Endowing textiles with self-repairing ability through the fabrication of composites with a bacterial biofilm
Bibliographic record
Abstract
To address the increasing environmental footprint of the fast-growing textile industry, self-repairing textile composites have been developed to allow torn or damaged textiles to restore their morphological, mechanical, and functional features. A sustainable way to create these textile composites is to introduce a coating material that is biologically derived, biodegradable, and can be produced through scalable processes. Here, we fabricated self-repairing textile composites by integrating the biofilms of Escherichia coli (E. coli) bacteria into conventional knitted textiles. The major structural protein component in E. coli biofilm is a matrix of curli fibers, which has demonstrated extraordinary abilities to self-assemble into mechanically strong macroscopic structures and self-heal upon contact with water. We demonstrated the integration of biofilm through three simple, fast, and scalable methods: adsorption, doctor blading, and vacuum filtration. We confirmed that the composites were breathable and mechanically strong after the integration, with improved Young's moduli or elongation at break depending on the fabrication method used. Through patching and welding, we showed that after rehydration, the composites made with all three methods effectively healed centimeter-scale defects. Upon observing that the biofilm strongly attached to the textiles by covering the extruding textile fibers from the self-repair failures, we proposed that the strength of the self-repairs relied on both the biofilm's cohesion and the biofilm-textile adhesion. Considering that curli fibers are genetically-tunable, the fabrication of self-repairing curli-expressing biofilm-textile composites opens new venues for industrially manufacturing affordable, durable, and sustainable functional textiles.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".