Conductive hydrogels are promising materials with mixed ionic‐electronic conduction to interface living tissue (ionic signal transmission) with medical devices (electronic signal transmission). The hydrogel form factor also uniquely bridges the wet/soft biological environment with the dry/hard environment of electronics. The synthesis of hydrogels for bioelectronics requires scalable, biocompatible fillers with high electronic conductivity and compatibility with common aqueous hydrogel formulations/resins. Despite significant advances in the processing of carbon nanomaterials, fillers that satisfy all these requirements are lacking. Herein, intrinsically dispersible acid‐crystalized PEDOT:PSS nanoparticles (ncrys‐PEDOT_X) are reported which are processed through a facile and scalable nonsolvent induced phase separation method from commercial PEDOT:PSS without complex instrumentation. The particles feature conductivities of up to 410 S cm⁻¹, and when compared to other common conductive fillers, display remarkable dispersibility, enabling homogeneous incorporation at relatively high loadings within diverse aqueous biomaterial solutions without additives or surfactants. The aqueous dispersibility of the ncrys‐PEDOT_X particles also allows simple incorporation into resins designed for microstereolithography without sonication or surfactant optimization; complex biomedical structures with fine features (< 150 µm) are printed with up to 10% particle loading . The ncrys‐PEDOT_X particles overcome the challenges of traditional conductive fillers, providing a scalable, biocompatible, plug‐and‐play platform for soft organic bioelectronic materials.

The peripheral nerves (PNs) innervate the dermis and epidermis, and are suggested to play an important role in wound healing. Several methods to quantify skin innervation during wound healing have been reported. Those usually require multiple observers, are complex and labor-intensive, and the noise/background associated with the immunohistochemistry (IHC) images could cause quantification errors/user bias. In this study, we employed the state-of-the-art deep neural network, Denoising Convolutional Neural Network (DnCNN), to perform pre-processing and effectively reduce the noise in the IHC images. Additionally, we utilized an automated image analysis tool, assisted by Matlab, to accurately determine the extent of skin innervation during various stages of wound healing. The 8 mm wound is generated using a circular biopsy punch in the wild-type mouse. Skin samples were collected on days 3, 7, 10 and 15, and sections from paraffin-embedded tissues were stained against pan-neuronal marker- protein-gene-product 9.5 (PGP 9.5) antibody. On day 3 and day 7, negligible nerve fibers were present throughout the wound with few only on the lateral boundaries of the wound. On day 10, a slight increase in nerve fiber density appeared, which significantly increased on day 15. Importantly, we found a positive correlation (R² = 0.926) between nerve fiber density and re-epithelization, suggesting an association between re-innervation and re-epithelization. These results established a quantitative time course of re-innervation in wound healing, and the automated image analysis method offers a novel and useful tool to facilitate the quantification of innervation in the skin and other tissues.

Electrical stimulation has shown promise in clinical studies to treat nerve injuries. This work is aimed to create an aligned bioelectronic construct that can be used to bridge a nerve gap, directly interfacing with the damaged nerve tissue to provide growth support. The conductive three‐dimensional bioelectronic scaffolds described herein are composite materials, comprised of conductive polypyrrole (PPy) nanoparticles embedded in an aligned collagen hydrogel. The bioelectronic constructs are seeded with dorsal root ganglion derived primary rat neurons and electrically stimulated in vitro. The PPy loaded constructs support a 1.7‐fold increase in neurite length in comparison to control collagen constructs. Furthermore, upon electrical stimulation of the PPy‐collagen construct, a 1.8‐fold increase in neurite length is shown. This work illustrates the potential of bioelectronic constructs in neural tissue engineering and lays the groundwork for the development of novel bioelectronic materials for neural interfacing applications.

Field-effect transistor (FET) biosensors using graphene have become one of the most promising biosensing platforms for the early diagnosis of diseases with features such as high sensitivity, label-free detection and application compatibility with point-of-care systems. Herein, we critically discuss recent advances in graphene FET (GFET) biosensor development toward clinically relevant biomarkers associated with representative diseases including cancer, neurodegenerative disease, infectious disease, and inflammatory conditions. Recent progress was reviewed to evaluate GFET architectures, surface functionalization methods, and detection quality. The biomarkers explored were clusterin in Alzheimer’s disease, thrombin in coagulopathy, estrogen receptor α (ER-α) in breast cancer, Carcinoembryonic antigen in lung cancer, microRNAs for malignant tumors, exosomes derived from HepG2 for the hepatocellular carcinoma (HCC) cell line, interleukin-6 (IL-6) for chronic obstructive pulmonary disease (COPD), Polyclonal antibodies and antigens (P24) for HIV and prostate-specific antigen for prostate cancer. The developed devices demonstrate ultralow detection limits at femtomolar to attomolar concentrations with the aid of designed antibodies, aptamers and nanomaterials. Herein, this review presents the sensing mechanisms and biomedical application of various GFET platforms, focusing on their emerging potential as next-generation platforms for rapid, non-invasive and point-of-care diagnostics.