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The balance between the exploration and the exploitation plays a significant role in the meta-heuristic algorithms, especially when they are used to solve large-scale optimization problems. In this paper, we propose a multiple-strategy learning particle swarm optimization algorithm, called MSL-PSO, to solve problems with large-scale variables, in which different learning strategies are utilized in different stages. At the first stage, each individual tries to probe some positions by learning from the demonstrators who have better performance on the fitness value and the mean position of the population. All the best probed positions, each of which has the best fitness among all positions probed by its corresponding individual, will compose a new temporary population. The temporary population will be sorted on the fitness values in a descending order, and will be used for each individual to find its demonstrators, which is based on the rank of the best probed solution in the temporary population and the rank of the individual in the current population, to learn using a new strategy in the second stage. The first stage is used to improve the exploration capability, and the second one is expected to balance the convergence and diversity of the population. To verify the effectiveness of MSL-PSO for solving large-scale optimization problems, some empirical experiments are conducted, which include CEC2008 problems with 100, 500, and 1000 dimensions, and CEC2010 problems with 1000 dimensions. Experimental results show that our proposed MSL-PSO is competitive or has a better performance compared with ten state-of-the-art algorithms.

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A novel realization of microtubular direct methanol fuel cells (µDMFC) with ultrahigh power output is reported by using "rolled-up" nanotechnology. The microtube (Pt-RuO -RUMT) is prepared by rolling up Ru O layers coated with magnetron-sputtered Pt nanoparticles (cat-NPs). The µDMFC is fabricated by embedding the tube in a fluidic cell. The footprint of per tube is as small as 1.5 × 10 cm . A power density of ≈257 mW cm is obtained, which is three orders of magnitude higher than the present microsized DFMCs. Atomic layer deposition technique is applied to alleviate the methanol crossover as well as improve stability of the tube, sustaining electrolyte flow for days. A laminar flow driven mechanism is proposed, and the kinetics of the fuel oxidation depends on a linear-diffusion-controlled process. The electrocatalytic performance on anode and cathode is studied by scanning both sides of the tube wall as an ex situ working electrode, respectively. This prototype µDFMC is extremely interesting for integration with micro- and nanoelectronics systems.

Fullerene-based indoor OPVs, particularly phenyl-C61 butyric acid methyl ester (PCBM), has been regarded as a prospective harvesting indoor light energy source to drive low-power consumption electronic devices such as sensors and IoTs. Due to the low tunability of its inherently spherical structure, the performance of the fullerene-based indoor OPVs seem to hit a bottleneck compared with the non-fullerene materials. Here, we explore the potential application of fullerene derivative bis-PCBM in indoor OPVs, which owns a higher the lowest unoccupied molecular orbital (LUMO) level than PCBM. The results show that when blended with PCDTBT, bis-PCBM devices yield a high VOC of up to 1.05 V and 0.9 V under AM 1.5G illumination and 1000 lx indoor light, compared with the corresponding values of 0.93 V and 0.79 V for PCBM devices. Nevertheless, the disorders in bis-PCBM suppress the JSC and FF and, therefore, result in a lower efficiency compared to PCBM devices. However, the efficiency and stability differences between the two kinds of cells were much reduced under indoor light conditions. After further optimization of the material composition and fabrication process, bis-PCBM could be an alternative to PCBM, offering great potential for indoor OPV with high performance.