Rational Design of NiZnx@CuO Nanoarray Architectures for Electrocatalytic Oxidation of Methanol.

Methanol oxidation reaction (MOR) in anodes is one of the significant aspects of direct methanol fuel cells (DMFCs), which also plays a critical role in achieving a carbon-neutral economy. Designing and developing efficient, cost-effective, and durable non-Pt group metal-based methanol oxidation catalysts are highly desired, but a gap still remains. Herein, we report well-defined hierarchical NiZnx@CuO nanoarray architectures as active electrocatalysts for MOR, synthesized by combining thermal oxidation treatment and magnetron sputtering deposition through a brass mesh precursor. After systematically evaluating the electrocatalytic performance of NiZnx@CuO nanoarray catalysts with different preparation conditions, we found that the NiZn1000@CuO (thermally oxidized at 500 °C for 2 h, nominal thickness of the NiZn alloy film is 1000 nm) electrode delivers a high current density of 449.3 mA cm-2 at 0.8 V for MOR in alkaline media as well as excellent operation stability (92% retention after 12 h). These outstanding MOR performances can be attributed to the hierarchical well-defined structure that can not only render abundant active sites and a synergistic effect to enhance the electrocatalytic activity but also can effectively facilitate mass and electron transport. More importantly, we found that partial Zn atoms could leach from the NiZn alloy, resulting in rough surface nanorods, which would further increase the specific surface area. These results indicate that the NiZn1000@CuO nanoarray architecture could be a promising Pt group metal alternative as an efficient, cost-effective, and durable anode catalyst for DMFCs.

The Tunable and Efficient Nanoporous CuAg Alloy Catalysts Toward Methanol Oxidation Reaction Synthesized by Electrochemical Dealloying of Metallic Glassy Precursors.

Three-dimensional (3D) nanoporous CuAg (NPCuAg) alloy catalysts with various Cu/Ag ratios are prepared by electrochemical dealloying of metallic glassy (MG) precursors. All dealloyed samples exhibit homogenous nanoporous structure and element composition distribution. After systematically evaluating their electrocatalytic performance toward MOR, it was found that the catalytic activity of the NPCuAg catalysts is enhanced along with the increase of Cu/Ag ratio, which may be attributed to the more exposed active reaction sites derived from high surface area of nanoporous structure and the optimal synergistic effect. Thus, the NPCu1.75 Ag alloy catalyst presents the best methanol electro-oxidation properties, including a high current density of 397.2 mA cm-2 and good operation stability that retaining 84.5 % catalytic activity even after 7200 s. These results outperform most reported copper-based MOR catalysts in alkaline methanol solution. Considering these advantages, the designed electrodes are expected to be promising catalysts for alkaline DMFCs applications.

An Off-Grid Turbo Channel Estimation Algorithm for Millimeter Wave Communications.

The bandwidth shortage has motivated the exploration of the millimeter wave (mmWave) frequency spectrum for future communication networks. To compensate for the severe propagation attenuation in the mmWave band, massive antenna arrays can be adopted at both the transmitter and receiver to provide large array gains via directional beamforming. To achieve such array gains, channel estimation (CE) with high resolution and low latency is of great importance for mmWave communications. However, classic super-resolution subspace CE methods such as multiple signal classification (MUSIC) and estimation of signal parameters via rotation invariant technique (ESPRIT) cannot be applied here due to RF chain constraints. In this paper, an enhanced CE algorithm is developed for the off-grid problem when quantizing the angles of mmWave channel in the spatial domain where off-grid problem refers to the scenario that angles do not lie on the quantization grids with high probability, and it results in power leakage and severe reduction of the CE performance. A new model is first proposed to formulate the off-grid problem. The new model divides the continuously-distributed angle into a quantized discrete grid part, referred to as the integral grid angle, and an offset part, termed fractional off-grid angle. Accordingly, an iterative off-grid turbo CE (IOTCE) algorithm is proposed to renew and upgrade the CE between the integral grid part and the fractional off-grid part under the Turbo principle. By fully exploiting the sparse structure of mmWave channels, the integral grid part is estimated by a soft-decoding based compressed sensing (CS) method called improved turbo compressed channel sensing (ITCCS). It iteratively updates the soft information between the linear minimum mean square error (LMMSE) estimator and the sparsity combiner. Monte Carlo simulations are presented to evaluate the performance of the proposed method, and the results show that it enhances the angle detection resolution greatly.