Creaming Layers of Nanocellulose Stabilized Water-Based Polystyrene: High-Solids Emulsions for 3D Printing
Bibliographic record
Abstract
Oil-in-water emulsions stabilized using cellulose nanofibrils (CNF) form extremely stable and high-volume creaming layers which do not coalesce over extended periods of time. The stability is a result of the synergistic action of Pickering stabilization and the formation of a CNF percolation network in the continuous phase. The use of methyl cellulose (MC) as a co-emulsifier together with CNF further increases the viscosity of the system and is known to affect the droplet size distribution of the formed emulsion. Here, we utilize these highly stable creaming layer systems for in situ polymerization of styrene with the aim to prepare an emulsion-based dope for additive manufacturing. We show that the approach exploiting the creaming layer enables the effortless water removal yielding a paste-like material consisting of polystyrene beads decorated with CNF and MC. Further, we report comprehensive characterization that reveals the properties and the performance of the creaming layer. Solid-state NMR measurements confirmed the successful polymerization taking place inside the nanocellulosic network, and size exclusion chromatography revealed average molecular weight (Mw) of polystyrene as approximately 700,000 Da. Moreover, the amount of the leftover monomer was found to be less than 1% as detected by gas chromatography. The dry solids content of the paste was ∼20% which is a significant increase compared to the solids content of the original CNF dispersion (1.7 wt%). The shrinkage of the CNF, MC and polystyrene structures upon drying—an often-faced challenge—was found to be acceptable for this composite containing highly hygroscopic biobased materials. At best, the two dimensional shrinkage was no more than ca. 20% which is significantly lower than the shrinkage of pure CNF being as high as 50%. The paste, which is a composite of biobased materials and a synthetic polymer, was demonstrated in direct-ink-writing to print small objects. With further optimization of the formulation, we find the emulsion templating approach as a promising route to prepare composite materials.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".