Wood flour and kraft lignin enable air-drying of the nanocellulose-based 3D-printed structures
Bibliographic record
Abstract
The predominant technique for producing 3D-printed structures of nanocellulose involves freeze-drying despite its drawbacks in terms of energy consumption and carbon footprint. This study explores the less-energy-intensive drying approach by leveraging the valorization of forest residual streams. We utilized wood flour and Kraft lignin as fillers to facilitate room-temperature drying of the nanocellulose-based 3D printed structures. Various ink formulations, integrating cellulose nanofibers, wood flour, and lignin, were tested for direct ink writing (DIW). The formulations exhibited shear-thinning behavior and distinct yield stress with rising stress levels, ensuring the effective flow of the ink during DIW. Consequently, multilayered objects were printed with high shape fidelity and precise dimensions. Lignin and wood flour prevented structural collapse upon room-temperature drying. A reduced shrinkage was observed with the addition of lignin in freeze and room temperature drying. Moreover, the room-temperature dried samples were denser and demonstrated significantly higher resistance to applied compressive force, surpassing those reported for cellulose-based 3D composites in the existing literature. Remarkably, the trade-off effects of lignin are highlighted in terms of efficient stress-distributing and micro-scale sliding, enabling better strength. Along with wood flour, it further increases thermal stability. However, lignin hinders the hierarchical porous structure, the main ion transportation channels, reducing the double-layer capacitance of the carbonized structures. Overall, the results underscore the potential of all-biobased formulations for DIW for practical applications, highlighting their enhanced mechanical properties and structural integrity via the more sustainable drying method.
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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".