3D-printable PVA-based inks filled with leather particle scraps for UV-assisted direct ink writing: Characterization and printability
Bibliographic record
Abstract
Although leather production has significant environmental impacts, it remains a popular material due to its durability, aesthetics, and mechanical properties. Recycling leather scraps, an abundant waste stream of the tanning industry, is gaining increasing attention as a way to reduce waste, pollutants, and emissions from producing pristine material. To this end, additive manufacturing represents a novel and promising way to recycle leather scraps and byproducts as secondary raw materials for new product 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. The selected technology enables processing materials with reduced energy and material use. The morphology and thermal properties of the leather filler were assessed before adding it to a novel cross-linkable PVA-based matrix. The rheological behavior, printability, crosslinking degree, and mechanical properties of three different ink formulations were evaluated, reaching up to 20% wt. of leather scraps and almost 40% wt. after post-curing. According to the tests, the PVA-leather scrap inks can be successfully processed through UV-assisted Direct Ink Writing, obtaining good crosslinking degrees, printability, shape fidelity, and mechanical properties broadly similar to virgin leather materials, i.e., 170 MPa elastic modulus, 13% elongation at break, and 6 MPa stress at break. 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.
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 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".