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Although cobalt oxides show great promise as supercapacitor electrode materials, their slow kinetics and low conductivity make them unsuitable for widespread application. We developed Ni and Cu-doped Co3O4 nanoparticles (NPs) via a simple chemical co-precipitation method without the aid of a surfactant. The samples were analyzed for their composition, function group, band gap, structure/morphology, thermal property, surface area and electrochemical property using X-ray diffraction (XRD), ICP-OES, Fourier transform infrared (FTIR) spectroscopy, Ultraviolet-visible (UV-Vis), Scanning electron microscopy (SEM), Thermogravimetric analysis (TGA) and/or Differential thermal analysis (DTA), Brunauer-Emmett-Teller (BET), and Impedance Spectroscopy (EIS), Cyclic voltammetry (CV), respectively. Notably, for the prepared sample, the addition of Cu to Co3O4 NPs results in a 11.5-fold increase in specific surface area (573.78 m2 g-1) and a decrease in charge transfer resistance. As a result, the Ni doped Co3O4 electrode exhibits a high specific capacitance of 749 Fg-1, 1.75 times greater than the pristine Co3O4 electrode's 426 F g-1. The electrode's enhanced surface area and electronic conductivity are credited with the significant improvement in electrochemical performance. The produced Ni doped Co3O4 electrode has the potential to be employed in supercapacitor systems, as the obtained findings amply demonstrated. [ABSTRACT FROM AUTHOR]

The aim of this study was to prepare the well-dispersed modified chitosan–alumina (CA) with size less than 50 nm by the facile synthesis method to evaluate the adsorption of hydroquinone and arsenic (V) ions in the aqueous solutions. Derived gamma-alumina from boehmite was coated and modified by chitosan and sodium dodecyl sulfate (SDS) and salicylic acid, respectively. The characterization of nanocomposites was studied by XRD, FTIR, FESEM, EDX TEM and BET analysis. The chitosan and SDS phases were detected in the structure of the adsorbent as confirmed by XRD achievements. A quadratic polynomial model was developed to describe the effect of the operating parameters including pH, temperature and initial concentration on the adsorption capacity of the prepared sample while the experimental data were designed by a response surface method (RSM). The maximum adsorption capacity for the best adsorbent named CSAS3 was measured to be 86.95 and 95.24 mg/g for HQ and As (V) ions by employing linear Langmuir equation, respectively. The kinetic study indicated that the experimental data were in an appropriate matching with the linearized pseudo-quadratic kinetic equation (R2 = 0.999). The results showed the successful removal of hydroquinone and arsenic ions form the aqueous after 5 consecutive cycles. [ABSTRACT FROM AUTHOR]

Existing multiple workflow scheduling techniques focus on traditional Quality of Service (QoS) parameters such as cost, deadline, and makespan to find optimal solutions by consuming a large amount of electrical energy. Higher energy consumption decreases system efficiency, increases operational cost, and generates more carbon footprint. These major problems can lead to several problems, such as economic strain, environmental degradation, resource depletion, energy dependence, health impacts, etc. In a cloud computing environment, scheduling multiple workflows is critical in developing a strategy for energy optimization, which is an NP-hard problem. This paper proposes a novel, bi-phase Energy-Efficient Fruit Fly-based Optimization (EFFO) algorithm for optimizing energy consumption for scheduling multiple workflows. In the first phase, the proposed EFFO algorithm uses first come, first serve, priority scheduling and a Genetic Algorithm to generate the initial workflow search space. In the second phase, the energy consumption is optimized by the proposed EFFO algorithm. Eight NAS benchmarks and five NAS classes (A, B, C, S & W) are employed as a case study. The simulation results are carried out on the WorkflowSim 1.0 platform to test the efficacy of the proposed EFFO algorithm. The experimental results are compared against energy-aware for workflow scheduling and virtual machine consolidation (EASVMC), Power-Efficient Scheduling for Virtual Machine Systems (PESVMS), Energy Efficiency Scheduler (EES), and heterogeneous earliest finish time (HEFT) algorithms and outperformed them with 10.518%, 16.302%, 26.154%, and 28.982%, respectively, based on average energy consumption on five scientific workflows comprised Montage, CyberShake, Laser Interferometer Gravitational-Wave Observatory (LIGO), Scripps Institution of Oceanography High-Throughput (SIPHT), and Epigenomics. [ABSTRACT FROM AUTHOR]