Design and Development of Biocompatible, Flexible, and Biodegradable Collagen-Based Organic Field-Effect Transistors
Bibliographic record
Abstract
Organic electronics are rapidly advancing, driven by innovative materials and device design strategies that can enhance both mechanical robustness and device performance, thereby broadening their potential applications. The emergence of skin-like electronics has positioned devices such as thin film transistors as promising candidates for various applications, including biosensing, bioelectronics and regenerative medicine. Despite these promising developments, achieving stretchable and deformable organic electronics that are also biocompatible and degradable upon demand remains a considerable challenge. In this work, we developed a flexible, biocompatible, and degradable organic field-effect transistor (OFET) through the integration of a degradable substrate, a high-performance semiconducting polymer, and collagen─one of the main components of human skin. The resulting devices demonstrated both excellent electronic properties and favorable mechanical properties, with the devices retaining their characteristics under various bending strains and after multiple bending cycles. Additionally, to explore the degradability, we subjected the devices to controlled conditions, achieving approximately 48% mass loss within a few days. Biocompatibility assessments were conducted using human embryonic kidney cells, with cell viability tests confirming the compatibility of the devices and their individual components. Overall, our findings underscore the potential of collagen-based organic electronics for advancing deformable bioelectronics, combining key features such as biocompatibility and degradability to address critical needs in this emerging field.
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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) | 0.000 | 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".