Bacterial Cellulose Network from Kombucha Fermentation Impregnated with Emulsion-Polymerized Poly(methyl methacrylate) to Form Nanocomposite
Bibliographic record
Abstract
The use of bio-based residues is one of the key indicators towards sustainable development goals. In this work, bacterial cellulose, a residue from the fermentation of kombucha tea, was tested as a reinforcing nanofiber network in an emulsion-polymerized poly(methyl methacrylate) (PMMA) matrix. The use of the nanofiber network is facilitating the formation of nanocomposites with well-dispersed nanofibers without using organic solvents or expensive methodologies. Moreover, the bacterial cellulose network structure can serve as a template for the emulsion polymerization of PMMA. The morphology, size, crystallinity, water uptake, and mechanical properties of the kombucha bacterial cellulose (KBC) network were studied. The results showed that KBC nanofibril diameters were ranging between 20–40 nm and the KBC was highly crystalline, >90%. The 3D network was lightweight and porous material, having a density of only 0.014 g/cm3. Furthermore, the compressed KBC network had very good mechanical properties, the E-modulus was 8 GPa, and the tensile strength was 172 MPa. The prepared nanocomposites with a KBC concentration of 8 wt.% were translucent with uniform structure confirmed with scanning electron microscopy study, and furthermore, the KBC network was homogeneously impregnated with the PMMA matrix. The mechanical testing of the nanocomposite showed high stiffness compared to the neat PMMA. A simple simulation of the tensile strength was used to understand the limited strain and strength given by the bacterial cellulose network. The excellent properties of the final material demonstrate the capability of a residue of kombucha fermentation as an excellent nanofiber template for use in polymer nanocomposites.
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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".