Bioinks from All‐Natural Pickering Emulgels Co‐Stabilized by Cationic CNC and Inclusion Complexes Formed by α‐Cyclodextrin
Bibliographic record
Abstract
Abstract Direct ink writing is especially relevant to the biomedical field due to the customizable extrusion and the possibility of creating pre‐designed architectures. Abundant natural polymers are sustainable and biocompatible alternatives to synthetic and persistent polymers. The printing of pure nanocellulose suspensions proves difficult due to low solid loadings, high shrinkage, as well as non‐fitting rheology. Emulsion gels (emulgel) alternatives gain attention in the field owing to their favorable viscoelastic properties and the possibility of creating multiphase systems. The authors’ sulfur‐free cationic cellulose nanocrystals (CNC) of low degree of substitution enable straightforward deployment in Pickering emulsions. An emulgel ink co‐stabilized by cationic CNC and α‐cyclodextrin is introduced as an interfacial inclusion complex. All ink components are natural and biodegradable compounds. The produced emulgel inks allow for high fidelity printing and minimum shrinkage upon drying that relaxes the need for supports, even in complex overhanging structures. A low yield stress (230–270 Pa) facilitates the inclusion of cells for biomedical applications into the formulation. The emulgel can be tuned to the desired rheological properties and be equipped with both polar and apolar compounds due to the biphasic system, making it a promising platform for biocompatible 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 | 0.000 |
| 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".