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Herein, we report a preparation method based on a modified zeolite imidazolate skeleton loose nanofiltration membrane used to achieve the efficient separation of salt and dye from dye wastewater. In this study, negatively charged hydrophilic TA@ZIF-8 was synthesized using a tannin surface-functionalization-assisted etching process by tannic acid (TA). A novel, highly negatively charged, and loose nanofiltration membrane was then prepared by adding TA@ZIF-8 to a polyethyleneimine (PEI) and tricarboxylic acid (TMA) crosslinking system. The addition of TA@ZIF-8 nanoparticles not only changed the degree of crosslinking of PEI/TMA but also provided a preferential channel for water molecules, thereby improving the flux of the membrane. Then, the success of TA@ZIF-8 NPs and membranes preparation were verified by SEM, XRD, FT-IR, TGA, BET, XPS and other methods. The results showed that the optimal preparation conditions were 0.020 wt% TA@ZIF-8 loading, and the contact angle of the membranes decreased from 79.8° to 53.7°. The water flux of the membrane is 81 L·m−2·h−1·bar−1, which is 1.5 times that of PEI/TMA membrane. In addition, TA@ZIF-8–2 membrane not only has high rejection rate for Methyl blue (98.7 %), Toluidine blue (89.6 %), Cr6+ (91.2 %), but also maintains high transmittance for NaCl and Na 2 SO 4. Besides, the separation performance of the membrane was stable for a long time in a mixed solution of dye and salt and excellent antifouling performance. Therefore, the loose TA@ZIF-8/PEI/TMA nanofiltration membrane has broad application prospects in printing and dyeing wastewater treatments. • Core-shell TA@ZIF-8 were synthesis by surface functionalization and etching of tannic acid. • TA@ZIF-8 NPs were successfully incorporated into PEI/TMA crosslinking system to prepare loose composite films. • The core-shell structure of TA@ZIF-8 improved the permeation flux of the membrane. • The nanocomposite membrane exhibited efficient separation of dye and inorganic salt molecules. [ABSTRACT FROM AUTHOR]

Strategies to control biofouling without using antimicrobial chemicals are needed to prevent the spread of antibiotic resistance genes and disruption of microbial activities in biological treatment. This study developed an environmentally friendly biofouling resistant membrane by incorporating d -tyrosine onto a commercial nanofiltration membrane using FAU type zeolite nanoparticles that covalently bound to the membrane surface as carriers for slow release. The d -tyrosine loaded membrane had similar water permeability as the unmodified membrane, but greatly reduced initial cell attachment and strongly inhibited subsequent biofilm formation without inactivating the bacteria. The membrane slowly released d -tyrosine in the time course of over 5 days, and retained its anti-biofouling capability in repeated 24 h efficacy tests for as long as 6 days. In nanofiltration operation, the d -tyrosine incorporated zeolite coating completely inhibited cell adhesion on the membrane surface and significantly alleviated membrane flux decline. [ABSTRACT FROM AUTHOR]

Control of microbial growth at the membrane surface on a continuous basis is needed to mitigate biofouling in long term membrane applications. In this study, a regenerable antimicrobial nanocomposite membrane was developed for sustainable membrane biofouling control via slow release of silver. Nanozeolites attached to the surface of a commercial polyamide nanofiltration membrane by covalent binding served as carriers for Ag + or Ag(0). At a silver loading of 7.18±0.62 mg/m 2 , the nanocomposite membranes effectively inhibited growth of P. aeruginosa for up to seven 24 h exposure cycles, and the antimicrobial efficacy correlated well with silver release kinetics data. The release of silver strongly depended on the Cl − concentration in the solution. At low Cl − concentrations, the low aqueous solubility of silver controlled silver release; the Ag + and Ag(0) loaded membranes showed similar release rate. At high Cl − concentration, silver release from the Ag(0) loaded membrane was notably more stable and longer lasting as Ag(0) oxidation became the rate-limiting step. After depletion, reloading silver via a simple physisoprtion protocol resumed the antimicrobial activity. The regenerated membranes had similar silver loading and antimicrobial activity as the freshly prepared membranes, demonstrating the potential of this approach in long-term control of membrane biofouling. [ABSTRACT FROM AUTHOR]

In this work, the NH 2 -MIL-125/F127/PES membranes with superior ultrafiltration, anti-fouling and self-cleaning were constructed via the RTIPS method. Characterizations showed the successful fabrication of NH 2 -MIL-125 with good photocatalysis activities, together with its stable anchoring in PES membrane. The loss of NH 2 -MIL-125 was suppressed by hydrogen bond between F127 and NH 2 -MIL-125, where hydrophilic F127 significantly boosted permeability and anti-fouling property. The permeability-selectivity trade-off effect was alleviated by NH 2 -MIL-125 with a regular size window. It was observed that the membranes prepared by the RTIPS method occurred a spongy-like cross-section and a porous surface, which imparted the membrane with improved ultrafiltration and mechanical properties over the finger-like cross-section and dense skin caused by the NIPS method. The optimal membranes exhibited decent permeability (1511 L/m2 h), BSA rejection rate (85.3%) and high COD removal rate over the commercial membranes, whose flux recovery rate was up to 76.91% after two cycles under light irradiation by the synergistic effect of F127 and NH 2 -MIL-125. The membrane fouled with RhB exhibited excellent regeneration properties benefiting from the holes (h+) and superoxide free radicals (• O 2 −), as well as reactive •OH. Overall, the membrane with F127 and NH 2 -MIL-125 presented a highly promising in improving ultrafiltration and self-cleaning via the RTIPS method. [Display omitted] • RTIPS as a novel method was used to fabricate photocatalytic membranes. • The properties of the membranes through RTIPS method were better than that by the NIPS method. • Synergistic effect of NH 2 -MIL-125 and F127 imparted the membranes with outstanding performance. • The modified membranes presented a highly promising in mitigating membrane fouling. [ABSTRACT FROM AUTHOR]

Abstract: Despite extensive studies on the relationship between membrane surface properties and fouling, the roles of specific surface functional groups in RO membrane fouling under different solution conditions are still poorly understood. This research aims to elucidate how specific surface functional groups affect RO membrane fouling by alginate in solution conditions relevant to seawater desalination. Common RO membrane surface functionalities are represented by self-assembled mono-layers with –COOH, –NH2, –CONH2 and –OH ending groups. Alginate adsorption and removal was studied under relevant solution conditions using quartz crystal microbalance with dissipation monitoring (QCM-D). The results show that ionic composition has great impacts on alginate–alginate interaction, which is more important than membrane surface chemistry in determining alginate adsorption equilibrium at the high ionic strength of seawater. The initial adsorption rate is highly sensitive to surface chemistry. The –NH2 group has strong affinity for alginate, yet the –COOH group exhibits the highest adsorption rate in simulated seawater. The reversibility of the adsorbed layer was influenced by both alginate–surface interaction and the structure of the layer. The large alginate aggregates formed in the presence of Ca2+ result in loosely packed and less viscous adsorbed layer on the surface, which is easier to remove by surfactant cleaning. [Copyright &y& Elsevier]

Recent mapping-based motion deblurring methods lack the regularization of prior knowledge, resulting in an over-reliance on the training data and limited generalization ability. As deblurring aims to improve image quality, we quantitatively analyze and further discover the strong correlation between image quality and sharpness. Motivated by the above facts and notable accomplishments of recent no-reference image quality assessment (NR-IQA), we present a novel framework that incorporates quality knowledge into mapping-based deblurring models. Specifically, we extract quality-aware features from NR-IQA models as prior knowledge, and subsequently propose a prediction-based strategy and an encoder-reuse strategy to integrate knowledge into the encoder and decoder, respectively. After training, the model can simultaneously deblur images and predict quality features, indicating that it has grasped the knowledge and validating the effectiveness of the proposed embedding strategies. Extensive experimental results show that embedding quality knowledge consistently improves model performance and the model achieves state-of-the-art intra/cross-dataset results. Code and pre-trained models are available at https://github.com/esnthere/QAMD. • Integrating no-reference image quality assessment into image deblurring. • Prediction-based strategy for encoder knowledge embedding. • Encoder-reuse strategy for decoder knowledge embedding. • Simultaneously deblurring images and predicting quality features. • SOTA intra/cross-dataset test performance. [ABSTRACT FROM AUTHOR]

No-reference/blind image quality assessment (BIQA) is designed to measure the image quality without any knowledge of the reference image. Most existing BIQA metrics employ natural scene statistics or learning based models, which have achieved great progress but there still remains much room for improvement. Cognitive researches indicate that natural images possess sparse structures which can be represented by a small number of descriptors. Considering the sparse property of images, we utilize the bag-of-words (BoW) model for image representation and propose a novel BIQA metric. By analyzing the effect of neighboring pixel number and quantization depth of local pattern on image content extraction, we adopt the local quantized pattern (LQP) to extract image feature descriptors. The codebook is constructed by clustering LQP based descriptors from a set of natural images instead of distorted images, which presents strong generalization ability. The BoW-based image feature representation is highly sensitive to various distortion types and levels. Experiments on three public databases verify the effectiveness of the proposed metric and indicate our method is highly consistent with human perception. [ABSTRACT FROM AUTHOR]

Noise modeling is an important research field in computer vision; the design of an accurate model for imaging sensor noise depends on not only a comprehensive benchmark dataset of the real world, but also a precise design of the noise modeling algorithm. However, due to the inaccurate estimation method of noise-free images and limited shooting scenes, the current realistic datasets could not describe the diverse noise properties sufficiently. Moreover, popular parametric noise models are not sophisticated enough to characterize the real-world noise exactly. In this work, we first construct a more comprehensive dataset of the real world by capturing more indoor and outdoor scenes under different lighting conditions using diverse smartphones, then we propose a non-parametric noise estimation method capable of modeling the spatial heterogeneity of real-world noise patterns. Specifically, in order to characterize the spatial heterogeneity of real-world noise, we assume a non-i.i.d Gaussian distribution and propose a deep convolutional neural network (DCNN)-based approach for learning pixel-wise noise variance maps. To learn the pixel-wise variance map, we have constructed a variance estimation network mapping from the conditional signals (clean image, ISO, and camera model) to surrogate labels obtained from the nonlocal search of similar patches from the clean-noisy image pair. Finally, we conducted denoising and classification experiments using different kinds of simulated noisy images, compared to the Poisson-Gaussian and Noise Flow noise models, the proposed method achieves denoising performance improvements (PSNR) of 1.13 dB and 2.51 dB respectively on the proposed real-world test dataset, denoising and classification results on the real noisy data captured by mobile phones have verified that our approach is more accurate than current noise modeling methods. • Current realistic datasets could not describe diverse noise properties sufficiently. • Parametric noise models cannot characterize the real-world noise exactly. • Constructing a more comprehensive real-world benchmark dataset. • Developing a pixel-wise variance estimation network. • Improved real-world denoising and classification performance. [ABSTRACT FROM AUTHOR]

Image aesthetics assessment (IAA) has attracted increasing attention recently but is still challenging due to its high abstraction and complexity. Intuitively, image emotion and aesthetics are both human subjective feelings evoked by visual content. Previous researches have demonstrated that image aesthetics has intrinsic relationships with emotion. In fact, human emotional experience has potential impact on the perception of image aesthetics. Therefore, emotion information can be exploited to enhance aesthetic representation learning. Inspired by this, this paper presents an Emotion-Aware Hierarchical Interaction NETwork (EAHI-NET) for multimodal image aesthetics assessment, which explores both intra-modal and inter-modal interactions between aesthetics and emotions hierarchically. Specifically, we first propose the intra-modal emotion-aesthetics interaction module to obtain emotion-enhanced visual and textual aesthetic representations respectively. Then we propose the inter-modal feature enhancement to obtain the cross-modal aesthetic and emotion features. Finally, we design the inter-modal emotion-aesthetics interaction module to further investigate the cross-modal interplay between aesthetics and emotion, based on which hierarchical feature representations are achieved for multimodal IAA. The extensive experiments show that the proposed method can outperform the state-of-the-arts on multimodal AVA and Photo.net datasets. • Human emotional experience potentially affects image aesthetics perception. • Exploiting emotion information to enhance aesthetic learning. • User comments provide aesthetic and emotional semantic information. • Interactions between aesthetics and emotion from intra- and inter-modalities. [ABSTRACT FROM AUTHOR]

JPEG is the most commonly used image compression standard. In practice, JPEG images are easily subject to blocking artifacts at low bit rates. To reduce the blocking artifacts, many deblocking algorithms have been proposed. However, they also introduce certain degree of blur, so the deblocked images contain multiple distortions. Unfortunately, the current quality metrics are not designed for multiply distorted images, so they are limited in evaluating the quality of deblocked images. To solve the problem, this paper presents a no-reference (NR) quality metric for deblocked images. A DeBlocked Image Database (DBID) is first built with subjective Mean Opinion Score (MOS) as ground truth. Then a NR DeBlocked Image Quality (DBIQ) metric is proposed by simultaneously evaluating blocking artifacts in smooth regions and blur in textured regions. Experimental results conducted on the DBID database demonstrate that the proposed metric is effective in evaluating the quality of deblocked images, and it significantly outperforms the existing metrics. As an application, the proposed metric is further used for automatic parameter selection in image deblocking algorithms. [ABSTRACT FROM AUTHOR]

Ooids are common carbonate particles that are traditionally considered as abiogenically formed by physical and chemical processes in highly agitated environments. Recent studies point to the importance of microbial activities in ooid formation, but more case studies are required to confirm and clarify the roles of microbes and their organomineralization processes. Here we report an integrated petrographic, element geochemical, and isotopic study of Mesoproterozoic giant ooids from the Wumishan Formation ( ca. 1.50–1.45 Ga) of North China. The Wumishan giant ooids (2.0–14.4 mm) are composed of small micritic nuclei and thick radial fibrous cortices. Abundant organic relics, including putative bacterial filaments and mucus-like extracellular polymeric substances (EPS), are present in ooid cortices. Organominerals (e.g., nanoparticles and polyhedrons) are concentrated and lined with the axes of radial fibers, suggesting in situ mineralization of bacterial filaments. The abundance of organic relics and fiber-aligned organominerals confirms the constructive roles of microbes in the formation of the Wumishan giant ooids. The preservation of bacterial filaments/filament bundles in their growth orientation requires fast mineralization in CaCO 3 -supersaturated environments, which may have been controlled by the shallow chemocline in redox-stratified Mesoproterozoic basins. [ABSTRACT FROM AUTHOR]