Surfactant-templated 5 mol% Al2O3-doped silica membranes nanofiltration membranes were synthesized via the sol-gel method, and afterward, were optimized, and tested with respect to the permeability and rejection rate. The disordered silica network was stabilized by doping 5 mol% alumina. Tetraethyl orthosilicate and aluminum isopropoxide were used as the silica and alumina precursors, respectively. Cetyltrimethylammonium bromide (CTAB) was used not only as a pore-forming agent, but also to control the reaction rate of the aluminum isopropoxide, thus obtaining highly homogeneous materials. The results about filtration of model solutions showed that the optimized membranes are featured by both a relatively high water permeability (1.1–2.3 L·m−2·h−1 ·bar−1) and a high rejection for salts (74% for NaCl, and >95% for MgSO4 and Na2SO4) and organic pollutants (e.g., about 98% for caffeine). High rejection of divalent ions and organic molecules was also observed when a real wastewater effluent was filtered. The influence of the synthesis conditions on the membrane performance is discussed.

Some of the groundwater aquifers in the Puglia Region, Italy, suffer from high salinity and potential micropollutant contamination due to seawater infiltration and chemical discharge. The objective of this study is twofold: to evaluate the performance of the recently reported alumina-doped silica nanofiltration membranes for water potabilization, and to provide a possible solution to improve the groundwater quality in the Puglia Region while maintaining a low energy-footprint. Two lab-made alumina-doped silica membranes with different pore structures, namely S/O = 0.5 and S/O = 2, were tested with real groundwater samples and their performances were compared with those of a commercial polymeric membrane (Dow NF90). Moreover, groundwater samples were sparked with acetamiprid, imidacloprid, and thiacloprid to test the membrane performance in the presence of potential contamination by pesticides. At a trans-membrane pressure of 5 bar, NF90 could reduce the groundwater conductivity from 4.6 to around 1.3 mS·cm−1 and reject 56–85% of the model pesticides, with a permeate flux of 14.2 L·m−2·h−1. The two inorganic membranes S/O = 2 and S/O = 0.5 reduced the permeate conductivity to 3.8 and 2.4 mS·cm−1, respectively. The specific energy consumption for all three membranes was below 0.2 kWh·m−3 which indicates that the potabilization of this groundwater by nanofiltration is commercially feasible.

No abstract

Due to progressive limitation of access to clean drinkable water, it is nowadays a priority to find an effective method of water purification from those emerging organic contaminants, which might have potentially harmful and irreversible effects on living organisms and environment. This manuscript reports the development of a new strategy for water purification, which combines a novel and recently developed Al2O3-doped silica nanofiltration membrane with a thermocatalytic perovskite, namely cerium-doped strontium ferrate (CSF). The thermocatalytic activity of CSF offers the opportunity to degrade organic pollutants with no light and without input of chemical oxidants, providing simplicity of operation. Moreover, our studies on real samples of secondary effluent from wastewater treatment showed that the thermocatalyst has the ability to degrade also part of the non-toxic organic matter, which allows for reducing the chemical oxygen demand of the retentate and mitigating membrane fouling during filtration. Therefore, the new technology is effective in the production of clean feed and permeate and has a potential to be used in degradation of micropollutants in water treatment.

Contrastive Language-Image Pre-training (CLIP) has been shown to learn visual representations with promising zero-shot performance. To further improve its downstream accuracy, existing works propose additional learnable modules upon CLIP and fine-tune them by few-shot training sets. However, the resulting extra training cost and data requirement severely hinder the efficiency for model deployment and knowledge transfer. In this paper, we introduce a free-lunch enhancement method, CALIP, to boost CLIP's zero-shot performance via a parameter-free attention module. Specifically, we guide visual and textual representations to interact with each other and explore cross-modal informative features via attention. As the pre-training has largely reduced the embedding distances between two modalities, we discard all learnable parameters in the attention and bidirectionally update the multi-modal features, enabling the whole process to be parameter-free and training-free. In this way, the images are blended with textual-aware signals and the text representations become visual-guided for better adaptive zero-shot alignment. We evaluate CALIP on various benchmarks of 14 datasets for both 2D image and 3D point cloud few-shot classification, showing consistent zero-shot performance improvement over CLIP. Based on that, we further insert a small number of linear layers in CALIP's attention module and verify our robustness under the few-shot settings, which also achieves leading performance compared to existing methods. Those extensive experiments demonstrate the superiority of our approach for efficient enhancement of CLIP. Code is available at https://github.com/ZiyuGuo99/CALIP.