Deep neural networks (DNNs) have shown very promising results for various image restoration (IR) tasks. However, the design of network architectures remains a major challenging for achieving further improvements. While most existing DNN-based methods solve the IR problems by directly mapping low quality images to desirable high-quality images, the observation models characterizing the image degradation processes have been largely ignored. In this paper, we first propose a denoising-based IR algorithm, whose iterative steps can be computed efficiently. Then, the iterative process is unfolded into a deep neural network, which is composed of multiple denoisers modules interleaved with back-projection (BP) modules that ensure the observation consistencies. A convolutional neural network (CNN) based denoiser that can exploit the multiscale redundancies of natural images is proposed. As such, the proposed network not only exploits the powerful denoising ability of DNNs, but also leverages the prior of the observation model. Through end-to-end training, both the denoisers and the BP modules can be jointly optimized. Experimental results on several IR tasks, e.g., image denoisig, super-resolution and deblurring show that the proposed method can lead to very competitive and often state-of-the-art results on several IR tasks, including image denoising, deblurring and super-resolution.Index Terms-denoising-based image restoration, deep neural network, denoising prior, image restoration.
Hydrogen evolution reaction performance of MoS can be enhanced through electric-field-facilitated electron transport. The best catalytic performance of a MoS nanosheet can achieve an overpotential of 38 mV (100 mA cm ) at gate voltage of 5 V, the strategy of utilizing the electric field can be used in other semiconductor materials to improve their electrochemical catalysis for future relevant research.
Biological fluoride ion channels are sub-1-nanometer protein pores with ultrahigh F
−
conductivity and selectivity over other halogen ions. Developing synthetic F
−
channels with biological-level selectivity is highly desirable for ion separations such as water defluoridation, but it remains a great challenge. Here we report synthetic F
−
channels fabricated from zirconium-based metal-organic frameworks (MOFs), UiO-66-X (X = H, NH
2
, and N
+
(CH
3
)
3
). These MOFs are comprised of nanometer-sized cavities connected by sub-1-nanometer-sized windows and have specific F
−
binding sites along the channels, sharing some features of biological F
−
channels. UiO-66-X channels consistently show ultrahigh F
−
conductivity up to ~10 S m
−1
, and ultrahigh F
−
/Cl
−
selectivity, from ~13 to ~240. Molecular dynamics simulations reveal that the ultrahigh F
−
conductivity and selectivity can be ascribed mainly to the high F
−
concentration in the UiO-66 channels, arising from specific interactions between F
−
ions and F
−
binding sites in the MOF channels.
Transition metal dichalcogenides, such as MoS and VSe have emerged as promising catalysts for the hydrogen evolution reaction (HER). Substantial work has been devoted to optimizing the catalytic performance by constructing materials with specific phases and morphologies. However, the optimization of adsorption/desorption process in HER is rare. Herein, we concentrate on tuning the dynamics of the adsorption process in HER by applying a back gate voltage to the pristine VSe nanosheet. The back gate voltage induces the redistribution of the ions at the electrolyte-VSe nanosheet interface, which realizes the enhanced electron transport process and facilitates the rate-limiting step (discharge process) under HER conditions. A considerable low onset overpotential of 70 mV is achieved in VSe nanosheets without any chemical treatment. Such unexpected improvement is attributed to the field tuned adsorption-dynamics of VSe nanosheet, which is demonstrated by the greatly optimized charge transfer resistance (from 1.03 to 0.15 MΩ) and time constant of the adsorption process (from 2.5 × 10 to 5.0 × 10 s). Our results demonstrate enhanced catalysis performance in the VSe nanosheet by tuning the adsorption dynamics with a back gate, which provides new directions for improving the catalytic activity of non-noble materials.
Achieving adhesion between hydrogels and diverse materials in a facile and universal way is challenging. Existing methods rely on special chemical or physical properties of the hydrogel and adherends, which lead to limited applicability and complicated pretreatments. A stitch‐bonding strategy is proposed here by introducing a polymer chain with versatile functional group and triggerable crosslinking property inspired by catechol chemistry. The polymer chain can stitch the hydrogel by forming a network in topological entanglement with the preexisting hydrogel network, and directly bond to the adherend surface by versatile chemical interactions. Through this, the polymer chain solution works as a universal glue for facile adhesion of hydrogels to diverse substrates like metals, glasses, elastomers, plastics, and living tissues, without requiring any chemical design or pretreatment for the hydrogel and adherends. The adhesion energy between polyacrylamide hydrogel and diverse substrates can reach 200–400 J m−2, and it can reach ≈900 J m−2 with a toughened polyacrylic acid polyacrylamide hydrogel. The mechanism of stitch‐bonding strategy is illustrated by studying various influence factors.
Transition metal nitrides (TMNs) are considered as potential electrode materials for high‐performance energy storage devices. However, the structural instability during the electrochemical reaction process severely hinders their wide application. A general strategy to overcome this obstacle is to fabricate nanocomposite TMNs on the conducting substrate. Herein, the honeycomb‐like CoN‐Ni3N/N‐C nanosheets are in situ grown on a flexible carbon cloth (CC) via a mild solvothermal method with post‐nitrogenizing treatment. As an integrated electrode for the supercapacitor, the optimized CoN‐Ni3N/N‐C/CC achieves remarkable electrochemical performance due to the enhanced intrinsic conductivity and increased concentration of the active sites. In particular, the flexible quasi‐solid‐state asymmetric supercapacitor assembled with CoN‐Ni3N/N‐C/CC cathode and VN/CC anode delivers an excellent energy density of 106 μWh cm−2, maximum power density of 40 mW cm−2, along with an outstanding cycle stability. This study provides a neoteric perspective on construction of high‐performance flexible energy storage devices with novel metallic nitrides.
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