Multiphase Under‐Liquid Biofabrication With Living Soft Matter: A Route to Customize Functional Architectures With Microbial Nanocellulose
Bibliographic record
Abstract
The growth of aerobic microbes at air-water interfaces typically leads to biofilm formation. Herein, a fermentative alternative that relies on oil-water interfaces to support bacterial activity and aerotaxis is introduced. The process uses under-liquid biofabrication by structuring bacterial nanocellulose (BNC) to achieve tailorable architectures. Cellulose productivity in static conditions is first evaluated using sets of oil homologues, classified in order of polarity. The oils are shown for their ability to sustain bacterial growth and BNC production according to air transfer and solubilization, both of which impact the physiochemical properties of the produced biofilms. The latter are investigated in terms of their morphological (fibril size and network density), structural (crystallinity) and physical-mechanical (surface area and strength) features. The introduced under-liquid biofabrication is demonstrated for the generation of BNC-based macroscale architectures and compartmentalized soft matter. This can be accomplished following three different routes, namely, 3D under-liquid networking (multi-layer hydrogels/composites), emulsion templating (capsules, emulgels, porous materials), and anisotropic layering (Janus membranes). Overall, the proposed platform combines living matter and multi-phase systems as a robust option for material development with relevance in biomedicine, soft robotics, and bioremediation, among others.
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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".