hydrophilicity and hygroscopicity. In this study, a possible solution to this problem was explored by using multimaterial 3D printing to add a thin, structured outer layer to the biocomposite in either pure PLA or TPU. This layer helps limit any external moisture from coming into contact with the underlying biocomposite by creating a barrier with increased hydrophobicity. A grid, triangle, and honeycomb pattern were each tested at 50%, 75%, and 100% pattern densities for each material. It was found that, along with the pattern that was printed, the filament deposition process created additional roughness that influenced the way the water droplets interacted with the surface. All the patterned surfaces displayed a higher water contact angle than when the material was printed in a flat manner. Additionally, factors that influence the feasibility of using this outer structured layer to improve the surface hydrophobicity of biocomposite parts were explored, including material compatibility and adhesion.
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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) | 0.001 | 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".