Polymeric biomaterials exhibit excellent physicochemical characteristics as a scaffold for cell and tissue engineering applications. Chemical modification of the polymers has been the primary mode of functionalization to enhance biocompatibility and regulate cellular behaviors such as cell adhesion, proliferation, differentiation, and maturation. Due to the complexity of the in vivo cellular microenvironments, however, chemical functionalization alone is usually insufficient to develop functionally mature cells/tissues. Therefore, the multifunctional polymeric scaffolds that enable electrical, mechanical, and/or magnetic stimulation to the cells, have gained research interest in the past decade. Such multifunctional scaffolds are often combined with exogenous stimuli to further enhance the tissue and cell behaviors by dynamically controlling the microenvironments of the cells. Significantly improved cell proliferation and differentiation, as well as tissue functionalities, are frequently observed by applying extrinsic physical stimuli on functional polymeric scaffold systems. In this regard, the present paper discusses the current state-of-the-art functionalized polymeric scaffolds, with an emphasis on electrospun fibers, that modulate the physical cell niche to direct cellular behaviors and subsequent functional tissue development. We will also highlight the incorporation of the extrinsic stimuli to augment or activate the functionalized polymeric scaffold system to dynamically stimulate the cells.

Continuous quantitative monitoring of the change in mineral content during the bone healing process is crucial for efficient clinical treatment. Current radiography-based modalities, however, pose various technological, medical, and economical challenges such as low sensitivity, radiation exposure risk, and high cost/instrument accessibility. In this regard, an analytical approach utilizing electrochemical impedance spectroscopy (EIS) assisted by machine learning algorithms is developed to quantitatively characterize the physico-electrochemical properties of the bone, in response to the changes in the bone mineral contents. The system is designed and validated following the process of impedance data measurement, equivalent circuit model designing, machine learning algorithm optimization, and data training and testing. Overall, the systematic machine learning-based classification utilizing the combination of EIS measurements and electrical circuit modeling offers a means to accurately monitor the status of the bone healing process.

Abstract: Electrospinning is a versatile method for synthesizing nanofibrous structures from nearly all polymers, offering a solution for the industrial‐scale mass production of nanomaterials in a wide range of applications. However, the continuous non‐woven structure intrinsic to electrospun fibers limits their applications, where a smaller length scale is desired. Here, we present a novel method to synthesize polymeric nanofiber‐fragments based on colloid electrospinning of polymer and sacrificial silica nanoparticles, followed by mechanical fracturing with ultrasonication. The size and hydrophobicity of silica nanoparticles are optimized for their improved integration within the polymer matrix, and the controllability of nanofiber‐fragment length by the amount of silica nanoparticle loading, down to 2 µm in length for poly(vinylidene fluoride‐trifluoroethylene) nanofibers with an average fiber diameter of approximately 100 nm, is shown. The resultant nanofiber‐fragments are shown to maintain their material properties including piezoelectric coefficients and their enhanced injectability for drug delivery application is demonstrated with an animal model.