Crystalline metal phosphide-coated amorphous iron oxide-hydroxide (FeOOH) with oxygen vacancies as highly active and stable oxygen evolution catalyst in alkaline seawater at high current density.

In this study, we employed a straightforward phosphorylation approach to achieve a dual objective: constructing c-a heterostructures consisting of crystalline Ni12P5 and amorphous FeOOH, while simultaneously enhancing oxygen vacancies. The resulting oxygen evolution reaction (OER) catalyst, Ni12P5/FeOOH/NF, exhibited remarkable performance with current densities of 500 mA cm-2 in both 1 M KOH and 1 M KOH + seawater, requiring low overpotentials of only 288 and 365 mV, respectively. Furthermore, Ni12P5/FeOOH/NF exhibited only a slight increase in overpotential, with increments of 18 mV and 70 mV in 1 M KOH after 15 and 150 h, and 32 mV and 108 mV in 1 M KOH + seawater at 500 mA cm-2 after 15 and 150 h, respectively. This minimal change can be attributed to the stabilized c-a structure, the protective coating of Ni12P5, and superhydrophilic. Through in-situ Raman and ex-situ XPS analysis, we discovered that Ni12P5/FeOOH/NF can undergo a reconfiguration into an oxygen vacancy-rich (Fe/Ni)OOH phase during OER process. The elevated OER activity is mainly due to the contribution of the oxygen vacancy-rich (Fe/Ni)OOH phase from the reconfigure of the Ni12P5/FeOOH/NF. This finding emphasizes the critical role of oxygen vacancies in facilitating the production of OO species and overcoming the limitations associated with OOH formation, ultimately enhancing the kinetics of the OER.

Enhanced Activity of Small Pt Nanoparticles Decorated with High-Loading Single Fe─N4 for Methanol Oxidation and Oxygen Reduction via the Assistive Active Sites Strategy.

Decorating platinum (Pt) with a single atom offers a promising approach to tailoring their catalytic activity. In this study, for the first time, an innovative assistive active sites (AAS) strategy is proposed to construct high-loading (3.46wt.%) single Fe─N4 as AAS, which are further hybridized with small Pt nanoparticles to enhance both oxygen reduction reaction (ORR) and methanol oxidation reaction (MOR) activities. For ORR, the target catalyst (Pt/HFeSA-HCS) exhibits a higher mass activity (MA) of 0.98 A mgPt -1 and specific activity (SA) of 1.39 mA cmPt -2 at 0.90 V versus RHE. As for MOR, Pt/HFeSA-HCS shows exceptional MA (3.21 A mgPt -1) and SA (4.27 mA cmPt -2) at peak values, surpassing commercial Pt/C by 15.3 and 11.5 times, respectively. The underlying mechanism behind this AAS strategy is to find that in MOR, Fe─N4 promotes water dissociation, generating more *OH to accelerate the conversion of *CO to CO2. Meanwhile, in ORR, Fe─N4 acts as a competitor to adsorb *OH, weakening Pt─OH bonding and facilitating desorption of *OH on the Pt surface. Constructing AAS that can enhance dual functionality simultaneously can be seen as a successful "kill two birds with one stone" strategy.

Core-Shell ZIF-8@ZIF-67-Derived Cobalt Nanoparticle-Embedded Nanocage Electrocatalyst with Excellent Oxygen Reduction Performance for Zn-Air Batteries.

Metal-nitrogen-carbon (M-N-C) catalysts obtained from zeolitic imidazolate frameworks (ZIFs) have great potential in the oxygen reduction reaction (ORR). Herein, based on the same three-dimensional (3D) topological structure of ZIF-67 and ZIF-8, ZIF-67 is grown on the ZIF-8 surface by the epitaxial growth method, and ZIF-8 is used as a sacrificial template to obtain a Co-embedded layered porous carbon nanocage (CoPCN) electrocatalyst. Meanwhile, the self-sacrificing template effectively improves the specific surface area of the porous structure and reduces the depletion of active sites. The CoPCN shows a high half-wave potential of 0.885 V and superior stability as well as excellent methanol resistance. Theoretical calculations demonstrate that the Co-N1-C2 sites of CoPCN effectively reduce the energy barrier of ORR. In addition, a zinc-air battery (ZAB) based on the CoPCN exhibits excellent peak power density (90 mW cm-2) and superior cycle performance. This work presents a novel idea in the design of ZIF precursor systems to synthesize efficient ORR catalysts.

Effects of total flavones of Abelmoschus manihot (L.) on the treatment of diabetic nephropathy via the activation of solute carriers in renal tubular epithelial cells.

As a traditional Chinese medicine, Huangkui capsule (HKC) has been used to treat patients with kidney diseases, including diabetic nephropathy (DN). We have recently demonstrated that HKC could re-regulate the activities of solute carriers (SLC)s in proximal and distal convoluted tubules of kidneys in regression of the development of DN. The main active chemical constituents of HKC are the flavones of Abelmoschus manihot (L.). The current study aims to further evaluate the efficacy of total flavones of A. manihot (TFA) in the regression of DN by analyzing SLC activities in proximal and distal convoluted tubules of kidneys. TFA (0.076 g/kg/d) or vehicle was administered in db/db mice, the animal model of type 2 diabetes and DN, daily via oral gavage for four weeks. Blood glucose levels and urinary albumin-to-creatinine ratio (UACR) were measured and used for the determination of T2D and DN. Ten SLCs, including slc2a2, slc4A1, slc5a2, slc5A3, slc5a8, slc6a20, slc27a2, slc12a3, slc34a1 and slc38a2 were highly expressed in proximal and distinct convoluted tubules of kidneys. Their expression at mRNA and protein levels before and after TFA treatment were analyzed with real-time RT-PCR and immunohistochemistry. Data showed that UACR in the db/db mice after TFA treatment was significantly decreased. Compared with the group of non-diabetic control, slc2a2, slc4A1, slc5a2, slc5A3, slc5a8, slc6a20, slc27a2, slc12a3, slc34a1 and slc38a2 in the group of DN were down-regulated but up-regulated after TFA treatment. Further analyses of whole kidney sections indicated that the numbers and structures of the nephron in db/db mice was increased and improved after TFA treatment. Thereby, the current study provides further evidence that the flavones in A. manihot have pharmacological effects on the treatment of DN by improving the biological function of SLCs in kidneys.

A facile strategy to prepare FeNx decorated PtFe intermetallic with excellent acidic oxygen reduction reaction activity and stability.

The construction of low-Pt-content intermetallic on carbon supports has been verified as a promising method to promote the activity of the oxygen reduction reaction (ORR). In this study, we have developed a simple and effective strategy to obtain a well-designed CNT-PtFe-PPy precursor. This precursor contains modulated Pt- and Fe-based content dispersed in polypyrrole (PPy) chain segments, which are in-situ generated on the templates of carbon nanotubes (CNTs). Subsequent pyrolysis of the CNT-PtFe-PPy precursor produces a CNT-PtFe@FeNC catalyst, which contains both Fe-Nx and PtFe intermetallic active sites. Due to the highly efficient dispersion of active species, the CNT-PtFe@FeNC electrocatalyst displays a 9.5 times higher specific activity (SA) and 8.5 times higher mass activity (MA) than those of a commercial Pt/C catalyst in a 0.1 M HClO4 solution. Additionally, these results, combined with excellent durability (the SA and MA maintained 94 % and 91 % of initial activity after a 10-k cycle accelerated durability test), represent among the best performance achieved so far for Pt-based ORR electrocatalysts. Furthermore, density functional theory (DFT) calculations revealed that the presence of Fe-N4 species reduces the adsorption energy between the PtFe intermetallic compound and OH*, accelerating the ORR process.

Robust to Rank Selection: Low-Rank Sparse Tensor-Ring Completion.

Tensor-ring (TR) decomposition was recently studied and applied for low-rank tensor completion due to its powerful representation ability of high-order tensors. However, most of the existing TR-based methods tend to suffer from deterioration when the selected rank is larger than the true one. To address this issue, this article proposes a new low-rank sparse TR completion method by imposing the Frobenius norm regularization on its latent space. Specifically, we theoretically establish that the proposed method is capable of exploiting the low rankness and Kronecker-basis-representation (KBR)-based sparsity of the target tensor using the Frobenius norm of latent TR-cores. We optimize the proposed TR completion by block coordinate descent (BCD) algorithm and design a modified TR decomposition for the initialization of this algorithm. Extensive experimental results on synthetic data and visual data have demonstrated that the proposed method is able to achieve better results compared to the conventional TR-based completion methods and other state-of-the-art methods and, meanwhile, is quite robust even if the selected TR-rank increases.

Online subspace learning and imputation by Tensor-Ring decomposition.

This paper considers the completion problem of a partially observed high-order streaming data, which is cast as an online low-rank tensor completion problem. Though the online low-rank tensor completion problem has drawn lots of attention in recent years, most of them are designed based on the traditional decomposition method, such as CP and Tucker. Inspired by the advantages of Tensor Ring decomposition over the traditional decompositions in expressing high-order data and its superiority in missing values estimation, this paper proposes two online subspace learning and imputation methods based on Tensor Ring decomposition. Specifically, we first propose an online Tensor Ring subspace learning and imputation model by formulating an exponentially weighted least squares with Frobenium norm regularization of TR-cores. Then, two commonly used optimization algorithms, i.e. alternating recursive least squares and stochastic-gradient algorithms, are developed to solve the proposed model. Numerical experiments show that the proposed methods are more effective to exploit the time-varying subspace in comparison with the conventional Tensor Ring completion methods. Besides, the proposed methods are demonstrated to be superior to obtain better results than state-of-the-art online methods in streaming data completion under varying missing ratios and noise.

Low Tensor-Ring Rank Completion by Parallel Matrix Factorization.

Tensor-ring (TR) decomposition has recently attracted considerable attention in solving the low-rank tensor completion (LRTC) problem. However, due to an unbalanced unfolding scheme used during the update of core tensors, the conventional TR-based completion methods usually require a large TR rank to achieve the optimal performance, which leads to high computational cost in practical applications. To overcome this drawback, we propose a new method to exploit the low TR-rank structure in this article. Specifically, we first introduce a balanced unfolding operation called tensor circular unfolding, by which the relationship between TR rank and the ranks of tensor unfoldings is theoretically established. Using this new unfolding operation, we further propose an algorithm to exploit the low TR-rank structure by performing parallel low-rank matrix factorizations to all circularly unfolded matrices. To tackle the problem of nonuniform missing patterns, we apply a row weighting trick to each circularly unfolded matrix, which significantly improves the adaptive ability to various types of missing patterns. The extensive experiments have demonstrated that the proposed algorithm can achieve outstanding performance using a much smaller TR rank compared with the conventional TR-based completion algorithms; meanwhile, the computational cost is reduced substantially.