In this work, the grapheme oxide (GO) and GO/ZnO nanocomposite were successfully obtained from the oxidation of graphite and characterized by X-ray diffraction (XRD), scanning electron microscopy (SEM), transmission electron microscopy (TEM), and thermogravimetric analysis (TGA). In the GO/ZnO nanocomposite, the GO sheets were coated with aggregated ZnO nanoneedles with ca. 20 nm of diameter. The obtained materials were used as heterogeneous catalysts for acetylation of Soybean Fatty Acids Methyl Esters (FAME), promoting the epoxy ring-opening using acetic anhydride. The epoxy ring was almost completely opened in the presence of GO or GO/ZnO nanocomposites, with conversion rates up to 99% and selectivity of ca. 90%, and partially opened using only ZnO. The GO/Zn and GO catalysts were reused three times with conversion rates of ca. 85 and 74%, respectively.
Herein, the laser reduction of graphene oxide (GO) and zinc oxide nanoparticle (ZnONP) nanocomposite films is proposed as a one‐step process for supercapacitor fabrication. The films, deposited by casting onto a flexible poly(ethylene terephthalate) (PET) substrate coated with indium‐doped tin oxide (ITO), are subjected to laser irradiation (5 mW, 405 nm) to reduce the GO phase and produce laser‐reduced GO (LRGO). Scanning electron microscopy/energy dispersion spectroscopy (SEM–EDS), micro‐Raman spectroscopy, and current versus voltage (I × V) analyses show a partial reduction of GO to LRGO, forming several conductor‐insulating (LRGO/GO) microporous interfaces, and thereby favoring the formation of a supercapacitor structure. Moreover, the topmost LRGO film layer is extensively reduced, making it sufficiently conducting to work as the counter electrode as well. However, the reduction process is less effective when ZnONPs are introduced into the GO matrix because ZnONPs get clustered and scatter the incident laser before reaching the GO phase. The capacitive behavior, assessed by cyclic voltammetry and galvanostatic charge–discharge measurements, reveals the following specific capacitances: 2.68 F g−1 (GO/LRGO) and 1.44 F g−1 (GO/LRGO/ZnONP). The method proposed herein is advantageous because it produces the microcapacitor structures and LRGO counter electrode in a single laser reduction step.
SummaryOver the past few decades, the number of users and services of the mobile communications system has considerably increased, and since its essential resources such as spectrum and energy are limited, their optimization has drawn particular interest. Concomitantly, artificial intelligence (AI) techniques have advanced and their applications have been expanded, including problems of classification, regression, and optimization of tasks of mobile communications systems. Regarding fifth and sixth generations of such systems, the insertion of AI is foreseen toward the allocation of available resources. The present study applied two recently proposed techniques based on deep reinforcement learning algorithms (viz., deep deterministic policy gradient [DDPG] and twin‐delayed DDPG [TD3]), for the power control and spectrum allocation of a mobile communications system with device‐to‐device (D2D) underlay communications. The results show that both algorithms have superior performance to the three algorithms used for comparison: A random algorithm, a greedy algorithm, and REINFORCE, a classical reinforcement learning algorithm. Furthermore, the results show the proposed algorithms have good generalization capability and performed the allocation intelligently, taking into account the relationship between distances separating devices and interference between communications. The results also proved robust in terms of small variations in input data and noise.
Laser Reduction
In article number http://doi.wiley.com/10.1002/pssa.201901046, Artemis Marti Ceschin and co‐workers propose laser scribing of insulator‐conductor graphene oxide/laser‐reduced graphene oxide micro‐heterojunctions and the current collector to produce at low‐cost and in a single step a flexible supercapacitor device.
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