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Existing deep unfolding methods unroll an optimization algorithm with a fixed number of steps, and utilize convolutional neural networks (CNNs) to learn data-driven priors. However, their performance is limited for two main reasons. Firstly, priors learned in deep feature space need to be converted to the image space at each iteration step, which limits the depth of CNNs and prevents CNNs from exploiting contextual information. Secondly, existing methods only learn deep priors at the single full-resolution scale, so ignore the benefits of multi-scale context in dealing with high level noise. To address these issues, we explicitly consider the image denoising process in the deep feature space and propose the deep unfolding multi-scale regularizer network(DUMRN) for image denoising. The core of DUMRN is the feature-based denoising module(FDM) that directly removes noise in the deep feature space. In each FDM, we construct a multi-scale regularizer block to learn deep prior information from multi-resolution features. We build the DUMRN by stacking a sequence of FDMs and train it in an end-to-end manner. Experimental results on synthetic and real-world benchmarks demonstrate that DUMRN performs favorably compared to state-of-the-art methods.

Cation segregation on cathode surfaces plays a key role in determining the activity and operational stability of solid oxide fuel cells (SOFCs). The double perovskite oxide PrBa0.8Ca0.2Co2O5+δ(PBCC) has been widely studied as an active cathode but still suffer from serious detrimental segregations. To enhance the cathode stability, a PBCC derived A-site medium-entropy Pr0.6La0.1Nd0.1Sm0.1Gd0.1Ba0.8Ca0.2Co2O5+δ(ME-PBCC) oxide was prepared and its segregation behaviors were investigated under different conditions. Compared with initial PBCC oxide, the segregations of BaO and Co3O4on the surface of ME-PBCC material are significantly suppressed, especially for Co3O4, which is attributed to its higher configuration entropy. Our results also confirm the improved electrochemical performance and structural stability of ME-PBCC material, enabling it as a promising cathode for SOFCs.

Inspired by the knowledge that the chiroptical activity of Cu and Ag nanoclusters (NCs) is generally about several-fold larger than that of the corresponding Au NCs but poor stability and that alloy metal particles often demonstrate unique stability, in this contribution the synthesis of the chiral Au–Cu alloy NCs was attempted via reduction of varying mole fractions of metal precursors by sodium borohydride in the presence of d-/l-penicillamine (d-/l-Pen), to gain insights into the alloying effect on the chiroptical activity. As a result, the gfactors of ∼0.57 × 10–3, obviously larger than that of the pure Au NCs, are found for the bimetallic Au–Cu NCs, suggesting that the incorporation of Cu heteroatoms plays a dramatic role for the creation of the strong optically active metal nanostructures. More importantly, compared with those of monometallic Cu counterparts, the optical active stability of bimetallic Au–Cu NCs is obviously improved because of the alloy formation. The location of the copper dopant, observed from the transmission electron microscopy results and deduced by the X-ray photoelectron spectroscopy measurements, can be speculated to be incorporated in both the “core” region and the “staple” region. Additionally, it was found that the different thiolated chiral molecule ligands exert influence on the chiroptical behavior of the Au–Cu NCs. Thus, the alloy formation strategy could produce novel nanostructures with the characteristic of large and stable chiroptical activity.

Compared to conventional Ba0.5Sr0.5Co0.8Fe0.2O3−δ, Bi0.675Sm0.025Sr0.3FeO3−δdemonstrates excellent electrochemical performance and superior CO2resistance. The better CO2tolerance is attributed to the larger average bonding energy (ABE) and the higher acidity of Bi/Sm/Fe.

Understanding the dynamic surface reconstruction is of vital importance for the development of oxygen evolution reaction (OER) electrocatalysts. Herein, we report FeOOH decorated NiMoO4nanowires as OER pre-catalysts. In-situRaman spectroscopy elucidates that modified NiMoO4undergoes fast reconstruction, including significant Mo leaching and simultaneous formation of Ni(Fe)OOH intermediates, which are determined as the real active species toward OER. Rapid and deep reconstruction results in amorphous nanowires with uniform Fe distribution and great size expansion. Impressively, the reconstructed pre-catalyst exhibits outstanding catalytic activity, ultra-low overpotential and extraordinary long-term stability under high current densities. Our work provides a facile and effective surface engineering approach of pre-catalysts and highlights the evolution of self-reconstruction chemistry for accelerating the kinetics of OER.