Fabrication of Curli Fiber-PEDOT:PSS Biomaterials with Tunable Self-Healing, Mechanical, and Electrical Properties
Bibliographic record
Abstract
Poly(3,4-ethylenedioxythiophene) polystyrenesulfonate (PEDOT:PSS) is a highly conductive, easily processable, self-healing polymer. It has been shown to be useful in bioelectronic applications, for instance, as a biointerfacing layer for studying brain activity, in biosensitive transistors, and in wearable biosensors. A green and biofriendly method for improving the mechanical properties, biocompatibility, and stability of PEDOT:PSS involves mixing the polymer with a biopolymer. Via structural changes and interactions with PEDOT:PSS, biopolymers have the potential to improve the self-healing ability, flexibility, and electrical conductivity of the composite. In this work, we fabricated novel protein–polymer multifunctional composites by mixing PEDOT:PSS with genetically programmable amyloid curli fibers produced by Escherichia coli bacteria. Curli fibers are among the stiffest protein polymers and, once isolated from bacterial biofilms, can form plastic-like thin films that heal with the addition of water. Curli-PEDOT:PSS composites containing 60% curli fibers exhibited a conductivity 4.5-fold higher than that of pristine PEDOT:PSS. The curli fibers imbued the biocomposites with an immediate water-induced self-healing ability. Further, the addition of curli fibers lowered the Young’s and shear moduli of the composites, improving their compatibility for tissue-interfacing applications. Lastly, we showed that genetically engineered fluorescent curli fibers retained their ability to fluoresce within curli-PEDOT:PSS composites. Curli fibers thus allow to modulate a range of properties in conductive PEDOT:PSS composites, broadening the applications of this polymer in biointerfaces and bioelectronics.
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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) | 0.001 | 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) | 0.000 | 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 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".