Mostafa Dahshan

To develop medical-grade stainless-steel 316L implants that are biocompatible, non-toxic and antibacterial, such implants need to be coated with biomaterials to meet the current demanding properties of biomedical materials. Hydroxyapatite (HA) is commonly used as a bone implant coating due to its excellent biocompatible properties. Zinc oxide (ZnO) nanoparticles are added to HA to increase its antibacterial and cohesion properties. The specimens were made of a stainless-steel grade 316 substrate coated with HA-ZnO using the electrophoretic deposition technique (EPD), and were subsequently characterized using scanning electron microscopy (SEM), energy dispersive X-ray (EDX), stylus profilometry, electrochemical corrosion testing and Fourier transform infrared (FTIR) spectroscopy. Additionally, cross-hatch tests, cell viability assays, antibacterial assessment and in vitro activity tests in simulated body fluid (SBF) were performed. The results showed that the HA-ZnO coating was uniform and resistant to corrosion in an acceptable range. FTIR confirmed the presence of HA-ZnO compositions, and the in vitro response and adhesion were in accordance with standard requirements for biomedical materials. Cell viability confirmed the viability of cells in an acceptable range (>70%). In addition, the antibacterial activity of ZnO was confirmed on Staphylococcus aureus. Thus, the HA-ZnO samples are recommended for biomedical applications.

To detect sustainable changes in the production processes, memory-type control charts are frequently utilized. This study is conducted to assess the performance of the Bayesian adaptive exponentially weighted moving average (AEWMA) control chart using ranked set sampling schemes following two different loss functions in the presence of a measurement error for posterior and posterior predictive distributions using conjugate priors. This study is based on the covariate model and multiple measurement methods in the presence of a measurement error (ME). The performance of the proposed Bayesian-AEWMA control chart with ME has been evaluated through the average run length and the standard deviation of the run length. Finally, a real-life application in semiconductor manufacturing was conducted to evaluate the effectiveness of the proposed Bayesian-AEWMA control chart with a measurement error based on different ranked set sampling schemes. The results demonstrate that the proposed control chart, in the presence of a measurement error, performed well in detecting out-of-control signals compared to the existing control chart. However, the median ranked set sampling scheme (MRSS) proved to be better than the other two schemes in the presence of a measurement error.