A comparison of endothelial cell growth on commercial coronary stents with and without laser surface texturing

Complete endothelialisation of coronary stents is an important determinant of future thrombotic complications following coronary stenting. Stent surface texture is an important factor that influences endothelial cell growth. With the emergence of second and third generation coronary stents, is limited comparative data describing endothelial cell growth in contemporary stent platforms, and limited data available on approaches used to rapidly modify the surfaces of commercial coronary stents to improve endothelialisation. In this study we have determined the in vitro proliferation of the primary human coronary artery endothelial cells on the commonly used 4 types of commercial coronary stents and found that the inner surface of BioMatrix drug-eluting stents (DES), after eliminating of the polymer and drug coating, had significantly higher endothelial cell proliferation compared to that of other bare metal stents (BMS): Multi-Link8, Integrity and Omega. The surfaces of the 3 types of BMS which are smooth, displayed similar endothelial cell proliferation, suggesting the importance of surface features in manipulating endothelial cell growth. Laser surface texturing was used to create micro/nano patterns on the stents. The laser treatment has significantly increased endothelial proliferation on the inner surfaces of all 4 types of stents, and Multi-Link8 stents displayed the highest (&gt;100%) improvement. The laser textured BioMatrix stents had the highest absolute number of endothelial cells growth. Our results provided useful information in the endothelialisation potential for the commonly used commercial coronary stents and suggested a potential future application of laser surface bioengineering to coronary stents for better biocompatibility of the device.</p

A generic mathematical model for gate-to-gate carbon emissions in laser materials processing

The manufacturing industry needs to reduce carbon emissions in support of the net-zero agenda. Carbon emissions are classified into three operational groups based on the framework of the Greenhouse Gas (GHG) Protocol. Although extensive research has been undertaken, this has focused on energy consumption, which contributes to Scope 2 emissions only. In order to address a knowledge gap relating to the total environmental burden of laser material processing, the paper develops a generic mathematical model covering all three operational groups of emissions in relation to the gate-to-gate scope of life cycle assessment. The model was applied to selective laser removal of coating to evaluate the environmental burden and to understand which impact factor contributes the highest emissions. Scope 2 emissions had the highest contribution to the environmental burden in the laser removal of coatings from substrates compared to Scope 1 and 3. The mathematical model provides a simple, accessible and less complex tool for evaluating carbon emissions in laser processing. This supports the net-zero emission agenda.</p

Industry 4.0 energy monitoring system for multiple production machines

Energy consumption is a key contributor to scope two emissions and to the cost of manufacturing. Reducing energy consumption in manufacturing can significantly improve environmental performance by reducing the scope two carbon footprint for industries, especially before the de-carbonization of energy. Monitoring energy consumption for manufacturing facilities, multiple machines, and production resources is essential to achieve this. Hence, this study focuses on developing an energy monitoring system that allows the industry to monitor real-time energy consumption for multiple machines via an open-sources application platform. The implementation of this system is presented in detail, including the integration, and interlinking of Internet of Things (IoT) devices and web application platforms. The development of databases, web application platform, and data visualisation is reported. This energy-monitoring platform displays real-time data, including voltage, current, peak power, energy consumption, energy cost, and scope 2 carbon emissions, empowering end users to make decisions according to manufacturing objectives. This study contributes to the industrial monitoring of energy patterns, which is crucial to facilitate energy efficiency measures under industry 4.0 competency. The implementation of this system does not require modifications to machines or equipment, reducing the barriers to implementation in the industry. Therefore, this monitoring system is necessary for moving towards net zero carbon and sustainable industry 4.0 manufacturing.</p

Energy consumption and performance optimisation of laser cleaning for coating removal

Selective removal of coatings by lasers can facilitate the reuse of coated tools in a circular economy. In order to optimise and control the process, it is essential to study the impact of process input variables on process performance. In this paper, coating removal from tooling was carried out using a picosecond a pulsed fibre laser, in order to investigate the effects of laser pulse energy, pulse frequency, galvo scanning speed and scanning track stepover. A fractional factorial design of experiments and analysis of variance was used to optimise the process; considering cleaning rate, specific energy consumption and surface integrity as assessed by changes in surface roughness and composition of the tooling after laser cleaning. The results shows synergy between cleaning rate and specific energy with the laser pulse frequency and galvo scanning speed as the two most significant factors, while the laser pulse energy had the greatest contribution to changes in surface composition. Based on extensive experiments, the relationship between processing rate and system specific energy consumption was mathematically modelled. The paper contributes a new specific energy model for laser cleaning and provides a benchmark of the process energy requirements compared to other manufacturing processes. Additionally, the generic scientific learning from this is that the rate of energy input is a key tool for maximising cleaning rate and minimising specific energy requirements, while the intensity of energy applied, is a key metric that influences surface integrity. More complex factors, influence the surface integrity