A detailed study to understand controlled additive manufacturing of regenerated cellulose
Bibliographic record
Abstract
Abstract Environmental concerns within the 3D printing industry have attracted interest in finding biodegradable, eco-friendly material solutions. Cellulose is the most abundant natural polymer on the planet. Cellulosic pulp, derived from biomass, can be dissolved in eco-friendly solvents such as N-methyl morpholine N-oxide (NMMO) to produce Lyocell™. Lyocell™ has had applications in the textile industry for the last decade. It has shown promise in producing high-quality cellulosic fibers and the ability to be altered, tailored, and manufactured with ease. Despite this, additive manufacturing using cellulose is still an area of research with ample room to grow. In this work, we propose an in-depth study of using Lyocell™ to manufacture 3D-printed parts using an affordable desktop 3D-printer modification. The 3D printing process of Lyocell™ is completely circular as the solvent can be recovered from 3D-printed parts, and the printed parts are biodegradable. The design of the developed 3D printing equipment, the rheological properties, and the 3D printing of the cellulose-NMMO solution are discussed in this work.
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 | 0.000 |
| 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) | 0.002 | 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".