The effective disposal of ever-increasing electronic waste (e-waste) is one of the grand challenges for the scientific and technological community today. As e-waste has exponentially been increasing the burden on our environment with long-term effects on the ecosystem, the need for finding sustainable means to recover, reuse, and recycle the materials available in the e-waste is much sought after in the present time. In this background, we demonstrate an easily scalable green route for the beneficiation and effective usage of metallic materials from the e-waste using cryo-temperature grinding with the primary objective of retrieving the near-full metallic residue from e-waste by an energy-efficient and eco-friendly approach. The metallic nanoparticles thus obtained are subsequently utilized for the selective reduction of CO 2 into different gaseous products via the electrochemical route, leading to the evolution of CH 4 , H 2 , and CO as major gaseous products at neutral pH and CO as the major product at basic pH. In a nutshell, the current approach can provide useful means for achieving major metallic residue from the e-waste, which can further be utilized for green energy production in an eco-friendly manner, making the process sustainable.

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High entropy alloys (HEAs) have drawn significant interest in the materials research community owing to their remarkable physical and mechanical properties. These improved physicochemical properties manifest due to the formation of simple solid solution phases with unique microstructures. Though several pathbreaking HEAs have been reported, the field of alloy design, which has the potential to guide alloy screening, is still an open topic hindering the development of new HEA compositions, particularly ones with hexagonal close packed (hcp) crystal structure. In this work, an attempt has been made to develop an intelligent extra tree (ET) classification model based on the key thermodynamic and structural properties, to predict the phase evolution in HEAs. The results of correlation analysis suggest that all the selected thermodynamic and structural features are viable candidates for the descriptor dataset. Testing accuracy of above 90% along with excellent performance matrices for the ET classifier reveal the robustness of the model. The model can be employed to design novel hcp HEAs and as a valuable tool in the alloy design of HEAs in the future.

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