Conductive polymers in smart wound healing: From bioelectric stimulation to regenerative therapies
Bibliographic record
Abstract
Wound healing, particularly in particularly after surgical operations and especially cardiothoracic surgeries, presents a significant global healthcare burden due to prolonged recovery time, recurrent infections, and limited effectiveness of the conventional therapies. The recent advancements in biomaterials have positioned conductive polymers (CPs) as promising components in the design of next-generation wound care technologies. CPs, such as polypyrrole (PPy), polyaniline (PANI) and poly (3,4-ethylenedioxythiophene) (PEDOT), possess unique electrical, chemical and biological properties, making them ideal for integration into multifunctional and responsive wound dressings. The present review focuses on the emerging role of CPs in wound healing, along with their incorporation into various delivery platforms including hydrogels, nanofibers, membranes, microneedle patches and 3D scaffolds. These materials provide a synergistic approach by enabling localized electrical stimulation, enhancing tissue regeneration, and producing antibacterial, antioxidant and anti-inflammatory effects. In particular, it is discussed how CP-based systems can be engineered to respond dynamically to the wound microenvironment such as pH, temperature or enzymatic activity, for accelerating controlled drug release and real-time therapeutic intervention. It also highlights the integration of CPs with complementary technologies such as triboelectric nanogenerators, biosensors and photothermal agents, contributing to smarter, more personalized wound care solutions. Moreover, this review addresses the current challenges, including biocompatibility, degradation kinetics and scalability, with a summary of the directions for the future research to optimize clinical translation. Based on the recent findings across materials science, bioengineering and regenerative medicine, this review illustrates the transformative potential of CPs in advancing effective, non-invasive and patient-specific wound healing strategies.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".