Semantic Segmentation of Satellite Images for Landslide Detection Using Foreground-Aware and Multi-Scale Convolutional Attention Mechanism.

Advancements in satellite and aerial imagery technology have made it easier to obtain high-resolution remote sensing images, leading to widespread research and applications in various fields. Remote sensing image semantic segmentation is a crucial task that provides semantic and localization information for target objects. In addition to the large-scale variation issues common in most semantic segmentation datasets, aerial images present unique challenges, including high background complexity and imbalanced foreground-background ratios. However, general semantic segmentation methods primarily address scale variations in natural scenes and often neglect the specific challenges in remote sensing images, such as inadequate foreground modeling. In this paper, we present a foreground-aware remote sensing semantic segmentation model. The model introduces a multi-scale convolutional attention mechanism and utilizes a feature pyramid network architecture to extract multi-scale features, addressing the multi-scale problem. Additionally, we introduce a Foreground-Scene Relation Module to mitigate false alarms. The model enhances the foreground features by modeling the relationship between the foreground and the scene. In the loss function, a Soft Focal Loss is employed to focus on foreground samples during training, alleviating the foreground-background imbalance issue. Experimental results indicate that our proposed method outperforms current state-of-the-art general semantic segmentation methods and transformer-based methods on the LS dataset benchmark.

The Empty-Nest Power User Management Based on Data Mining Technology.

With the aging of the social population structure, the number of empty-nesters is also increasing. Therefore, it is necessary to manage empty-nesters with data mining technology. This paper proposed an empty-nest power user identification and power consumption management method based on data mining. Firstly, an empty-nest user identification algorithm based on weighted random forest was proposed. Compared with similar algorithms, the results indicate that the performance of the algorithm is the best, and the identification accuracy of empty-nest users is 74.2%. Then a method for analyzing the electricity consumption behavior of empty-nest users based on fusion clustering index adaptive cosine K-means was proposed, which can adaptively select the optimal number of clusters. Compared with similar algorithms, the algorithm has the shortest running time, the smallest Sum of the Squared Error (SSE), and the largest mean distance between clusters (MDC), which are 3.4281 s, 31.6591 and 13.9513, respectively. Finally, an anomaly detection model with an Auto-regressive Integrated Moving Average (ARIMA) algorithm and an isolated forest algorithm was established. The case analysis shows that the recognition accuracy of abnormal electricity consumption for empty-nest users was 86%. The results indicate that the model can effectively detect the abnormal behavior of empty-nest power users and help the power department to better serve empty-nest users.

Clinical Efficacy of Infliximab in Patients With Crohn Disease in Different Locations of Disease Pathology: A Meta-Analysis.

PURPOSE: Infliximab (INX) has been approved for treating Crohn disease (CD) for many years, showing promis-ing efficacy in the clinic. However, the efficacy of the drug and the prognosis of CD vary significantly with dif-ferent locations of disease pathology. This study evaluated the efficacy of INX and prognosis in CD in different locations of disease pathology using systematic meta-analysis. METHODS: We used "Infliximab OR Remicade OR Avakine OR Inflectra OR Renflexis OR Remsima OR IgG1k monoclonal antibody" AND "Crohn's disease OR IBD OR inflammatory bowel disease" as search strategies for searching in PubMed, Wanfang and Embase. A systematic meta-analysis for overall proportions was used to analyze the data. RESULTS: Twelve studies involving 1,978 patients were included. The results confirmed that treatment with INX led to high clinical remission rates (82%, 95% CI: 64%-92%) and low relapse rates (4%, 95% CI: 2%-9%) in patients with CD. Our results also indicated that use of INX in patients with colon only (L2) CD led to lower clinical remission rates, and use of INX in patients with ileum and colon (L3) CD led to higher relapse rates. CONCLUSION: Our findings show different remission rates depending on location of the disease and may be useful for clinicians' choice of therapeutics.

Tensor improve equivariant graph neural network for molecular dynamics prediction.

Molecular dynamics(MD) simulations are essential for molecular structure optimization, drug-drug interactions, and other fields of drug discovery by simulating the motion of microscopic particles to calculate their macroscopic properties (e.g., energy). The main problems of the existing work are as follows: (1) Failure to fully consider the chemical bonding constraints between atoms, (2) Group equivariance can help achieve robust and accurate predictions of MD under arbitrary reference transformations and should be incorporated into the model design, (3) Tensor information such as relative position, velocity, and torsion angle can be used to enhance the prediction of molecular dynamics. And the existing methods are mainly limited to the scalar domain. In this paper, we propose a new model-tensor improve equivariant graph neural network for molecular dynamics prediction (TEGNN): (1) The model materialization of chemical bond constraints between atoms into geometric constraints. The molecule's forward kinematic information (position and velocity) is represented by generalized coordinates. In this way, the interatomic chemical bonding constraints are implicitly and naturally encoded in the forward kinematics, (2) The equivariant information transfer is allowed in TEGNN, which significantly improves the accuracy and computational efficiency of the final prediction, (3) TEGNN introduces equivariant locally complete frames into scalar-only equivariant graph neural networks, thus allowing the projection of tensor information of a given order onto the frame. On the N-body dataset of simulated molecular systems consisting of particles, sticks, and hinges, as well as on the real dataset MD17 for molecular dynamics prediction, multiple experiments support that TEGNN is ahead of the current state-of-the-art GNN in terms of prediction accuracy, constraint satisfaction, and data efficiency. We extend the current state-of-the-art equivariant neural network model. The proposed TEGNN accommodates more tensor information and considers the chemical bonding constraints between atoms during motion, ultimately improving the performance of predicting the kinematic states of molecules.

Unraveling the hydrogeochemical characteristics and pollution sources of groundwater in an intensive industrial area, East China.

It is challenging to interpret hydrogeochemical datasets with complex natural and anthropogenic genesis in intensive industrial areas. This paper elucidates the hydrogeochemical characteristics and pollution sources of groundwater in an industrial park, East China, combining the self-organizing map (SOM), hydrochemical graphs, and correlation analysis. The results show that the total dissolved solids of groundwater range from 73.45 to 997.92 mg/L and can be regarded as freshwater. The pH varies greatly from 6.44 to 9.90, most of samples belonging to weakly acidic-weakly alkaline. The groundwater can be classified into five clusters by SOM, representing the non- or least-polluted groundwater (cluster D), high salt groundwater (cluster A), high NH4+-N and HCO3- groundwater (cluster B), high Fe and Mn groundwater (cluster C), and high pH groundwater (cluster E), which were contaminated by industrial salts, historical agriculture activity, industrial reducing substances, and industrial alkali, respectively. The natural evolution of groundwater (cluster D) in the study area is mainly controlled by mineral weathering/dissolution. The contributions of calcite, dolomite, gypsum, halite, and silicate mineral to groundwater solute are 55.8-66.3%, 15.1-18.0%, 9.0-10.7%, 2.5-10.1%, and 2.3-9.4%, respectively, based on the mass conservation. The contaminated groundwaters (all other clusters except for cluster D) have different hydrochemical characteristics associated with the pollution sources. In addition, the relatively reductive environment in quaternary flu-lacustrine sediments favored the formation of high level of Fe, Mn, and NH4+-N in groundwater. This study provides a new insight into the characteristic contaminants and their distributions in groundwater and the associated pollution sources based on the large datasets in an intensive industrial area. The data evaluation methods and results of this study could be useful to the groundwater usage management and pollution control in this area and other industrial areas.

MMR: A Multi-view Merge Representation model for Chemical-Disease relation extraction.

Chemical-Disease relation (CDR) extraction aims to identify the semantic relations between chemical and disease entities in the unstructured biomedical document, which provides a basis for downstream tasks such as clinical medical diagnosis and drug discovery. Compared with general domain relation extraction, it needs a more effective representation of the whole document due to the specialized nature of texts in the biomedical domain, including the biomedical entity and entity-pair representation. In this paper, we propose a novel Multi-view Merge Representation (MMR) model to thoroughly capture entity and entity-pair representation of the document. First, we utilize prior knowledge and a pre-trained transformer encoder to capture entity semantic representation. Then we employ the U-Net layer and Graph Convolution Network layer to capture global entity-pair representation. Finally, we get a specific merged representation for each entity pair to be classified. We evaluate our model on the CDR dataset published by the BioCreative-V community and achieve a state-of-the-art result.

Myricetin ameliorates airway inflammation and remodeling in asthma by activating Sirt1 to regulate the JNK/Smad3 pathway.

BACKGROUND: Myricetin has various biological activities and health benefits; however, its effects on airway remodeling in asthma have not been reported. PURPOSE: We aimed to investigate the possibility that myricetin improves airway remodeling by activating Sirt1 and has potential as a new treatment for asthma. METHODS: RAW 264.7 cells were stimulated with lipopolysaccharide and co-cultured with 3T6 cells in vitro to simulate the in vivo effects of inflammation on airway remodeling. Using an ovalbumin-induced chronic asthma mouse model, we compared changes in inflammatory factors and airway remodeling-related factors under treatment with myricetin and/or the Sirt1 inhibitor EX-527 using western blotting and quantitative PCR. Expression plasmids carrying Smad3 site mutations were transfected into 3T6 cells to identify the Sirt1 deacetylation site on Smad3 protein. RESULTS: Myricetin significantly reduced the infiltration of airway inflammatory cells and the production of interleukin (IL)-6 and IL-5, and inhibited mucus secretion by goblet cells, collagen fiber proliferation, and the increase in inflammatory cells in bronchoalveolar lavage fluid from asthmatic mice. Results of in vitro experiments were consistent with those conducted in vivo. Exploring the mechanism of action of myricetin, we found that myricetin downregulated the levels of phosphorylated (p)-JNK, p-Smad3, and acetylated Smad3 proteins by activating Sirt1 both in vivo and in vitro. K341 was identified as the main deacetylation site of Smad3 by myricetin-activated Sirt1. CONCLUSION: Myricetin ameliorates airway inflammation and remodeling in asthma by activating Sirt1 to regulate the JNK/Smad3 pathway.

Wet-dry or freeze-thaw alternation can regulate the impacts of farmland plastic pollution on soil bacterial communities and functions.

The persistence of farmland plastic pollution has raised significant concerns regarding its potential long-term impacts on soil health in the context of global climate change. However, there are still gaps in the understanding of the impacts of plastic residues on soil microbial communities and functions in agricultural environments under unstable and extreme climatic conditions. In this study, the effects of plastic residues (two types and three shapes) on farmland soil bacterial communities and functions across varying environmental conditions were investigated through microscopic experiments. The results revealed that plastic residues subjected to wet-dry or freeze-thaw alternations exhibited greater degradation compared to those under natural conditions. The effects of plastic residue types and shapes on soil bacterial diversity and function were regulated by environmental factors. The plastic residues significantly reduced the stability of the bacterial network under natural condition (P < 0.05), whereas the opposite phenomenon was observed under wet-dry or freeze-thaw alternating conditions. Compared to under natural condition, lower numbers of bacterial functional pathways exhibiting significant differences due to plastic residues were observed under wet-dry or freeze-thaw alternating conditions. Significant associations were observed between soil bacterial communities and functions and various soil physicochemical properties under natural conditions (P < 0.05), and most of these associations were attenuated in the wet-dry or freeze-thaw alternations. This study demonstrated the potential impacts of plastic pollution on farmland soil microbiomes, which could be modulated by both residue characteristics and climatic conditions. Specifically, extreme environments could mitigate plastic-pollution-driven influences on soil microbiomes.

Thin-Film Composite Membrane with Porous Interlayer Composed of Dendritic Mesoporous Silica Nanoparticles for Enhanced Nanofiltration.

Positively charged nanofiltration (NF) membranes show great potential in the fields of water treatment and resource recovery. However, this kind of NF membrane usually suffers from relatively low water permeance. Herein, a positively charged NF membrane with a porous interlayer is developed, where the interlayer is formed by assembling dendritic mesoporous silica nanoparticles (DMSNs) after the formation of a polyamide layer. This post-assembly strategy avoids the adverse effect of the interlayer on the formation of positively charged NF membranes. The porous DMSN interlayer provides abundant connected channels for water transport, thus endowing the NF membrane with enhanced water permeance. A series of DMSNs with different sizes was synthesized, and their influence on membrane formation and membrane performance was systematically investigated. The optimized membrane exhibits a CaCl2 rejection rate of 95.2% and a water flux of 133.6 L·h-1·m-2, which is 1.6 times that of the control group without an interlayer. This work represents an approach to the fabrication of a positively charged NF membrane with porous interlayers for high-efficiency cation rejection.

High-Performance Polyamide Reverse Osmosis Membrane Containing Flexible Aliphatic Ring for Water Purification.

A reverse osmosis (RO) membrane with a high water permeance and salt rejection is needed to reduce the energy requirement for desalination and water treatment. However, improving water permeance while maintaining a high rejection of the polyamide RO membrane remains a great challenge. Herein, we report a rigid-flexible coupling strategy to prepare a high-performance RO membrane through introducing monoamine with a flexible aliphatic ring (i.e., piperidine (PPR)) into the interfacial polymerization (IP) system of trimesoyl chloride (TMC) and m-phenylenediamine (MPD). The resulted polyamide film consists of a robust aromatic skeleton and soft aliphatic-ring side chain, where the aliphatic ring optimizes the microstructure of polyamide network at a molecular level. The obtained membranes thereby showed an enhanced water permeance of up to 2.96 L·m-2 h-1 bar-1, nearly a 3-fold enhancement compared to the control group, meanwhile exhibiting an ultrahigh rejection toward NaCl (99.4%), thus successfully overcoming the permeability-selectivity trade-off limit. Furthermore, the mechanism of the enhanced performance was investigated by molecular simulation. Our work provides a simple way to fabricate advanced RO membranes with outstanding performance.

Regulating cell behavior via regional patterned distribution of heparin-like polymers.

Molecular patterning on biomaterial surfaces is an effective strategy to regulate biomaterial properties. Among the specific molecules, due to their biological functions, such as regulating cell behavior, heparin-like polymers (HLPs) have attracted much attention. In this study, HLP-distributed regional patterned surfaces (300 µm diameter circular array) were prepared by the combination of visible light-induced graft polymerization, transfer imprinting, and self-assembly to regulate the behavior of human umbilical vein endothelial cells (HUVECs) and human umbilical vein smooth muscle cells (HUVSMCs). The introduction of the regional pattern on HLP-modified surfaces enhanced the promotion effect of sulfonate-containing polymer (pSS) and sulfonate-, and glyco-containing copolymer (pS-co-pM), and slightly weakened the inhibition effect of glyco-containing polymer (pMAG) on the growth of HUVECs and HUVSMCs. Compared with flat surfaces, it was found that the unmodified regional patterned surfaces inhibit the spreading of HUVECs and HUVSMCs, while significantly promoting the spreading of HUVECs and HUVSMCs on all the HLP-distributed regional patterned surfaces. The patterned surface modified with pS-co-pM had the highest average spread area of HUVECs (∼10,554 µm2), which was 193 % higher than that of the unmodified flat surface. This trend was somewhat related to surface VEGF adsorption. The combination of regional divisive patterns and different HLP distributions enriched the potential of further exploring the influences of HLP chemical distributions and complex surface environments on cell-material interactions.

Preliminary study of the biomechanical behavior and physical characteristics of tantalum (Ta)-coated prostheses.

BACKGROUND: Use of Ta biomaterials in medicine started in the middle of the last century. The good biocompatibility and chemical stability, and the unique physical characteristics of Ta metal have resulted in many possible developments of Ta biomaterials. METHODS: In this study, histopathological observation, histomorphometric analysis, scanning electron microscope (SEM) observation, energy-dispersive X-ray spectroscopy (EDX) analysis, biomechanical testing, and examination of the coating's mechanical strength have been used to evaluate the value of clinical application of Ta-coated prostheses prepared by a plasma-spraying process. RESULTS: Histopathological observation has demonstrated that the periprosthetic new bone tissues tightly and stably adhere to the Ta coating after the implantation, with no signs of loosening. Early after implantation, there is no significant difference in periprosthetic bone volume and ultimate shear strength between Ta-coated and Ti-coated prostheses (P > 0.05). EDX analysis suggests that the ultimate shear stress does not damage Ta coating. Mechanical strength testing shows that the adhesive strength and Vicker's surface hardness (HV) of the Ta coating are significantly higher than those of the Ti coating (P < 0.01). CONCLUSIONS: Ta coating has good stability and bone biocompatibility; the extraordinary physical characteristics of Ta coating have great significance in maintaining prosthetic stability and surface porosity after implantation.

Self-assembled monolayers direct a LiF-rich interphase toward long-life lithium metal batteries.

High-energy density lithium (Li) metal batteries (LMBs) are promising for energy storage applications but suffer from uncontrollable electrolyte degradation and the consequently formed unstable solid-electrolyte interphase (SEI). In this study, we designed self-assembled monolayers (SAMs) with high-density and long-range-ordered polar carboxyl groups linked to an aluminum oxide-coated separator to provide strong dipole moments, thus offering excess electrons to accelerate the degradation dynamics of carbon-fluorine bond cleavage in Li bis(trifluoromethanesulfonyl)imide. Hence, an SEI with enriched lithium fluoride (LiF) nanocrystals is generated, facilitating rapid Li+ transfer and suppressing dendritic Li growth. In particular, the SAMs endow the full cells with substantially enhanced cyclability under high cathode loading, limited Li excess, and lean electrolyte conditions. As such, our work extends the long-established SAMs technology into a platform to control electrolyte degradation and SEI formation toward LMBs with ultralong life spans.

Products distribution and interaction mechanism during co-pyrolysis of rice husk and oily sludge by experiments and reaction force field simulation.

In this work, the co-pyrolysis behavior of rice husk (RH) and oily sludge (OS) was investigated by combining experiments and simulation. The thermogravimetric-derivative thermogravimetric (TG-DTG) and Reaction force field (ReaxFF MD) results indicate that synergetic effects exist in co-pyrolysis. Compared with the single component pyrolysis, the activation energy of RH and OS in co-pyrolysis was decreased by 15.97% and 17.14% shown by kinetic analysis, respectively. The Pyrolysis-gas chromatography/mass spectrometry (PY-GC/MS) experiments, and simulation products analysis reveal that more bio-oil and molecules with low molecular weight were produced during the co-pyrolysis process. The synergetic effect mechanism was studied by detecting the variation of free radical intermediates. The results show that hydroxyl radicals from RH pyrolysis reduced cracking temperature of OS, and the hydrogen radicals from OS pyrolysis increased the degree of ring-splitting of RH. The study explores an approach to identify the synergetic effect and reveal the mechanism of co-pyrolysis.

Chromosome 10 abnormality predicts prognosis of neuroblastoma patients with bone marrow metastasis.

BACKGROUND: Neuroblastoma (NB) is the most common extracranial solid tumor in children. It is known for high heterogeneity and concealed onset. In recent years, the mechanism of its occurrence and development has been gradually revealed. The purpose of this study is to summarize the clinical characteristics of children with NB and abnormal chromosome 10, and to investigate the relationship between the number and structure of chromosome 10 abnormalities and NB prognosis. METHODS: Chromosome G-banding was used at the time of diagnosis to evaluate the genetics of chromosomes in patients with NB and track their clinical characteristics and prognosis. All participants were diagnosed with NB in the Medical Oncology Department of the Beijing Children's Hospital from May 2015 to December 2018 and were followed up with for at least 1 year. RESULTS: Of all 150 patients with bone marrow metastases, 42 were clearly diagnosed with chromosomal abnormalities. Thirteen patients showed abnormalities in chromosome 10, and chromosome 10 was the most commonly missing chromosome. These 13 patients had higher LDH and lower OS and EFS than children with chromosomal abnormalities who did not have an abnormality in chromosome 10. Eight patients had both MYCN amplification and 1p36 deletion. Two patients had optic nerve damage and no vision, and one patient had left supraorbital metastases 5 months after treatment. CONCLUSIONS: The results indicated that chromosome 10 might be a new prognostic marker for NB. MYCN amplification and 1p36 deletion may be related to chromosome 10 abnormalities in NB. Additionally, NB patients with abnormal chromosome 10 were prone to orbital metastases.

Ultrathin Film Composite Membranes Fabricated by Novel In Situ Free Interfacial Polymerization for Desalination.

Ultrathin polyamide nanofilms are desirable as the separation layers for the highly permeable thin-film composite (TFC) membranes, and recently, their lowest thickness limits have attracted a lot of attention from researchers. Due to the interference of the underlying substrate, preparing a defect-free, ultrathin polyamide nanofilm directly on top of a membrane substrate remains a great challenge. Herein, we report a novel fabrication technique of TFC membranes, named in situ free interfacial polymerization (IFIP), where the IP reaction occurs at the uniform, free oil-water interface dozens of microns above the substrate, and then the resulting nanofilm spontaneously assembles into the TFC structure without extra manual transfer. This IFIP method not only overcomes the limitations of conventional IP, succeeding in preparing ultrathin-nanofilm composite membranes for nanofiltration and reverse osmosis application, but also enables scale membrane manufacturing that is not feasible via previously reported free-standing IP. Based on the IFIP method, the thickness of the polyamide nanofilm was successfully reduced to ca. 3-4 nm, which we believe is close to the ultrathin limit of the polyamide nanofilm for separation application. Meanwhile, the structure-performance relationship revealed that the strategy of increasing TFC membrane permeance by reducing polyamide layer thickness also had a limit. Besides, the IP mechanisms in regard to the formation of surface morphology and film growth were explored by combining experimental and molecular simulation methods. Overall, this work is expected to push forward the fundamental study and practical application of the ultrathin-film composite membrane.

A less harmful system of preparing robust fabrics for integrated self-cleaning, oil-water separation and water purification.

Although the development of constructing oil-water separation materials is quick, the defects of using harmful regents, weak stability and single function still exist. Here, we report an effective and less-harmful system with poly-dimethylsiloxane (PDMS)/ZnO composite solution to fabricate robust superhydrophobic surfaces for oil-water separation and removal of organic pollutant. The obtained samples were characterized by a range of instruments. The water contact angle (WCA) of coated cotton was 155.6°, which attributed to the synergetic effect of low surface energy of PDMS and roughness of ZnO nanoparticles. The coated cotton was tolerant to mechanical damage, various corrosive solvents and temperature conditions. The emphasis of this study is the combination of superhydrophobicity and photocatalysis, resulting in multifunctional cotton with dual self-cleaning properties, outstanding oil-water separation ability and efficient water purification property. When utilized a simple laboratory facility, the cotton could separate water from oil-water mixture with a high efficiency (99.3%). Furthermore, the dyed water could be purified with coated cotton through photocatalysis under UV light and became colorless. Meanwhile, this mild and facile method could also be utilized to modify other porous substrates, such as PET, silk, non-woven and sponge. Therefore, the characteristics of environmental protection and easy operation make this cotton a desirable candidate for extensive applications in self-cleaning, oil-water separation and water purification.

Cotreatment of MSWI Fly Ash and Granulated Lead Smelting Slag Using a Geopolymer System.

Municipal solid waste incineration fly ash (MSWI FA) and granulated lead smelting slag (GLSS) are toxic industrial wastes. In the present study, granulated lead smelting slag (GLSS) was pretreated as a geopolymer precursor through the high-energy ball milling activation process, which could be used as a geopolymeric solidification/stabilization (S/S) reagent for MSWI FA. The S/S process has been estimated through the physical properties and heavy metals leachability of the S/S matrices. The results show that the compressive strength of the geopolymer matrix reaches 15.32 MPa after curing for 28 days under the best parameters, and the physical properties meet the requirement of MU10 grade fly ash brick. In addition, the toxicity characteristic leaching procedure (TCLP) test results show that arsenic and heavy metals are immobilized effectively in the geopolymer matrix, and their concentrations in the leachate are far below the US EPA TCLP limits. The hydration products of the geopolymer binder are characterized by X-ray diffraction and Fourier transform infrared methods. The results show that the geopolymer gel and Friedel's salt are the main hydration products. The S/S mechanism of the arsenic and heavy metals in the geopolymer matrix mainly involves physical encapsulation of the geopolymer gel, geopolymer adsorption and ion exchange of Friedel's salt.

Fabrication of superhydrophobic cotton fabric with fluorinated TiO2 sol by a green and one-step sol-gel process.

The purpose of this paper is to propose a facile, green and low-cost approach of the preparation of superhydrophobic cotton textiles, which can be fabricated with fluorinated TiO2 sols via a sol-gel method. The coating was prepared with TiO2 sols catalyzed with acetic acid, then modified by poly(Hexafluorobutyl methacrylate) (PHFBMA) which was synthesized by free radical polymerization. The wettability, surface morphology and chemical composition of pristine and modified cotton fabrics were investigated. The modified fabric presents a high contact angle reached up to 152.5°. The successful incorporation of fluorinated TiO2 nanoparticles into cotton fabric was verified by the above measurements results. Additionally, the chemical stability of the coated fabric has been tested by immersing in different pH solutions and organic solvents, demonstrating the outstanding water repellency of the fabric. Furthermore, the treated cotton fabric shows excellent self-cleaning properties, which makes it an ideal material for large-scaled industrial applications in various conditions.

Ultrathin Polyamide Membrane with Decreased Porosity Designed for Outstanding Water-Softening Performance and Superior Antifouling Properties.

Poly(piperazine-amide)-based nanofiltration membranes exhibit a smooth surface and superior antifouling properties but often have lower Ca2+ and Mg2+ rejection due to their larger inner micropore and thus cannot be extensively used in water-softening applications. To decrease the pore size of poly(piperazine-amide) membranes, we designed and synthesized a novel monomer, 1,2,3,4-cyclobutane tetracarboxylic acid chloride (BTC), which possesses a smaller molecular conformation than trimesoyl chloride (TMC). The thickness of the prepared BTC-piperazine (PIP) polyamide nanofilm via interfacial polymerization is as thin as 15 nm, significantly lower than the 50 nm thickness of the TMC-PIP nanofilm. The surface characterization reveals that the BTC-PIP polyamide membrane exhibits an enhanced hydrophilicity, a smooth surface, and a decreased surface-negative charge. The desalination performance (both rejection and water flux) of these membranes in terms of Ca2+ and Mg2+ exceeds that of the current commercial water-softening membranes. In addition, the BTC-PIP polyamide membrane also exhibits superior antifouling properties compared to the TMC-based polyamide membrane. More importantly, molecular simulations show that the BTC-PIP membrane has a lower average pore size than that of the TMC-PIP membrane, which demonstrates an enhanced steric hindrance effect, as confirmed by desalination performance. Our results demonstrate that in the household and industrial water-softening market, BTC-PIP membrane with decreased porosity, enhanced hydrophilicity, and smooth surface is preferred alternative to the conventional TMC-based polyamide membranes.