Electrochemical catalytic reductive cross couplings are powerful and sustainable methods to construct C−C bonds by using electron as the clean reductant. However, activated substrates are used in most cases. Herein, we report a general and practical electro-reductive Ni-catalytic system, realizing the electrocatalytic carboxylation of unactivated aryl chlorides and alkyl bromides with CO2. A variety of unactivated aryl bromides, iodides and sulfonates can also undergo such a reaction smoothly. Notably, we also realize the catalytic electrochemical carboxylation of aryl (pseudo)halides with CO2 avoiding the use of sacrificial electrodes. Moreover, this sustainable and economic strategy with electron as the clean reductant features mild conditions, inexpensive catalyst, safe and cheap electrodes, good functional group tolerance and broad substrate scope. Mechanistic investigations indicate that the reaction might proceed via oxidative addition of aryl halides to Ni(0) complex, the reduction of aryl-Ni(II) adduct to the Ni(I) species and following carboxylation with CO2.

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Numerous compounds, including weak bases (e.g., glucosamine, ethylenediamine, and pyridine) and weak acids (e.g., bicarbonate, acetate, propionate, and butyrate), were found to inhibit the catalysis of cephalosporin C acylase (CCA), which is a recombinant enzyme expressed in E. coli. Additionally, the protective effect of the inhibitors on free and immobilized CCA against heat treatment was investigated. The inhibitors were added to increase recovery of the activity of the enzyme immobilized by covalent attachment to an epoxy support. The activities of immobilized CCA obtained in the presence of acetate or bicarbonate were 99.2±2.5 U g-1 and 94.1±3.0 U g-1 , respectively, which were 31.7 % and 25 % higher, respectively, than that of the control. In addition, the immobilized CCA exhibited improved thermostability. The half-life of immobilized CCA obtained in the presence of acetate or bicarbonate increased by 190 % and 120 %, respectively, compared to that of immobilized CCA obtained in the absence of an inhibitor.

Recently, with the rapid development of deep learning (DL), an increasing number of DL-based methods are applied in pansharpening. Benefiting from the powerful feature extraction capability of deep learning, DL-based methods have achieved state-of-the-art performance in pansharpening. However, most DL-based methods simply fuse multi-spectral (MS) images and panchromatic (PAN) images by concatenating, which can not make full use of the spectral information and spatial information of MS and PAN images, respectively. To address this issue, we propose a spectral-spatial interaction Network (SSIN) for pansharpening. Different from previous works, we extract the features of PAN and MS, respectively, and then interact them repetitively to incorporate spectral and spatial information progressively. In order to enhance the spectral-spatial information fusion, we further propose spectral-spatial attention (SSA) module to yield a more effective spatial-spectral information transfer in the network. Extensive experiments on QuickBird, WorldView-4, and WorldView-2 images demonstrate that our SSIN significantly outperforms other methods in terms of both objective assessment and visual quality.

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