Electrohydrodynamic (EHD) processes are promising healthcare fabrication technologies, as evidenced by the number of commercialised and food-and-drug administration (FDA)-approved products produced by these processes. Their ability to produce both rapidly and precisely nano-sized products provides them with a unique set of qualities that cannot be matched by other fabrication technologies. Consequently, this has stimulated the development of EHD processing to tackle other healthcare challenges. However, as with most technologies, time and resources will be needed to realise fully the potential EHD processes can offer. To address this bottleneck, researchers are adopting machine learning (ML), a subset of artificial intelligence, into their workflow. ML has already made ground-breaking advancements in the healthcare sector, and it is anticipated to do the same in the materials domain. Presently, the application of ML in fabrication technologies lags behind other sectors. To that end, this review showcases the progress made by ML for EHD workflows, demonstrating how the latter can benefit greatly from the former. In addition, we provide an introduction to the ML pipeline, to help encourage the use of ML for other EHD researchers. As discussed, the merger of ML with EHD has the potential to expedite novel discoveries and to automate the EHD workflow.
Metal matrix composites have been recognized as a feasible approach to obtain a new generation of biodegradable Zn-based material. Nevertheless, there is a great challenge in achieving good dispersion properties of the bioactive reinforcements within zinc matrix. A novel and facile approach, namely graphene oxide (GO)-assisted hetero-aggregation, were developed to achieve uniformly dispersed nanoceramics in the Zn matrix, by using very low-content (0.03 vol%) GO as a linker between the Zn matrix and reinforcement. The negatively-charged GO becomes a suitable “bridge” connected the positively-charged metallic powder and bioactive reinforcement by charge neutralization in polarity solvent. Three kinds of reinforcements, including MgO, ZnO and CuO, were used to verify the feasibility of the above-mentioned method. As-sintered 3CuO/Zn matrix composites, which possessed uniformly distributed reinforcement, uniaxial compressive strength of 301.2 MPa, failure strain over 40%, moderate corrosion rate of 0.063 mm·y−1, acceptable cytocompatibility and antibacterial property, should be a useful material for orthopedic applications.
