Guiding Bacterial Activity for Biofabrication of Complex Materials <i>via</i> Controlled Wetting of Superhydrophobic Surfaces
Bibliographic record
Abstract
Abstract Superhydrophobic surfaces are promising for preventing fouling and the formation of biofilms, with important implications in the food chain, maritime transport, and health sciences, among others. In this work, we exploit the interplay between wetting principles of superhydrophobic surfaces and microbial fouling for advanced three-dimensional (3D) biofabrication of biofilms. We utilize hydrostatic and capillary pressures to finely control the air–water interface and the aerotaxis-driven biofabrication on superhydrophobic surfaces. Superhydrophobic 3D molds are produced by a simple surface modification that partially embeds hydrophobic particles in silicone rubber. Thereafter, the molds allow the templating of the air–water interface of the culture medium, where the aerobic nanocellulose-producing bacteria (Komagataeibacter medellinensis) are incubated. The biofabricated replicas are hollow and seamless nanofibrous objects with a controlled morphology. Gradients of thickness, topographical feature size, and fiber orientation on the biofilm are obtained by controlling wetting, incubation time, and nutrient availability. Furthermore, we demonstrate that capillary length limitations are overcome by using pressurized closed molds, whereby a persistent air plastron allows the formation of 3D microstructures, regardless of their morphological complexity. We also demonstrate that interfacial biofabrication is maintained for at least 12 days without observable fouling of the mold surface. In summary, we achieve controlled biofouling of the air–water interface as imposed by the experimental framework under controlled wetting. The latter is central to both microorganism-based biofabrication and fouling, which are major factors connecting nanoscience, synthetic biology, and microbiology.
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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".