Numerical modeling and simulations of active direct methanol fuel cell (DMFC) systems under various ambient temperatures and operating conditions

Direct methanol fuel cells (DMFCs) are potential candidates for portable backup power generation and auxiliary power units owing to their advantageous features, such as ease of fuel storage and delivery. Optimizing each component of a DMFC system is critical to improving the overall system performance and power density. This paper presents an active DMFC system model, in which a one-dimensional DMFC stack model is combined with major system components, including fuel and water tanks, liquid–gas separator, heat exchangers, pumps, and blowers. The model is implemented using a commercial flow-sheet simulator, ASPEN-HYSYS, and then applied to an active DMFC system to analyze the effects of the DMFC operating parameters and heat management. Special emphasis is placed on establishing active control strategies for the DMFC stack temperature, methanol crossover rate, and water recovery by optimizing the system components and operating conditions. Overall, this study helps identify innovative active DMFC system designs and configurations.     

Effect of variation of hydrophobicity of anode diffusion media along the through-plane direction in direct methanol fuel cells

Reducing methanol crossover from the anode to cathode in direct methanol fuel cells (DMFCs) is critical for attaining high cell performance and fuel utilization, particularly when highly concentrated methanol fuel is fed into DMFCs. In this study, we present a novel design of anode diffusion media (DM) wherein spatial variation of hydrophobicity along the through-plane direction is realized by special polytetrafluoroethylene (PTFE) coating procedure. According to the capillary transport theory for porous media, the anode DM design can significantly affect both methanol and water transport processes in DMFCs. To examine its influence, three different membrane-electrode assemblies are fabricated and tested for various methanol feed concentrations. Polarization curves show that cell performance at high methanol feed concentration conditions is greatly improved with the anode DM design with increasing hydrophobicity toward the anode catalyst layer. In addition, we investigate the influence of the wettability of the anode microporous layer (MPL) on cell performance and show that for DMFC operation at high methanol feed concentration, the hydrophilic anode MPL fabricated with an ionomer binder is more beneficial than conventional hydrophobic MPLs fabricated with PTFE. This paper highlights that controlling wetting characteristics of the anode DM and MPL is of paramount importance for mitigating methanol crossover in DMFCs.     

Sulfonated poly(ether sulfone) electrolytes structured with mesonaphthobifluorene graphene moiety for PEMFC

Sulfonated poly(ether sulfone)s containing a mixture of cis and trans mesonaphthobifluorene moiety were synthesized, and their properties were characterized. The mesonaphthobifluorene graphene moiety contained 6 phenyl rings and was conjugated together to form planar sheets of sp²-bonded carbon. Poly(arylene ether sulfone)s containing a mixture of cis and trans tetraphenyl ethylene units were synthesized by polycondensation, and converted into graphene by intramolecular Friedel–Craft cyclization with Lewis acid (FeCl₃). The sulfonation was taken selectively on mesonaphthobifluorene units with concentrated sulfuric acid. The structural properties of the sulfonated polymers were investigated by ¹H NMR spectroscopy. The membranes were studied with regard to ion exchange capacity (IEC), water uptake, and proton conductivity.     

A planar catalytic combustion sensor using nano-crystalline F-doped SnO2 as a supporting material for hydrogen detection

A planar catlytic combustion gas sensor based on Pd/Pt catalyst supported on F-doped SnO₂ nano-crystalline materials has been designed and fabricated for hydrogen detection. The sensor consists of platinum heaters on an alumina plate coated with a catalytic layer and compensating layer. This sensor exhibited better performance than that of the sensors employing sensing material of Pd/Pt catalyst on γ-Al₂O₃ and of Pd/Pt catalyst on nano-crystalline SnO₂. The detection limit of the sensor at 370 °C is in the concentration range of 0.5–5% (v/v), with an excellent linearity of signal voltage to the hydrogen gas concentration.     

Analysis of Single‐RF MIMO Receiver with Beam‐Switching Antenna

This paper proposes a single‐RF MIMO receiver that adopts a beam‐switching antenna (BSA) instead of a conventional array antenna. The beauty of the proposed single‐RF MIMO receiver with BSA is that it can be deployed in a very small physical space while achieving a full spatial multiplexing gain. Our analysis has revealed that the use of a BSA inevitably results in the spectrum spreading effect at the RF output, which in turn causes an SNR decrease and adjacent channel interference (ACI). Two novel receiver techniques are proposed to mitigate the issues of redundant sub‐band suppression and ACI avoidance. Numerical analysis results verify the performance improvement from the proposed receiver techniques.     

A reaction kinetic study of CO<Subscript>2</Subscript> gasification of petroleum coke,coals and mixture

Characteristics of Char-CO₂ gasification were compared in the temperature range of 1,100–1,400 °C using a thermogravimetric analyzer (TGA) for petroleum coke, coal chars and mixed fuels (Petroleum coke/coal ratios: 0, 0.25, 0.5, 0.75, 1). The results showed that reaction time decreased with increasing gasification temperature, BET surface area and alkali index of coal. Mixed fuels composed of petroleum coke/coal exhibited reduced activation energies. Modified volumetric reaction model and shrinking core model might be suitably matched with experimental data depending on coal type and petroleum coke/coal ratio. Rate equations were suggested by selecting gas-solid reaction rate models for each sample that could simulate CO₂ gasification behavior.     

Prevalence and Characteristics of Phenicol-Oxazolidinone Resistance Genes in Enterococcus Faecalis and Enterococcus Faecium Isolated from Food-Producing Animals and Meat in Korea

The use of phenicol antibiotics in animals has increased. In recent years, it has been reported that the transferable gene mediates phenicol-oxazolidinone resistance. This study analyzed the prevalence and characteristics of phenicol-oxazolidinone resistance genes in Enterococcus faecalis and Enterococcus faecium isolated from food-producing animals and meat in Korea in 2018. Furthermore, for the first time, we reported the genome sequence of E. faecalis strain, which possesses the phenicol-oxazolidinone resistance gene on both the chromosome and plasmid. Among the 327 isolates, optrA, poxtA, and fexA genes were found in 15 (4.6%), 8 (2.5%), and 17 isolates (5.2%), respectively. Twenty E. faecalis strains carrying resistance genes belonged to eight sequence types (STs), and transferability was found in 17 isolates. The genome sequences revealed that resistant genes were present in the chromosome or plasmid, or both. In strains EFS17 and EFS108, optrA was located downstream of the ermA and ant(9)-1 genes. The strains EFS36 and EFS108 harboring poxtA-encoding plasmid cocarried fexA and cfr(D). These islands also contained IS1216E or the transposon Tn554, enabling the horizontal transfer of the phenicol-oxazolidinone resistance with other antimicrobial-resistant genes. Our results suggest that it is necessary to promote the prudent use of antibiotics through continuous monitoring and reevaluation.     

An augmented EDA with dynamic diversity control and local neighborhood search for coevolution of optimal negotiation strategies

In this paper, we present an estimation of distribution algorithm (EDA) augmented with enhanced dynamic diversity controlling and local improvement methods to solve competitive coevolution problems for agent-based automated negotiations. Since optimal negotiation strategies ensure that interacting agents negotiate optimally, finding such strategies—particularly, for the agents having incomplete information about their opponents—is an important and challenging issue to support agent-based automated negotiation systems. To address this issue, we consider the problem of finding optimal negotiation strategies for a bilateral negotiation between self-interested agents with incomplete information through an EDA-based coevolution mechanism. Due to the competitive nature of the agents, EDAs should be able to deal with competitive coevolution based on two asymmetric populations each consisting of self-interested agents. However, finding optimal negotiation solutions via coevolutionary learning using conventional EDAs is difficult because the EDAs suffer from premature convergence and their search capability deteriorates during coevolution. To solve these problems, even though we have previously devised the dynamic diversity controlling EDA (D²C-EDA), which is mainly characterized by a diversification and refinement (DR) procedure, D²C-EDA suffers from the population reinitialization problem that leads to a computational overhead. To reduce the computational overhead and to achieve further improvements in terms of solution accuracy, we have devised an improved D²C-EDA (ID²C-EDA) by adopting an enhanced DR procedure and a local neighborhood search (LNS) method. Favorable empirical results support the effectiveness of the proposed ID²C-EDA compared to conventional and the other proposed EDAs. Furthermore, ID²C-EDA finds solutions very close to the optimum.     

Ensemble of multi-task deep convolutional neural networks using transfer learning for fruit freshness classification

Multimedia Tools and Applications - Automatic classification of fruit freshness plays an important role in the agriculture industry. In this work, we propose an ensemble model that combines the...