A Simple Approach for Regenerating Electrolyzed Hydrogen Production Using Non-De-Ionized Water Sources.

This research focuses on using natural renewable water resources, filters, and performance recovery systems to reduce the cost of generating pure hydrogen for Proton Exchange Membrane Fuel Cells (PEMFCs). This study uses de-ionized (DI) water, tap water, and river water from upstream as the water source. Water from these sources passes through 1 µm PP filters, activated carbon, and reverse osmosis for filtering. The filtered water then undergoes hydrogen production experiments for a duration of 6000 min. Performance recovery experiments follow directly after hydrogen production experiments. The hydrogen production experiments show the following: DI water yielded a hydrogen production rate of 27.13 mL/min; unfiltered tap water produced 15.41 mL/min; unfiltered upstream river water resulted in 10.03 mL/min; filtered tap water yielded 19.24 mL/min; and filtered upstream river water generated 18.54 mL/min. Performance recovery experiments conducted by passing DI water into PEMFCs for 15 min show that the hydrogen generation rate of tap water increased to 25.73 mL/min, and the rate of hydrogen generation of upstream river water increased to 22.58 mL/min. In terms of cost-effectiveness, under the same volume of hydrogen production (approximately 600 kg/year), using only DI water costs 1.8-times more than the cost of using filtered tap water in experiments.

Correction to "On the Performance of Proton Exchange Membrane Fuel Cells with a Catalyst Layer Fabricated Using an Inorganic Dispersant with Various Ultrasonic Mixing".

[This corrects the article DOI: 10.1021/acsomega.1c05777.].

On the Performance of Proton Exchange Membrane Fuel Cells with a Catalyst Layer Fabricated Using an Inorganic Dispersant with Various Ultrasonic Mixing.

This study utilizes both an inorganic dispersant, montmorillonite, and an organic dispersant (AS-1164) with 1.6 and 3.2 mgPt/cm2 platinum coatings that underwent various frequencies of ultrasonic mixing (40, 80, and 120 kHz) to fabricate proton exchange membrane fuel cells (PEMFCs). The performance of these PEMFCs was then compared. At room temperature and a hydrogen gas flow rate of 15 sccm. After undergoing 3 h of vibration at 120 kHz, the 1.6 mgPt/cm2 platinum-coated organic sample has a power density of 3.69 mW/cm2, while its inorganic counterpart has an impressive power density of 4.49 mW/cm2. In addition, using the 1.6 mgPt/cm2 platinum-coated inorganic dispersants that underwent vibration at 40 kHz, its resulting power density is only 0.95 mW/cm2. This result shows that the distribution of platinum coating is more even under high-frequency vibrations than low-frequency ones.

Efficient architecture for spike sorting in reconfigurable hardware.

This paper presents a novel hardware architecture for fast spike sorting. The architecture is able to perform both the feature extraction and clustering in hardware. The generalized Hebbian algorithm (GHA) and fuzzy C-means (FCM) algorithm are used for feature extraction and clustering, respectively. The employment of GHA allows efficient computation of principal components for subsequent clustering operations. The FCM is able to achieve near optimal clustering for spike sorting. Its performance is insensitive to the selection of initial cluster centers. The hardware implementations of GHA and FCM feature low area costs and high throughput. In the GHA architecture, the computation of different weight vectors share the same circuit for lowering the area costs. Moreover, in the FCM hardware implementation, the usual iterative operations for updating the membership matrix and cluster centroid are merged into one single updating process to evade the large storage requirement. To show the effectiveness of the circuit, the proposed architecture is physically implemented by field programmable gate array (FPGA). It is embedded in a System-on-Chip (SOC) platform for performance measurement. Experimental results show that the proposed architecture is an efficient spike sorting design for attaining high classification correct rate and high speed computation.

FPGA implementation of Generalized Hebbian Algorithm for texture classification.

This paper presents a novel hardware architecture for principal component analysis. The architecture is based on the Generalized Hebbian Algorithm (GHA) because of its simplicity and effectiveness. The architecture is separated into three portions: the weight vector updating unit, the principal computation unit and the memory unit. In the weight vector updating unit, the computation of different synaptic weight vectors shares the same circuit for reducing the area costs. To show the effectiveness of the circuit, a texture classification system based on the proposed architecture is physically implemented by Field Programmable Gate Array (FPGA). It is embedded in a System-On-Programmable-Chip (SOPC) platform for performance measurement. Experimental results show that the proposed architecture is an efficient design for attaining both high speed performance and low area costs.