Mapping 3D Printability of Ionically Cross-Linked Cellulose Nanocrystal Inks: Architecting from Nano- to Macroscale Structures
Bibliographic record
Abstract
Engineering the rheological properties of colloidal inks is one of the main challenges in achieving high-fidelity 3D printing. Herein, we provide a comprehensive study on the rheological behavior of inks based on cellulose nanocrystals (CNCs) in the presence of given salts to enable high-quality 3D printing. The rheological properties of the CNC suspensions are tailored by considering the nature of the electrolyte (i.e., 10 types of salts featuring different ion sizes, charge numbers, and inter- and intra-molecular interactions) at various concentrations (25-100 mM). A high printing fidelity is achieved in a narrow CNC and salt concentration range, significantly depending on the salt type. The structure-property relationship is explored in a "3D-printing" space (2D map), introducing a guideline for researchers active in this field. To further unravel the effect of salt type on morphological properties, CNC aerogels are developed by freeze-drying the printed structures. The results illustrate that enhancing viscoelastic properties render a denser structure featuring smaller pores.
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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".