Quantification of interfacial interaction related with adhesive membrane fouling by genetic algorithm back propagation (GABP) neural network

The artificial intelligence-based agricultural field irrigation warning system using GA-BP neural network under smart agriculture

This work explores an intelligent field irrigation warning system based on the Enhanced Genetic Algorithm-Backpropagation Neural Network (EGA-BPNN) model in the context of smart agriculture. To achieve this, irrigation flow prediction in agricultural fields is chosen as the research topic. Firstly, the BPNN principles are studied, revealing issues such as sensitivity to initial values, susceptibility to local optima, and sample dependency. To address these problems, a genetic algorithm (GA) is adopted for optimizing the BPNN, and the EGA-BPNN model is used to predict irrigation flow in agricultural fields. Secondly, the EGA-BPNN model can overcome the local optimization and overfitting problems of traditional BPNN through the global search ability of GA. Moreover, it is suitable for the irrigation flow prediction task with complex environmental factors in smart agriculture. Finally, comparative experiments compare the prediction accuracy of BPNN and EGA-BPNN using single and dual water level flow prediction models respectively. The results reveal that as the number of nodes in the hidden layer increases, the model's Mean Squared Error (MSE) and Relative Error (RE) show a decreasing trend, indicating an improvement in model prediction accuracy. When the number of nodes in the hidden layer increases from 6 to 16, the MSE of the single and dual water level flow prediction models decreases from 4.53×10-4 to 3.68×10-4 and 2.38×10-4 to 1.66×10-4, respectively. Under a standalone BPNN, the absolute relative error in flow prediction is 1.09%. In contrast, the EGA-BPNN model achieves a significantly lower mean absolute relative error of 0.41% for single-flow prediction, demonstrating superior prediction performance. Furthermore, compared to the BPNN, the EGA-BPNN model exhibits a 2.11 reduction in MSE, further emphasizing the positive impact of introducing the GA on model performance. The research outcomes contribute to more accurate water resource planning and management, providing a more reliable basis for decision-making.

High-efficiency PMS activation by difunctional Co-Fe PBA/g-C3N4 S-scheme heterojunction for oxytetracycline degradation: Performance evaluation and mechanism insight

The utilization of sulfate radical-based advanced oxidation processes (SR-AOPs) has captivated the academic community due to their minimal energy requirements and superior efficacy in peroxomonosulfate (PMS) activation for pollutant decomposition. Notwithstanding these advantages, engineering an effective and economical catalyst for PMS activation presents a considerable hurdle. In the present study, a metal-organic framework of CoFe PBA is ingeniously anchored onto g-C3N4 nanosheets, resulting in the formation of an innovative CoFe PBA/g-C3N4 S-scheme heterojunction that demonstrates remarkable efficiency in PMS activation. Intriguingly, the catalytic efficiency of CoFe PBA/g-C3N4 surpasses that of g-C3N4 and CoFe PBA by 7-fold and 2.33-fold, respectively. The heightened activity of CoFe PBA/g-C3N4 heterojunction is attributed to the enhanced charge transfer efficiency, a consequence of the successful heterojunction formation. Concurrently, the ability of photoexcited electrons to reduce Co3+/Fe3+ to Co2+/Fe2+ bolsters PMS activation. Significantly, this heterojunction retains unparalleled stability in degrading oxytetracycline without discernible performance attenuation, heralding its commendable prospects in real-world applications. Besides, mechanism exploration indicates that SO4-, h+, and electron transfer contribute to oxytetracycline degradation in the CoFe PBA/g-C3N4 system. This investigation serves as a beacon for the strategic development of highly active and stable catalysts for PMS activation, aiming at environmental decontamination.

Predicting thermodynamic adhesion energies of membrane fouling in planktonic anammox MBR via backpropagation neural network model

Predicting thermodynamic adhesion energies was a critical strategy for mitigating membrane fouling. This study utilized a backpropagation (BP) neural network model to predict the thermodynamic adhesion energies associated with membrane fouling in a planktonic anammox MBR. Acid-base (ΔGAB), electrostatic double layer (ΔGEL), and Lifshitz-van der Waals (ΔGLW) energies were selected as output variables, the training dataset was collected by the advanced Derjaguin-Landau-Verwey-Overbeek (XDLVO) method. Optimization results identified "7-10-3″ as the optimal network structure for the BP model. The prediction results demonstrated a high degree of fit between the predicted and experimental values of thermodynamic adhesion energy (R2 ≥ 0.9278), indicating a robust predictive capability of the model in this study. Overall, the study presented a practical BP neural network model for predicting thermodynamic adhesion energies, significantly enhancing the prediction tool for adhesive fouling behavior in anammox MBRs.

Reversible Fusion-Fission MXene Fiber-Based Microelectrodes for Target-Specific Gram-Positive and Gram-Negative Bacterium Discrimination

Inaccurate or cumbersome clinical pathogen diagnosis between Gram-positive bacteria (G+) and Gram-negative (G-) bacteria lead to delayed clinical therapeutic interventions. Microelectrode-based electrochemical sensors exhibit the significant advantages of rapid response and minimal sample consumption, but the loading capacity and discrimination precision are weak. Herein, we develop reversible fusion-fission MXene-based fiber microelectrodes for G+/G- bacteria analysis. During the fissuring process, the spatial utilization, loading capacity, sensitivity, and selectivity of microelectrodes were maximized, and polymyxin B and vancomycin were assembled for G+/G- identification. The surface-tension-driven reversible fusion facilitated its reusability. A deep learning model was further applied for the electrochemical impedance spectroscopy (EIS) identification in diverse ratio concentrations of G+ and G- of (1:100-100:1) with higher accuracy (>93%) and gave predictable detection results for unknown samples. Meanwhile, the as-proposed sensing platform reached higher sensitivity toward E. coli (24.3 CFU/mL) and S. aureus (37.2 CFU/mL) in 20 min. The as-proposed platform provides valuable insights for bacterium discrimination and quantification.

Multifunctional polyaniline modified calcium alginate aerogel membrane with antibacterial, oil-water separation, dye and heavy metal ions removal properties for complex water purification

With the rapid development of urbanization, the discharge of industrial wastewater has led to increasingly critical water pollution issues. Additionally, heavy metals, organic dyes, microorganisms and oil pollution often coexist and have persistence and harmfulness. Developing materials that can treat these complex pollutants simultaneously has important practical significance. In this study, a calcium alginate-based aerogel membrane (PANI@CA membrane) was prepared by spraying, polymerization, Ca2+ cross-linking and freeze-drying using aniline and sodium alginate as raw materials. Oil-water emulsion can be separated by PANI@CA membrane only under gravity, and the separation efficiency was as high as 99 %. At the same time, the membrane can effectively intercept or adsorb organic dyes and heavy metal ions. The removal rates of methylene blue and Congo red were above 92 % and 63 % respectively even after ten times of cyclic filtration. The removal rate of Pb2+ was up to 95 %. In addition, PANI@CA membrane shows excellent photothermal conversion ability, and it can effectively kill Staphylococcus aureus under 808 nm laser irradiation. PANI@CA membrane has the advantages of low cost, simple preparation, good stability and high recycling ability, and has potential application prospects in wastewater treatment.

Molecular level unveils anion exchange membrane fouling induced by natural organic matter via XDLVO and molecular simulation

Membrane fouling, critically determined by the interplay of interfacial interaction between foulant and membrane, is a critical impediment that limits application extension of electrodialysis (ED) process. In this study, the extended Derjaguin-Landau-Verwey-Overbeek (XDLVO) model and molecular simulation were performed to quantify the interaction energy barrier for revealing anion exchange membranes (AEMs) fouling mechanisms of calcium ions coexisted with natural organic matter (NOM) (sodium alginate, humic acid, and bovine serum albumin). The insight gained from DMol3 module was also utilized to interpret the adhesion process of NOM at the molecular level. The interaction energy suggested that the presence of Ca-NOM complex magnify the adhesion on the surface cavities of AEMs structures. The molecular simulation and XDLVO presented a good agreement in predicting the fouling trajectory based on the experimental findings. The short-path acid-base interaction exerted a predominant influence on exploring the fouling formation process. In addition, the sodium alginate displayed more stable adhesion behavior through calcium ions bridges stimuli than humic acid and bovine serum albumin. In particular, the molecular simulation calculations exhibited a superior level of concurrence with colloid growth of membrane fouling. Combined XDLVO theory with DMol3 model proposed a new approach to understand membrane fouling mechanisms in ED process.

Enhanced management and antifouling performance of a novel NiFe-LDH@MnO2/PVDF hybrid membrane for efficient oily wastewater treatment

Layered double hydroxides (LDHs) have gained significant recognition for their facile synthesis and super-hydrophilic two-dimensional (2D) structure to fabricate antifouling membranes for oily wastewater separation. However, conventional PVDF membranes, due to their hydrophobic nature and inert matrix, often exhibit insufficient permeance and compatibility. In this study, a novel NiFe-LDH@MnO2/PVDF membrane was synthesized using ultrasonic, redox, and microwave-hydrothermal processes. This innovative approach cultivated grass-like NiFe-LDH@MnO2 nanoparticles within an inert PVDF matrix, promoting the growth of highly hydrophilic composites. The presence of NiFe-LDH@MnO2 resulted in pronounced enhancements in surface morphology, interfacial wettability, and oil rejection for the fabricated membrane. The optimal NiFe-LDH@MnO2/PVDF-2 membrane exhibited an extremely high pure water flux (1364 L m-2•h-1), and increased oil rejection (from 81.2% to 93.5%) without sacrificing water permeation compared to the original PVDF membrane. Additionally, the NiFe-LDH@MnO2/PVDF membrane demonstrated remarkable antifouling properties, evident by an exceptional fouling resistance ratio of 96.8% following slight water rinsing. Mechanistic insights into the enhanced antifouling performance were elucidated through a comparative "semi-immersion" investigation. The facile synthesis method, coupled with the improved membrane performance, highlights the potential application prospects of this hybrid membrane in emulsified oily wastewater treatment and environmental remediation.

Revealing the contradiction between DLVO/XDLVO theory and membrane fouling propensity for oil-in-water emulsion separation

Oil fouling is the crucial issue for the separation of oil-in-water emulsion by membrane technology. The latest research found that the membrane fouling rate was opposite to the widely used theoretical prediction by Derjaguin-Landau-Verwey-Overbeek (DLVO) or extended DLVO (XDLVO) theory. To interpret the contradiction, the molecular dynamics was adopted to explore the molecular behavior of oil and emulsifier (Tween 80) at membrane interface with the assistance of DLVO/XDLVO theory and membrane fouling models. The decreased flux attenuation and fitting of fouling models proved that the existence of Tween 80 effectively alleviated membrane fouling. Conversely, DLVO/XDLVO theory predicted that the membrane fouling should be exacerbated with the increase of Tween 80 concentration in O/W emulsion. This contradiction originated from the different interaction energy between oil/Tween 80 molecules and polyether sulfone (PES) membrane. The favorable free energy of Tween 80 was resulted from the sulfuryl groups in PES and hydrogen bonds (O-H…O) formation further strengthened the interaction. Therefore, Tween 80 could preferentially adsorb on membrane surface and form an isolation layer by demulsification and steric hindrance and resist the aggregation of oil, which effectively alleviated membrane fouling. This study provided a new insight in the interpretation of interaction in O/W emulsion.

Superwettable cellulose acetate-based nanofiber membrane with spider-web structure for highly efficient oily water purification

Electrospinning nanofibers membrane has received much attention to remove the insoluble oil from the sewage, while the poor mechanical strength and low oil/water separation efficiency of membranes limit their practical application. Here, we prepared a superwettable deacetylated cellulose acetate (d-CA)-based electrospinning nanofibers membrane simply dipped by bacterial cellulose (BC) and cross-linked with citric acid (CCA) to construct the spider-web structure spontaneously. Compared with the pristine d-CA membrane, the obtained d-CA/BC@CCA membrane exhibits the remarkable oil/water separation performance. The flux and separation efficiency of n-hexane/water emulsion without (SFE) and with (SSE) emulsifier for d-CA/BC@CCA membrane are 9364 L·m-2·h-1·bar-1, 98.34 % and 5479 L·m-2·h-1·bar-1, 99.39 %, respectively, which are mainly attributed to the improved hydrophilicity of its surface and the decreased pore sizes caused by the unique spider-web structure. In addition, d-CA/BC@CCA membrane also possesses the outstanding mechanical properties, the better cycle stability, as well as the excellent durability. This study provides a novel strategy for the construction of the high-performance oil/water separation membrane.

Effectiveness of different CuO morphologies nanomaterials on the permeability, antifouling, and mechanical properties of PVDF/PVP/CuO ultrafiltration membrane for water treatment

The hydrophobic nature of Poly (vinylidene fluoride) (PVDF) is a significant barrier to use in ultrafiltration, resulting in fouling, flux decline, and reduced lifespan in water treatment. This study examines the effectiveness of different morphologies of CuO nanomaterials (NMs) (spherical, rod, plate, and flower), synthesized by the facile hydrothermal method, to modify PVDF membrane with PVP additive for improving the performance of water permeability and antifouling. Such membrane configurations with different morphologies of CuO NMs improved hydrophilicity with a maximum water flux of 222-263 L m-2h-1 compared to 195 L m-2h-1 for the bare membrane and exhibited excellent thermal and mechanical strengths. The characterization results exhibited that plate-like CuO NMs were dispersed uniformly in the membrane matrix, and their incorporation as a composite improved the membrane properties. From the antifouling test with the bovine serum albumin (BSA) solution, the membrane with plate-like CuO NMs had the highest flux recovery ratio (FRR) (∼91%) and the lowest irreversible fouling ratio (∼10%). The antifouling enhancement was due to less interaction between modified membranes and foulant. Further, the nanocomposite membrane showed excellent stability and negligible Cu2+ ion leaching. Overall, our findings provide a new strategy for developing inorganic nanocomposite PVDF membranes for water treatment.