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Energy is the fundamental requirement of all physical, chemical, and biological processes which are utilized for better living standards. The toll that the process of development takes on the environment and economic activity is evident from the arising concerns about sustaining the industrialization that has happened in the last centuries. The increase in carbon footprint and the large-scale pollution caused by industrialization has led researchers to think of new ways to sustain the developmental activities, whilst simultaneously minimizing the harming effects on the enviroment. Therefore, decarbonization strategies have become an important factor in industrial expansion, along with the invention of new catalytic methods for carrying out non-thermal reactions, energy storage methods and environmental remediation through the removal or breakdown of harmful chemicals released during manufacturing processes. The present article discusses the structural features and photocatalytic applications of a variety of metal oxide-based materials. Moreover, the practical applicability of these materials is also discussed, as well as the transition of production to an industrial scale. Consequently, this study deals with a concise framework to link metal oxide application options within energy, environmental and economic sustainability, exploring the footprint analysis as well.

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This study presented a new multi-species binary coded algorithm, Mendelian Evolutionary Theory Optimization (METO), inspired by the plant genetics. This framework mainly consists of three concepts: First, the "denaturation" of DNA's of two different species to produce the hybrid "offspring DNA". Second , the Mendelian evolutionary theory of genetic inheritance, which explains how the dominant and recessive traits appear in two successive generations. Third, the Epimuation, through which organism resist for natural mutation. The above concepts are reconfigured in order to design the binary meta-heuristic evolutionary search technique. Based on this framework, four evolutionary operators-1) Flipper, 2) Pollination, 3) Breeding, and 4) Epimutation-are created in the binary domain. In this paper, METO is compared with well-known evolutionary and swarm optimizers 1) Binary Hybrid GA (BHGA), 2) Bio-geography Based Optimization (BBO), 3) Invasive Weed Optimization (IWO), 4) Shuffled Frog Leap Algorithm (SFLA), 5) Teaching-Learning Based Optimization (TLBO), 6) Cuckoo Search (CS), 7) Bat Algorithm (BA), 8) Gravitational Search Algorithm (GSA), 9) Covariance Matrix Adaptation Evolution Strategy(CMAES), 10) Differential Evolution (DE), 11) Firefly Algorithm (FA) and 12) So

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