Hollow metal–organic frameworks (MOFs) are promising materials with sophisticated structures, such as multiple shells, that cannot only enhance the properties of MOFs but also endow them with new ...functions. Herein, we show a rational strategy to fabricate multi‐shelled hollow chromium (III) terephthalate MOFs (MIL‐101) with single‐crystalline shells through step‐by‐step crystal growth and subsequent etching processes. This strategy relies on the creation of inhomogeneous MOF crystals in which the outer layer is chemically more robust than the inner layer and can be selectively etched by acetic acid. The regulation of MOF nucleation and crystallization allows the tailoring of the cavity size and shell thickness of each layer. The resultant multi‐shelled hollow MIL‐101 crystals show significantly enhanced catalytic activity during styrene oxidation. The insight gained from this systematic study will aid in the rational design and synthesis of other multi‐shelled hollow structures and the further expansion of their applications.
Promising MOF structures: Single‐crystalline multi‐shelled hollow metal–organic frameworks (MSHMs) were synthesized through step‐by‐step crystal growth and subsequent etching processes. The cavity size and shell thickness of each layer in the MSHMs was regulated through careful nucleation and crystallization of the metal–organic frameworks. The MSHM crystals show significantly increased catalytic activity.
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BFBNIB, FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SAZU, SBCE, SBMB, UL, UM, UPUK
Low-light image enhancement (LLIE) aims to improve the visual quality of images taken under complex low-light conditions. Recent works focus on carefully designing Retinex-based methods or end-to-end ...networks based on deep learning for LLIE. However, these works usually utilize pixel-level error functions to optimize models and have difficulty effectively modeling the real visual errors between the enhanced images and the normally exposed images. In this paper, we propose an adaptive dual aggregation network with normalizing flows (ADANF) for LLIE. First, an adaptive dual aggregation encoder is built to fully explore the global properties and local details of the low-light images for extracting illumination-robust features. Next, a reversible normalizing flow decoder is utilized to model real visual errors between enhanced and normally exposed images by mapping images into underlying data distributions. Finally, to further improve the quality of the enhanced images, a gated multi-scale information transmitting module is leveraged to introduce the multi-scale information from the adaptive dual aggregation encoder into the normalizing flow decoder. Extensive experiments on paired and unpaired datasets have verified the effectiveness of the proposed ADANF.
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IZUM, KILJ, NUK, PILJ, PNG, SAZU, UL, UM, UPUK
The enhancement of images captured under low-light conditions plays a vitally important role in the area of image processing and can significantly affect the performance of following operations. In ...recent years, deep learning techniques have been leveraged in the area of low-light image enhancement tasks, and deep-learning-based low-light image enhancement methods have been the mainstream for low-light image enhancement tasks. However, due to the inability of existing methods to effectively maintain the color distribution of the original input image and to effectively handle feature descriptions at different scales, the final enhanced image exhibits color distortion and local blurring phenomena. So, in this paper, a novel dual color-and-texture-enhancement-based low-light image enhancement method is proposed, which can effectively enhance low-light images. Firstly, a novel color enhancement block is leveraged to help maintain color distribution during the enhancement process, which can further eliminate the color distortion effect; after that, an attention-based multiscale texture enhancement block is proposed to help the network focus on multiscale local regions and extract more reliable texture representations automatically, and a fusion strategy is leveraged to fuse the multiscale feature representations automatically and finally generate the enhanced reflection component. The experimental results on public datasets and real-world low-light images established the effectiveness of the proposed method on low-light image enhancement tasks.
Due to the limitations of devices, images taken in low-light environments are of low contrast and high noise without any manual intervention. Such images will affect the visual experience and hinder ...further visual processing tasks, such as target detection and target tracking. To alleviate this issue, we propose a multi-scale joint low-light enhancement network based on the Retinex theory. The network consists of a decomposition part and an enhancement part. As a joint network, the decomposition and enhancement parts are mutually constrained, and the parameters are updated at the same time so that the image processing results are more excellent in detail. Our algorithm avoids the separation and recombination of decomposition and enhancement. Therefore, less information is lost in the processing of low-light images, and the enhancement result of the proposed algorithm is very close to the ground truth. In addition, in the enhancement part, we adopt a multi-scale network to fully extract image features. The multi-scale network maintains a balance between the global and local luminance of the illumination image. Retinex theory can effectively solve the problem of noise amplification and color distortion. At the same time, we have added color loss to solve the problem of color distortion, so that the enhancement result is closer to the normal-light image in color. The enhancement results are intuitively excellent, and the peak signal-to-noise ratio and structural similarity index results also reflect the reliability of the algorithm.
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EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, KILJ, KISLJ, MFDPS, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
The purpose of dark image enhancement is to restore dark images to visual images under normal lighting conditions. Due to the ill-posedness of the enhancement process, previous enhancement algorithms ...often have overexposure, underexposure, noise increases and artifacts when dealing with complex and changeable images, and the robustness is poor. This article proposes a new enhancement approach consisting in constructing a dim light enhancement network with more robustness and rich detail features through the collaborative constraint of multiple self-coding priors (CCMP). Specifically, our model consists of two prior modules and an enhancement module. The former learns the feature distribution of the dark light image under normal exposure as an a priori term of the enhancement process through multiple specific autoencoders, implicitly measures the enhancement quality and drives the network to approach the truth value. The latter fits the curve mapping of the enhancement process as a fidelity term to restore global illumination and local details. Through experiments, we concluded that the new method proposed in this article can achieve more excellent quantitative and qualitative results, improve detail contrast, reduce artifacts and noise, and is suitable for dark light enhancement in multiple scenes.
Plastic waste management becomes imperative to prevent detrimental plastic-related environmental impacts. These relate to the huge plastic waste amount in both generation and disposal, ...non-biodegradability of most plastic waste, persistent nature of plastic waste in the environment, and the increasing concern of micro- and nanoplastics on human health. To continue exporting the use and benefits of plastic materials while mitigating their impacts to the planet, reuse, recovery and recycling of plastic waste are strongly encouraged. This would promote optimization of the value chain of plastic-based materials and validate the concept of a circular polymer economy. Chemical recycling techniques have been extensively studied for energy and material recovery from plastic waste, which are assisted by catalysts for enhanced process efficiency. In this review, the research progress of the applied catalysts in various thermochemical techniques is comprehensively summarized and assessed, covering the non-catalytic and catalytic pyrolysis, reforming of pyrolysis volatiles, and gasification processes. The impacts of feedstock, process conditions and catalyst properties on the quality and yield of the products are carefully examined. Finally, the practical challenges and perspectives on catalyst design and process improvement for material circularity are suggested and the emerging novel processes for the degradation of plastic waste are highlighted.
•Applied catalysts for thermochemical recycling of plastic waste are analyzed.•Impacts of feedstock, process conditions and catalyst properties are discussed.•Practical challenges and perspectives on the catalyst design are suggested.•Emerging green processes for the degradation of plastic waste are highlighted.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
In traditional steam reforming of CH4, the CH4 conversion and its selectivity to CO and H2 are thermodynamically limited. In this work, we designed a series of Ni–Fe redox catalysts with varying ...Ni/Fe ratios. The Ni–Fe redox catalysts could function as oxygen carriers to selectively convert CH4 to syngas via chemical looping. The selectivity to CO was dramatically enhanced via a selective conversion route of CH4 to C and H2 in the reduction, followed by C gasification to syngas with hot steam. Taking the advantages of the highly reactive Ni species for CH4 activation and Fe species for water splitting, together with the resulting NiFe alloy in the reduced catalyst for catalytic CH4 decomposition, high CH4 conversion up to 97.5% and CO selectivity up to 92.9% were achieved at 900 °C with productivity of CO and H2 of 9.6 and 29.0 mol kgcatalyst –1, respectively, on equimolar Ni–Fe catalyst.
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IJS, KILJ, NUK, PNG, UL, UM
•Ni-Mg-Al-O oxygen carriers with equilibrium phase composition were synthesised by from layered double hydroxides precursors.•The synthesised oxygen carriers showed excellent performance over 100 ...redox cycles.•The phase compositions of the oxygen carriers underwent significant changes over the redox cycles.•The phase segregation of the NiO-MgO solid solution lead to improved reactivity of the Ni2+ during reduction.
Chemical looping combustion offers a solution to achieve carbon capture with minimal energy penalty and significantly reduced NOx formation. The design and synthesis of oxygen carriers play an important role in the advancement of chemical looping combustion. In this investigation, Mg-Ni-Al-O oxygen carriers were synthesised using layered double hydroxides (LDH) as precursors, which facilitate molecular level dispersion of cations in the resulting oxide composites. The behaviour of the synthesised oxygen carriers over 100 chemical looping cycles was studied. The results confirmed the presence of two solid solutions systems: MgO-NiO and MgAl2O4-NiAl2O4, the compositions of which changed over cycles and determined the performance of the oxygen carriers.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK, ZRSKP
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•Ni catalysts are investigated for steam reforming of LDPE pyrolysis volatiles.•The metal-support interaction is studied over Ni catalysts with seven supports.•Distinct effects of ...support are observed on syngas yield and composition.•Weak metal-support interaction favors coke formation on Ni/CeO2 and Ni/Y2O3.•Al2O3 and MgO are found potential supports of Ni catalysts for this process.
Syngas is a valuable intermediate for sustainable management of waste plastics, which can be converted to new monomers and chemicals. In this work, Ni catalysts were studied for syngas recovery via steam reforming of low-density polyethylene (LDPE) pyrolysis volatiles on various supports, such as Al2O3, MgO, CeO2, Y2O3, TiO2, SiO2 and ZSM-5. Two commercial catalysts (NiAl and NiCaAl) were used for comparison. The supports exhibited distinct effects on the catalytic activity, selectivity and coke formation. Ni/CeO2, Ni/Y2O3, Ni/Al2O3, NiAl and NiCaAl showed high syngas yield but these catalysts were prone to coke formation. Ni/MgO, Ni/TiO2, Ni/SiO2 and Ni/ZSM-5 were the least active. The obtained H2/CO ratios were correlated with CO selectivity, revealing the unique impacts of supports on syngas composition via water-gas shift reaction. Compared with commercial NiAl, our work demonstrated that the prepared Ni catalysts on Al2O3 and MgO were suitable candidates for steam reforming of plastic pyrolysis volatiles.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
