Fabrication of Curli Fiber-PEDOT:PSS Biomaterials with Tunable Self-Healing, Mechanical, and Electrical Properties
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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 byEscherichia 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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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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 it