Virgin polymers based on petrochemical feedstock are mainly preferred by most plastic goods manufacturers instead of recycled plastic feedstock. Major reason for this is the lack of reliable information about the quality, suitability, and availability of recycled plastics, which is partly due to lack of proper segregation techniques. In this paper, we present our ongoing efforts to segregate plastics based on its types and improve the reliability of information about recycled plastics using the first-of-its-kind blockchain smart contracts powered by multi-sensor data-fusion algorithms using artificial intelligence. We have demonstrated how different data-fusion modes can be employed to retrieve various physico-chemical parameters of plastic waste for accurate segregation. We have discussed how these smart tools help in efficiently segregating commingled plastics and can be reliably used in the circular economy of plastic. Using these tools, segregators, recyclers, and manufacturers can reliably share data, plan the supply chain, execute purchase orders, and hence, finally increase the use of recycled plastic feedstock.

What drives growth becomes cancerous when it goes beyond limits. Contrary to this common sense, today, consumerism drives our economies and feeds our appetite for ever-growing wants. As a result, we are damaging our ecosystems and risking our very existence on Earth. Though too late, various efforts are promoted by governments and driven by industries to rapidly decarbonize our energy systems and sustainably consume and recycle raw materials. We have discussed two ongoing projects in the domain of energy transition and circular economy. The first one transforms industrial carbon emissions into green fuels and the second one helps in efficient and sustainable segregation and recycling of plastic waste using multi-sensor-driven AI and blockchain tools. These examples demonstrate how circular economy and energy transitions complement each other in the battle against climate change and pollution.

No abstract

-Different implementations of planar perfectly matched absorbers are studied under the unified framework of the Finite-Volume Time-Domain (FVTD) method. This comparative analysis allows to discuss the similarities existing between the theoretical models and explores the differences in their practical implementation and numerical performance in the framework of the FVTD method. Numerical experiments for performance analysis of the different PML models are conducted in terms of discretization and angle of incidence using waveguide models. The results are compared to theoretically expected values and to the first-order Silver Müller absorbing boundary condition.

Have you ever wondered why we use certain computational electromagnetics methods and how we decide on the choice of tools for modeling problems in electromagnetics? Though true for any field, particularly in electrodynamics, there is a gap between current practitioners' knowledge and knowhow about different tools and the state‐of‐the‐art developments. Rapid advancements made in material science and (nano)‐fabrication techniques are demanding new tools with multiscale and multiphysics capabilities. In this article, we cover the recent developments and highlight capabilities and limitations of different electromagnetic modeling tools. One has to answer a series of questions about modeling constraints and objectives before picking the appropriate tool. We aim to clarify some of the misconceptions, discuss limits and capabilities of some of the popular electromagnetic modeling tools, and provide a few practical tips, so that applied physicists and engineers can make informed decisions while choosing appropriate tools for their applications.

No abstract

No abstract

No abstract