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.

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.

As the urgency to achieve net-zero emissions by 2050 intensifies, industries face an imperative to reimagine their role in the fight against climate change. One promising avenue arises from the realization that industrial emissions, often deemed pollutants, can be the building blocks of a circular economy strategy. By directly utilizing these carbon emissions as raw materials, we can produce net-zero or low-carbon fuels, carbonates, polymers, and chemicals. At the heart of this paradigm shift lies the production of carbon-neutral methanol from industrial flue gas-a technically viable approach that has gained significant momentum in recent years. The conditions under which such a circular economy model for producing renewable methanol becomes commercially sustainable based on realistic constraints, however, are not sufficiently explored in the existing literature. This paper fills this gap by investigating if and when net-zero methanol production from industrial flue gas will be a sustainable long-term strategy. Using detailed technoeconomic modeling of integrated hydrogen and methanol production ecosystems for two production capacities, I will evaluate 32 practical production scenarios using realistic regulatory, economic, and market conditions. Even though renewable methanol from industrial emissions can be a viable technical solution to address climate change and global warming, I will show why this strategy will be commercially feasible only under favorable economic, regulatory, and market conditions. Furthermore, I will demonstrate how the market price of methanol and the cost of carbon-free electricity critically influence the commercial feasibility of this approach. When these two parameters are unfavorable, I will show why other factors, namely, carbon credits and byproduct (oxygen) sales, will not be sufficient to create an economically sustainable circular economy of renewable methanol from industrial emissions. Finally, I will provide arguments on why one has to think through stakeholder cooperation and public-private partnerships to mitigate various project risks. Despite the importance of this topic, it is not sufficiently covered in the available scientific literature. To advance policy and regulatory frameworks in this area, I strongly believe that further research and development is needed. I will also share perspectives on regulatory derisking mechanisms, which can help align regulations with private investors' preferences. With the analyses and arguments showcased in this paper, I will firmly assert that without favorable conditions, strong partnerships, and stakeholder cooperation, the production of renewable net-zero methanol from industrial emissions risks becoming a dead-end strategy.

The perfectly matched layer (PML) technique is extended for a cell-centered finite-volume time-domain (FVTD) method. A step-by-step procedure for the performance characterization of the FVTD PML is presented for both structured and unstructured finite-volume meshes. The FVTD PML is compared with the standard first-order Silver-Müller absorbing boundary condition (SM ABC) for practical applications. It is found that the FVTD PML for an unstructured grid achieves a reflection coefficient lower than -40 dB for incident angles up to 45° and outperforms the SM ABC by 15-20 dB.