Hierarchical PtCuMnP Nanoalloy for Efficient Hydrogen Evolution and Methanol Oxidation.

The higher amount of Pt usage and its poisoning in methanol oxidation reaction in acidic media is a major setback for methanol fuel cells. Herein, a promising dual application high-performance electrocatalyst has been developed for hydrogen evolution and methanol oxidation. A low Pt-content nanoalloy co-doped with Cu, Mn, and P is synthesized using a modified solvothermal process. Initially, ultrasmall ≈2.9 nm PtCuMnP nanoalloy is prepared on N-doped graphene-oxide support and subsequently, it is characterized using several analytical techniques and examined through electrochemical tests. Electrochemical results show that PtCuMnP/N-rGO has a low overpotential of 6.5 mV at 10 mA cm-2 in 0.3 m H2 SO4 and high mass activity for the hydrogen evolution reaction. For the methanol oxidation reaction, the PtCuMnP/N-rGO electrocatalyst exhibits robust performance. The mass activity of PtCuMnP/N-rGO is 6.790 mA mg-1 Pt , which is 7.43 times higher than that of commercial Pt/C (20% Pt). Moreover, in the chronoamperometry test, PtCuMnP/N-rGO shows exceptionally good stability and retains 72% of the initial current density even after 20,000 cycles. Furthermore, the PtCuMnP/N-rGO electrocatalyst exhibits outstanding performance for hydrogen evolution and methanol oxidation along with excellent anti-poisoning ability. Hence, the developed bifunctional electrocatalyst can be used efficiently for hydrogen evolution and methanol oxidation.

Real-time Neural Connectivity Inference with Presynaptic Spike-driven Spike Timing-Dependent Plasticity.

Brain-like artificial intelligence in electronics can be built efficiently by understanding the connectivity of neuronal circuitry. The concept of neural connectivity inference with a two-dimensional cross-bar structure memristor array is indicated in recent studies; however, large-scale implementation is challenging owing to device variations and the requirement of online parameter adaptation. This study proposes a neural connectivity inference method with one-dimensional spiking neurons using spike timing-dependent plasticity and presynaptic spike-driven spike timing-dependent plasticity learning rules, designed for a large-scale neuromorphic system. The proposed learning process decreases the number of spiking neurons by half. We simulate 12 ground-truth neural networks comprising one-dimensional eight and 64 neurons. We analyze the correlation between the neural connectivity of the ground truth and spiking neural networks using the Matthews correlation coefficient. In addition, we analyze the sensitivity and specificity of inference. Validation using the presynaptic spike-driven spike timing-dependent plasticity learning rule implies a potential approach for large-scale neural network inference with real hardware realization of large-scale neuromorphic systems.

Enhanced Thermal Stability and Conductivity of FeF3 Using Ni-Coated Carbon Composites: Application as High-Temperature Cathodes in Thermal Batteries.

Cathode active materials and conductive additives for thermal batteries operating at high temperatures have attracted research interest, with a particular focus on compounds offering high thermal stability. Recently, FeF3 has been proposed as a candidate for high-voltage cathode materials; however, its commercialization is hindered by its low conductivity. In this study, conductive additives, such as Ni-coated carbon composites (multi-walled carbon nanotubes (MWCNTs) and carbon black (CB)), were utilized to enhance the thermal stability and conductivity of FeF3. The incorporation of metal-carbon conductive additives in the FeF3 composite increased the thermal stability by more than 10 wt.% and ensured high capacity upon conductivity enhancement. The FeF3@Ni/MWCB 15 wt.% composite containing 30 wt.% Ni exhibited a discharge capacity of ∼86% of the theoretical capacity of 712 mAh/g. The use of Ni-coated carbon-based conductive additives will allow the application of FeF3 as an effective high-temperature cathode material for thermal batteries.

FeF3/(Acetylene Black and Multi-Walled Carbon Nanotube) Composite for Cathode Active Material of Thermal Battery through Formation of Conductive Network Channels.

Considerable research is being conducted on the use of FeF3 as a cathode replacement for FeS2 in thermal batteries. However, FeF3 alone is inefficient as a cathode active material because of its low electrical conductivity due to its wide bandgap (5.96 eV). Herein, acetylene black and multi-walled carbon nanotubes (MWCNTs) were combined with FeF3, and the ratio was optimized. When acetylene black and MWCNTs were added separately to FeF3, the electrical conductivity increased, but the mechanical strength decreased. When acetylene black and MWCNTs were both added to FeF3, the FeF3/M1AB4 sample (with 1 wt.% MWCNTs and 4% AB) afforded a discharge capacity of approximately 74% of the theoretical capacity (712 mAh/g) of FeF3. Considering the electrical conductivity and mechanical strength, this composition was confirmed to be the most suitable.

Anion Exchange Membranes for Fuel Cell Application: A Review.

The fuel cell industry is the most promising industry in terms of the advancement of clean and safe technologies for sustainable energy generation. The polymer electrolyte membrane fuel cell is divided into two parts: anion exchange membrane fuel cells (AEMFCs) and proton exchange membrane fuel cells (PEMFCs). In the case of PEMFCs, high-power density was secured and research and development for commercialization have made significant progress. However, there are technical limitations and high-cost issues for the use of precious metal catalysts including Pt, the durability of catalysts, bipolar plates, and membranes, and the use of hydrogen to ensure system stability. On the contrary, AEMFCs have been used as low-platinum or non-platinum catalysts and have a low activation energy of oxygen reduction reaction, so many studies have been conducted to find alternatives to overcome the problems of PEMFCs in the last decade. At the core of ensuring the power density of AEMFCs is the anion exchange membrane (AEM) which is less durable and less conductive than the cation exchange membrane. AEMFCs are a promising technology that can solve the high-cost problem of PEMFCs that have reached technological saturation and overcome technical limitations. This review focuses on the various aspects of AEMs for AEMFCs application.

Exergy Analysis of a Direct Contact Membrane Distillation (DCMD) System Based on Computational Fluid Dynamics (CFD).

Understanding the energy efficiency of direct contact membrane distillation (DCMD) is important for the widespread application and practical implementation of the process. This study analyzed the available energy, known as exergy, in a DCMD system using computational fluid dynamics (CFD). A CFD model was developed to investigate the hydrodynamic and thermal conditions in a DCMD module. After the CFD model was verified, it was used to calculate the temperature polarization coefficient (TPC) and exergy destruction magnitudes under various operating conditions. The results revealed that slight decreases and increases in the TPC occurred with distance from the inlet in the module. The TPC was found to increase as the feed temperature was reduced and the feed and permeate flow rates were increased. The exergy destruction phenomenon was more significant under higher feed temperatures and higher flux conditions. Although the most significant exergy destruction in the permeate occurred near the feed inlet, the effect became less influential closer to the feed outlet. An analysis of exergy flows revealed that the efficiency loss in the permeate side corresponded to 32.9-45.3% of total exergy destruction.