The clustered property among sparse weight vector is prevalent in applications such as direction of arrival (DOA) estimation. To address this, block structure has been utilized to capture the clustered information expressed by the continuity of nonzero elements in a sparse weight vector. However, many compressive sensing (CS)-based methods exploit inter-block sparsity while ignoring intra-block correlations. Sparse Bayesian learning (SBL)-based methods with parameterized distribution provide a flexible approach for handling block structures, but most of them assume known block partitioning. In addition, off-grid problem and multi-measurement vector (MMV) scenario are also common and challenging issues in CS-based methods. In this work, we fully utilize the potential clustered structure in the sparse solution of DOA estimation and derive a structure-aware SBL framework (SASBL) in MMV scenarios. The structure-aware matrix characterizes the correlation within each block, and the weight vector can describe the inter-block sparsity. A new learning rule for the weight vector is derived based on the Majorization-Minimization algorithm. Furthermore, the off-grid parameter is incorporated into the SASBL framework to obtain higher DOA estimation resolution. Simulation results demonstrate that the proposed SASBL outperforms subspace-based, typical SBL-based, off-grid SBL-based and gridless methods.

To meet the requirements for the potential applications of fuel cells, it is of vital importance to search for advanced electrocatalysts toward the methanol oxidation reaction (MOR) that have both high electrocatalytic activity and great CO resistance. In this work, we herein report the fabrication of a novel class of Pt–CuO–TiO₂/C catalyst and discover that the as-obtained nanocatalyst exhibited excellent electrocatalytic properties for the MOR. Remarkably, the mass activity is as high as 1537.3 mA mg_Pt ⁻¹, showing 6.0- and 2.75-fold enhancements compared with the reference Pt–C–H and Pt–TiO₂/C catalysts, respectively. Except the superb electrocatalytic activity, it is also demonstrated that such Pt–CuO–TiO₂/C has outstanding electrochemical stability and great tolerance to CO poisoning, representing an advanced electrocatalyst for use in fuel cells.

DMFCs are promising power storage devices, while for methanol oxidation reaction, weak catalysis and carbon monoxide poisoning greatly limit their wide commercialization, so it's greatly necessary to exploit the anode catalysts with high performance.