3D-printable PVA-based inks filled with leather particle scraps for UV-assisted direct ink writing: Characterization and printability
Bibliographic record
Abstract
Despite its significant environmental impacts, leather remains a popular material due to its durability, aesthetics, and mechanical properties. Recycling leather scraps is gaining increasing attention to reduce waste, pollutants, and emissions from pristine raw materials in the tanning industry. Material Extrusion additive manufacturing represents a promising way to recycle leather byproducts as secondary raw materials for new applications. This paper investigates the characterization and printability of photo- and thermal-curable PVA-based inks for UV-assisted Direct Ink Writing filled with leather filler scraps from the tanning industry, i.e., leather shavings. As a cold extrusion process , Direct Ink Writing reduces energy consumption and maximizes the waste percentage content in new material formulations. The morphology and thermal properties of the leather filler were assessed before adding it to a novel cross-linkable PVA-based matrix, preparing inks containing up to 20 % wt. of leather scraps, leading to almost 40 % wt. after post-curing. Rheological tests showed a shear-thinning behavior of the formulations and a clear transition from solid-like to fluid-like behavior, followed by a quick recovery of the solid-like behavior, ensuring good printability, shape retention, and fidelity. UV crosslinking and post-curing led to robust polymer networks , reaching crosslinking degrees of ∼90 %. According to the mechanical tests, the PVA-leather scrap inks exhibited mechanical properties broadly similar to virgin leather materials, i.e., 170 MPa elastic modulus , 13 % elongation at break , and 6 MPa stress at break. Scanning Electron Microscopy revealed a preferential alignment of the leather filler along the extrusion direction , confirming the reinforcing effect of the scrap particles. These results demonstrate the suitability of these inks as alternatives for new tailored applications in the leather industry, reducing virgin material usage through additive manufacturing .
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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 | 0.001 |
| 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".