Three-dimensional (3D) printing has experienced a steady increase in popularity for direct manufacturing, where complex geometric items can be produced without the aid of templating tools, and manufacturing waste can be remarkably reduced. While customized medical devices and daily life items can be made by 3D printing of thermoplastics, microbial contamination has been a serious obstacle during their usage. A very clever approaches to overcome this challenge is to incorporate antimicrobial metal or metal oxide (M/MO) nanoparticles within the thermoplastics during or prior to 3D printing. Many M/MO nanoparticles can prevent contamination from a wide range of microorganism, including antibiotic-resistant bacteria via various antimicrobial mechanisms. Additionally, they can be easily printed with thermoplastic without losing their integrity and functionality. In this mini review, we summarize recent advancements and discuss future trends related to the development of 3D printed antimicrobial thermoplastic nanocomposites by addition of M/MO nanoparticles.

Three-dimensional (3D) printing is one of the most futuristic manufacturing technologies, allowing on-demand manufacturing of products with highly complex geometries and tunable material properties. Among the different 3D-printing technologies, fused deposition modeling (FDM) is the most popular one due to its affordability, adaptability, and pertinency in many areas, including the biomedical field. Yet, only limited amounts of materials are commercially available for FDM, which hampers their application potential. Polybutylene succinate (PBS) is one of the biocompatible and biodegradable thermoplastics that could be subjected to FDM printing for healthcare applications. However, microbial contamination and the formation of biofilms is a critical issue during direct usage of thermoplastics, including PBS. Herein, we developed a composite filament containing polybutylene succinate (PBS) and lignin for FDM printing. Compared to pure PBS, the PBS/lignin composite with 2.5~3.5% lignin showed better printability and antioxidant and antimicrobial properties. We further coated silver/zinc oxide on the printed graft to enhance their antimicrobial performance and obtain the strain-specific antimicrobial activity. We expect that the developed approach can be used in biomedical applications such as patient-specific orthoses.

Polymer filament and its printability, which is strongly influenced by the rheological behavior, can represent a significant hurdle in translating fused deposition modeling (FDM) from the lab to the industrial or clinical settings. The aim of this study is to demonstrate the potential of machine learning (ML) approaches to speed up the development of polymer filaments for FDM. Four types of ML methods; artificial neural network, support vector regression, polynomial chaos expansion (PCE), and response surface model were used to predict the rheological behaivior of polybutylene succinate. In general, all four approaches presented significantly high correlation values with respect to the training and testing data stages. Remarkably, the PCE algorithm repeatedly provided the highest correlation for each response variable in both the training and testing stages. Noteworthy, variation differs between response variables rather than between algorithms. Taken together, these modeling approaches could be used to optimize filament extrusion processes.