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Synthesis,characterization and life cycle assessment of electrochemically exfoliated KOH-activated holey graphene

Frontiers of Environmental Science & Engineering 2023年第12期17卷 175-188页

作者： Fatemeh Mozaffarpour Nafiseh Hassanzadeh ehsan vahidi Faculty of Materials Science and Engineering K.N.Toosi University of TechnologyTehran ***Iran Department of Mining and Metallurgical Engineering Mackay School of Earth Sciences and EngineeringUniversity of NevadaRenoNV 89557USA

Graphene materials have drawn tremendous attention in recent *** formation of holes and pores on graphene sheets can provide transfer channels and facilitate the ion/electron transport *** this study,graphene nanosheets were prepared electrochemically,and then,they were used as the starting material for the preparation of holey graphene(HG)through the KOH activation *** weight ratio of initial electrochemically exfoliated graphene(EEG)to KOH was optimized according to the morphological features,BET surface area examination,graphene number of layers calculated from XRD patterns,and the ID/IG ratio obtained from Raman *** showed that increasing the KOH amount led to the achievement of higher values of ID/IG and surface area and less re-stacking of graphene sheets which occurs because of the heat treatment *** environmental burdens of the production routes for the preparation of EEG and HG were investigated by cradle-to-gate life cycle assessment(LCA).The LCA results of EEG production indicated that electricity with the contributions of 94%,91%,82%,and 75%of the total impact in four environmental categories,including fossil fuel depletion,ozone depletion,global warming,and smog was the main environmental *** the pore generation process,KOH was recognized as the biggest contributor(about 51%to 83%of the total impact)in six impact categories,including ozone depletion,non-carcinogenics,smog,global warming,carcinogenics,and eutrophication which could be attributed to its high consumption amount(21.9 kg).This work offers environmental considerations for the development of sustainable graphene materials.

关键词： Holey graphene Electrochemical exfoliation Life cycle assessment(LCA) Environmental impact KOH activation

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A Deep Reinforcement Learning-Based Technique for Optimal Power Allocation in Multiple Access Communications

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Intelligent Automation & Soft Computing 2024年第1期39卷 93-108页

作者： Sepehr Soltani ehsan Ghafourian Reza Salehi Diego Martín Milad vahidi Department of Industrial Engineering College of EngineeringUniversity of HoustonHoustonTX77204USA Department of Computer Science Iowa State UniversityAmesIowaUSA ETSI de Telecomunicación Universidad Politécnica de MadridMadridSpain Faculty of School of Plant and Environmental Sciences Virginia Polytechnic Institute and StateUniversityBlacksburgVirginia24060USA

Formany years,researchers have explored power allocation(PA)algorithms driven bymodels in wireless networks where multiple-user communications with interference are ***,data-driven machine learning methods have become quite popular in analyzing wireless communication systems,which among them deep reinforcement learning(DRL)has a significant role in solving optimization issues under certain *** this purpose,in this paper,we investigate the PA problem in a k-user multiple access channels(MAC),where k transmitters(e.g.,mobile users)aim to send an independent message to a common receiver(e.g.,base station)through wireless *** this end,we first train the deep Q network(DQN)with a deep Q learning(DQL)algorithm over the simulation environment,utilizing offline ***,the DQN will be used with the real data in the online training method for the PA issue by maximizing the sumrate subjected to the source ***,the simulation results indicate that our proposedDQNmethod provides better performance in terms of the sumrate compared with the available DQL training approaches such as fractional programming(FP)and weighted minimum mean squared error(WMMSE).Additionally,by considering different user densities,we show that our proposed DQN outperforms benchmark algorithms,thereby,a good generalization ability is verified over wireless multi-user communication systems.

关键词： Deep reinforcement learning deep Q learning multiple access channel power allocation

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