Together, 316L steel, magnesium-alloy, Ni-Ti, titanium-alloy, and cobalt-alloy are commonly employed biomaterials for biomedical applications due to their excellent mechanical characteristics and resistance to corrosion, even though at times they can be incompatible with the body. This is attributed to their poor biofunction, whereby they tend to release contaminants from their attenuated surfaces. Coating of the surface is therefore required to mitigate the release of contaminants. The coating of biomaterials can be achieved through either physical or chemical deposition techniques. However, a newly developed manufacturing process, known as powder mixed-electro discharge machining (PM-EDM), is enabling these biomaterials to be concurrently machined and coated. Thermoelectrical processes allow the migration and removal of the materials from the machined surface caused by melting and chemical reactions during the machining. Hydroxyapatite powder (HAp), yielding Ca, P, and O, is widely used to form biocompatible coatings. The HAp added-EDM process has been reported to significantly improve the coating properties, corrosion, and wear resistance, and biofunctions of biomaterials. This article extensively explores the current development of bio-coatings and the wear and corrosion characteristics of biomaterials through the HAp mixed-EDM process, including the importance of these for biomaterial performance. This review presents a comparative analysis of machined surface properties using the existing deposition methods and the EDM technique employing HAp. The dominance of the process factors over the performance is discussed thoroughly. This study also discusses challenges and areas for future research.

The internal dynamics during the coalescence of a sessile droplet and a subsequently deposited impacting droplet, with either identical or distinct surface tension, is studied experimentally in the regime where surface tension is dominant. Two color high-speed cameras are used to capture the rapid internal flows and associated mixing from both side and bottom views simultaneously by adding an inert dye to the impacting droplet. Given sufficient lateral separation between droplets of identical surface tension, a robust surface jet is identified on top of the coalesced droplet. Image processing shows this jet is the result of a surface flow caused by the impact inertia and an immobile contact line. By introducing surface tension differences between the coalescing droplets, the surface jet can be either enhanced or suppressed via a Marangoni flow. The influence of the initial droplet configuration and relative surface tension on the long-term dynamics and mixing efficiency, plus the implications for emerging applications such as reactive inkjet printing, are also considered.

Lithium ion (Li-ion) battery packs have become the most popular option for powering electric vehicles (EVs). However, they have certain drawbacks, such as high temperatures and potential safety concerns as a result of chemical reactions that occur during their charging and discharging processes. These can cause thermal runaway and sudden deterioration, and therefore, efficient thermal management systems are essential to boost battery life span and overall performance. An electrochemical-thermal (ECT) model for Li-ion batteries and a conjugate heat transfer model for three-dimensional (3D) fluid flow and heat transfer are developed using COMSOL Multiphysics^®. These are used within a novel computational fluid dynamics (CFD)-enabled multi-objective optimization approach, which is used to explore the effect of the mini-channel cold plates’ geometrical parameters on key performance metrics (battery maximum temperature ($T_{max}$), pressure drop ($∆P$), and temperature standard deviation ($T_{\sigma }$)). The performance of two machine learning (ML) surrogate methods, radial basis functions (RBFs) and Gaussian process (GP), is compared. The results indicate that the GP ML approach is the most effective. Global minima for the maximum temperature, temperature standard deviation, and pressure drop ($T_{max}$, $T_{\sigma }$, and $∆P$, respectively) are identified using single objective optimization. The third version of the generalized differential evaluation (GDE3) algorithm is then used along with the GP surrogate models to perform multi-objective design optimization (MODO). Pareto fronts are generated to demonstrate the potential trade-offs between $T_{max}$, $T_{\sigma }$, and $∆P$. The obtained optimization results show that the maximum temperature dropped from 36.38 to 35.98 °C, the pressure drop dramatically decreased from 782.82 to 487.16 Pa, and the temperature standard deviation decreased from 2.14 to 2.12 K; the corresponding optimum design parameters are the channel width of 8 mm and the horizontal spacing near the cold plate margin of 5 mm.