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The urea oxidation reaction (UOR) is a possible solution to solve the world’s energy crisis. Fuel cells have been used in the UOR to generate hydrogen with a lower potential compared to water splitting, decreasing the costs of energy production. Urea is abundantly present in agricultural waste and in industrial and human wastewater. Besides generating hydrogen, this reaction provides a pathway to eliminate urea, which is a hazard in the environment and to people’s health. In this study, nanosheets of CuCo2O4 grown on nickel foam were synthesized as an electrocatalyst for urea oxidation to generate hydrogen as a green fuel. The synthesized electrocatalyst was characterized using X-ray diffraction, scanning electron microscopy, and X-ray photoelectron spectroscopy. The electroactivity of CuCo2O4 towards the oxidation of urea in alkaline solution was evaluated using electrochemical measurements. Nanosheets of CuCo2O4 grown on nickel foam required the potential of 1.36 V in 1 M KOH with 0.33 M urea to deliver a current density of 10 mA/cm2. The CuCo2O4 electrode was electrochemically stable for over 15 h of continuous measurements. The high catalytic activities for the hydrogen evolution reaction make the CuCo2O4 electrode a bifunctional catalyst and a promising electroactive material for hydrogen production. The two-electrode electrolyzer demanded a potential of 1.45 V, which was 260 mV less than that for the urea-free counterpart. Our study suggests that the CuCo2O4 electrode can be a promising material as an efficient UOR catalyst for fuel cells to generate hydrogen at a low cost. 

Abstract The rapid development of general-purpose computing on graphics processing units (GPGPU) is allowing the implementation of highly-parallelized Monte Carlo simulation chains for particle physics experiments. This technique is particularly suitable for the simulation of a pixelated charge readout for time projection chambers, given the large number of channels that this technology employs. Here we present the first implementation of a full microphysical simulator of a liquid argon time projection chamber (LArTPC) equipped with light readout and pixelated charge readout, developed for the DUNE Near Detector. The software is implemented with an end-to-end set of GPU-optimized algorithms. The algorithms have been written in Python and translated into CUDA kernels using Numba, a just-in-time compiler for a subset of Python and NumPy instructions. The GPU implementation achieves a speed up of four orders of magnitude compared with the equivalent CPU version. The simulation of the current induced on 10^3 pixels takes around 1 ms on the GPU, compared with approximately 10 s on the CPU. The results of the simulation are compared against data from a pixel-readout LArTPC prototype. 

Abstract The ProtoDUNE-SP detector is a single-phase liquid argon time projection chamber (LArTPC) that was constructed and operated in the CERN North Area at the end of the H4 beamline. This detector is a prototype for the first far detector module of the Deep Underground Neutrino Experiment (DUNE), which will be constructed at the Sandford Underground Research Facility (SURF) in Lead, South Dakota, U.S.A. The ProtoDUNE-SP detector incorporates full-size components as designed for DUNE and has an active volume of 7 × 6 × 7.2 m 3 . The H4 beam delivers incident particles with well-measured momenta and high-purity particle identification. ProtoDUNE-SP's successful operation between 2018 and 2020 demonstrates the effectiveness of the single-phase far detector design. This paper describes the design, construction, assembly and operation of the detector components.