Rapid Inversion Research Articles

Hydrophilic magnetic polyimide nanocomposite for rapid extraction of phthalate esters from drinking water samples

Phthalate esters (PAEs), a type of widely used plasticizer, tend to migrate from plastic products into drinking water. In this paper, a hydrophilic magnetic Fe3O4/polyimide/polyvinylpyrrolidone (Fe3O4/PI/PVP) nanocomposite was synthesized by simultaneously encapsulating Fe3O4 nanoparticles with PVP in PI via a simple and rapid phase inversion method. The prepared Fe3O4/PI/PVP nanocomposite was used as an adsorbent for magnetic solid-phase extraction (MSPE) to determine 6 PAEs in drinking water samples in combination with GC–MS. Due to the addition of PVP, the nanocomposite has good hydrophilicity and can be easily dispersed in water. The nanocomposite was directly used for extraction without solvent activation, and extraction equilibrium was achieved within 5 min with high extraction recovery. The Fe3O4/PI/PVP nanocomposite-based MSPE/GC–MS method showed great potential for PAE analysis, with a limit of detection of 6 PAEs ranging from 0.006 to 0.167 μg/L and relative standard deviations (RSD) between 0.7 % and 10.9 %. The developed method was successfully applied to determine trace PAEs in several drinking water samples with recoveries ranging from 73.2 % to 118.5 % and RSDs less than 14.0 %. This work provides a new reference for the preparation of hydrophilic magnetic adsorbent materials and a method for the enrichment and separation of PAEs in aqueous matrices.

Inverting magnetotelluric data using a physics-guided auto-encoder with scaling laws extension

Artificial neural networks (ANN) have gained significant attention in magnetotelluric (MT) inversions due to their ability to generate rapid inversion results compared to traditional methods. While a well-trained ANN can deliver near-instantaneous results, offering substantial computational advantages, its practical application is often limited by difficulties in accurately fitting observed data. To address this limitation, we introduce a novel approach that customizes an auto-encoder (AE) whose decoder is replaced with the MT forward operator. This integration accounts for the governing physical laws of MT and compels the ANN to focus not only on learning the statistical relationships from data but also on producing physically consistent results. Moreover, because ANN-based inversions are sensitive to variations in observation systems, we employ scaling laws to transform real-world observation systems into formats compatible with the trained ANN. Synthetic and real-world examples show that our scheme can recover comparable results with higher computational efficiency compared to the classic Occam’s inversion. This study not only perfectly fits the observed data but also enhances the adaptability and efficiency of ANN-based inversions in complex real-world environments.

Shallow Water Bathymetry Inversion Based on Machine Learning Using ICESat-2 and Sentinel-2 Data

Shallow water bathymetry is essential for maritime navigation, environmental monitoring, and coastal management. While traditional methods such as sonar and airborne LiDAR provide high accuracy, their high cost and time-consuming nature limit their application in remote and sensitive areas. Satellite remote sensing offers a cost-effective and rapid alternative for large-scale bathymetric inversion, but it still relies on significant in situ data to establish a mapping relationship between spectral data and water depth. The ICESat-2 satellite, with its photon-counting LiDAR, presents a promising solution for acquiring bathymetric data in shallow coastal regions. This study proposes a rapid bathymetric inversion method based on ICESat-2 and Sentinel-2 data, integrating spectral information, the Forel-Ule Index (FUI) for water color, and spatial location data (normalized X and Y coordinates and polar coordinates). An automated script for extracting bathymetric photons in shallow water regions is provided, aiming to facilitate the use of ICESat-2 data by researchers. Multiple machine learning models were applied to invert bathymetry in the Dongsha Islands, and their performance was compared. The results show that the XG-CID and RF-CID models achieved the highest inversion accuracies, 93% and 94%, respectively, with the XG-CID model performing best in the range from −10 m to 0 m and the RF-CID model excelling in the range from −15 m to −10 m.

Estimation of the spatial variability of the New England Mud Patch geoacoustic properties using a distributed array of hydrophones and deep learning

This article presents a spatial environmental inversion scheme using broadband impulse signals with deep learning (DL) to model a single spatially-varying sediment layer over a fixed basement. The method is applied to data from the Seabed Characterization Experiment 2022 (SBCEX22) in the New England Mud-Patch (NEMP). Signal Underwater Sound (SUS) explosive charges generated impulsive signals recorded by a distributed array of bottom-moored hydrophones. The inversion scheme is first validated on a range-dependent synthetic test set simulating SBCEX22 conditions, then applied to experimental data to predict the lateral spatial structure of sediment sound speed and its ratio with the interfacial water sound speed. Traditional geoacoustic inversion requires significant computational resources. Here, a neural network enables rapid single-signal inversion, allowing the processing of 1836 signals along 722 tracks. The method is applied to both synthetic and experimental data. Results from experimental data suggest an increase in both absolute compressional sound speed and sound speed ratio from southwest to northeast in the NEMP, consistent with published coring surveys and geoacoustic inversion results. This approach demonstrates the potential of DL for efficient spatial geoacoustic inversion in shallow water environments.

Model for Inverting the Leaf Area Index of Green Plums by Integrating IoT Environmental Monitoring Data and Leaf Relative Content of Chlorophyll Values

The quantitative inversion of the leaf area index (LAI) of green plum trees is crucial for orchard field management and yield prediction. The data on the relative content of chlorophyll (SPAD) in leaves and environmental data from orchards show a significant correlation with LAI. Effectively integrating these two data types for LAI inversion is important to explore. This study proposes a multi−source decision fusion LAI inversion model for green plums based on their adjusted determination coefficient (MDF−ADRS). First, three statistical methods—Pearson, Spearman rank, and Kendall rank correlation analyses—were used to measure the linear relationships between variables, and the six environmental factors most highly correlated with LAI were selected from the orchard’s environmental data. Then, using multivariate statistical analysis methods, LAI inversion models based on environmental feature factors (EFs−PM) and SPAD (SPAD−PM) were established. Finally, a weight optimization allocation strategy was employed to achieve a multi−source decision fusion LAI inversion model for green plums. This strategy adaptively allocates weights based on the predictive performance of each data source. Unlike traditional models that rely on fixed weights or a single data source, this approach allows the model to increase the influence of a key data source when its predictive strength is high and reduce noise interference when it is weaker. This dynamic adjustment not only enhances the model’s robustness under varying environmental conditions but also effectively mitigates potential biases when a particular data source becomes temporarily unreliable. Our experimental results show that the MDF−ADRS model achieves an R2 of 0.88 and an RMSE of 0.39 in the validation set, outperforming other fusion methods. Compared to the EFs−PM and SPAD−PM models, the R2 increased by 0.19 and 0.26, respectively, and the RMSE decreased by 0.16 and 0.22. This model effectively integrates multiple sources of data from green plum orchards, enabling rapid inversion and improving the accuracy of green plum LAI estimation, providing a technical reference for monitoring the growth and managing the production of green plums.

Research on the Quantitative Inversion of Soil Iron Oxide Content Using Hyperspectral Remote Sensing and Machine Learning Algorithms in the Lufeng Annular Structural Area of Yunnan, China.

This study used hyperspectral remote sensing to rapidly, economically, and non-destructively determine the soil iron oxide content of the Dinosaur Valley annular tectonic region of Lufeng, Yunnan Province. The laboratory determined the iron oxide content and original spectral reflectance (OR) in 138 surface soil samples. We first subjected the OR data to Savizky-Golay smoothing, followed by four spectral transformations-continuum removal reflectance, reciprocal logarithm reflectance, standard normal variate reflectance, and first-order differential reflectance-which improved the signal-to-noise ratio of the spectral curves and highlighted the spectral features. Then, we combined the correlation coefficient method (CC), competitive adaptive reweighting algorithm, and Boruta algorithm to screen out the characteristic wavelength. From this, we constructed the linear partial least squares regression model, nonlinear random forest, and XGBoost machine learning algorithms. The results show that the CC-Boruta method can effectively remove any noise and irrelevant information to improve the model's accuracy and stability. The XGBoost nonlinear machine learning algorithm model better captures the complex nonlinear relationship between the spectra and iron oxide content, thus improving its accuracy. This provides a relevant reference for the rapid and accurate inversion of iron oxide content in soil using hyperspectral data.

Automatically Differentiable Higher-Order Parabolic Equation for Real-Time Underwater Sound Speed Profile Sensing

This paper is dedicated to the acoustic inversion of the vertical sound speed profiles (SSPs) in the underwater marine environment. The method of automatic differentiation is applied for the first time in this context. Representing the finite-difference Padé approximation of the propagation operator as a computational graph allows for the analytical computation of the gradient with respect to the SSP directly within the numerical scheme. The availability of the gradient, along with the high computational efficiency of the numerical method used, enables rapid inversion of the SSP based on acoustic measurements from a hydrophone array. It is demonstrated that local optimization methods can be effectively used for real-time sound speed inversion. Comparative analysis with existing methods shows the significant superiority of the proposed method in terms of computation speed.

Design of Stable Chiral Aminosulfonium Ylides and Their Catalytic Asymmetric Synthesis.

The isolation and catalytic enantioselective synthesis of configurationally stable S-stereogenic sulfonium ylides have been significant challenges in the field of asymmetric synthesis. These reactive intermediates are crucial for a variety of synthetic transformations, yet their inherent tendency towards rapid inversion at the sulfur stereocenter has hindered their practical utilization. Conventional approaches have focused on strategies that incorporate a C=S bond-containing cyclic framework to help mitigate this stereochemical lability. In this work, we present an alternative tactic that leverages the stabilizing influence of an adjacent N-atom and cyclic sulfide moiety. Exploiting a copper catalyzed enantioselective intermolecular carbene transfer reaction, structurally diverse S-stereogenic aminosulfonium ylides have been achieved in excellent yields and enantioselectivities. Experimental results indicate that the careful selection of 2-diazo-1,3-diketone precursors is crucial for achieving optimal stereoinduction in this transformation. The resulting highly enantioenriched aminosulfonium ylides allow for further stereospecific elaborations to furnish aminosulfonium ylide oxides and sulfinamide. This work expands the boundaries of chiral sulfonium ylide chemistry, providing access to a broad range of previously elusive S-stereogenic aminosulfonium ylide scaffolds.

Design of Stable Chiral Aminosulfonium Ylides and Their Catalytic Asymmetric Synthesis

AbstractThe isolation and catalytic enantioselective synthesis of configurationally stable S‐stereogenic sulfonium ylides have been significant challenges in the field of asymmetric synthesis. These reactive intermediates are crucial for a variety of synthetic transformations, yet their inherent tendency towards rapid inversion at the sulfur stereocenter has hindered their practical utilization. Conventional approaches have focused on strategies that incorporate a C=S bond‐containing cyclic framework to help mitigate this stereochemical lability. In this work, we present an alternative tactic that leverages the stabilizing influence of an adjacent N‐atom and cyclic sulfide moiety. Exploiting a copper catalyzed enantioselective intermolecular carbene transfer reaction, structurally diverse S‐stereogenic aminosulfonium ylides have been achieved in excellent yields and enantioselectivities. Experimental results indicate that the careful selection of 2‐diazo‐1,3‐diketone precursors is crucial for achieving optimal stereoinduction in this transformation. The resulting highly enantioenriched aminosulfonium ylides allow for further stereospecific elaborations to furnish aminosulfonium ylide oxides and sulfinamide. This work expands the boundaries of chiral sulfonium ylide chemistry, providing access to a broad range of previously elusive S‐stereogenic aminosulfonium ylide scaffolds.

Accelerated 2D radial Look-Locker T1 mapping using a deep learning-based rapid inversion recovery sampling technique.

Efficient abdominal coverage with T1-mapping methods currently available in the clinic is limited by the breath hold period (BHP) and the time needed for T1 recovery. This work develops a T1-mapping framework for efficient abdominal coverage based on rapid T1 recovery curve (T1RC) sampling, slice-selective inversion, optimized slice interleaving, and a convolutional neural network (CNN)-based T1 estimation. The effect of reducing the T1RC sampling was evaluated by comparing T1 estimates from T1RC ranging from 0.63 to 2.0s with reference T1 values obtained from T1RC =2.5-5s. Slice interleaving methodologies were evaluated by comparing the T1 variation in abdominal organs across slices. The repeatability of the proposed framework was demonstrated by performing acquisition on test subjects across imaging sessions. Analysis of in vivo data based on retrospectively shortening the T1RC showed that with the CNN framework, a T1RC =0.84 s yielded T1 estimates without significant changes in mean T1 (p> 0.05) or significant increase in T1 variability (p> 0.48) compared to the reference. Prospectively acquired data using T1RC =0.84 s, an optimized slice interleaving scheme, and the CNN framework enabled 21 slices in a 20 s BHP. Analyses across abdominal organs produced T1 values within 2% of the reference. Repeatability experiments yielded Pearson's correlation, repeatability coefficient, and coefficient of variation of 0.99, 2.5%, and 0.12%, respectively. The proposed T1 mapping framework provides full abdominal coverage within a single BHP.

Synthesis of 5-[(Alkylsulfanyl)methyl]-1,3-dioxanes from 3-[(Alkylsulfanyl)methyl]pentane-2,4-diols

Heterocyclization of 3-[(alkylsulfanyl)methyl]pentane-2,4-diols with formaldehyde or propionaldehyde in boiling benzene in the presence of hydrochloric acid afforded new 5-[(alkylsulfanyl)methyl]-4,6-dimethyl-1,3-dioxanes. 1,3-Dioxanes are formed as a mixture of 4,6-cis- and 4,6-trans-isomers at a ratio of 1:0.3–0.9. In both isomers, the preferred chair conformation is realized. In the 4,6-cis-isomers of all 1,3-dioxanes, the methyl groups occupy a diequatorial position, and the alkylsulfanylmethyl substituent is in an axial orientation. In the 4,6-cis-isomer of 5-[(pentylsulfanyl)methyl]-2-ethyl-1,3-dioxane, the ethyl group is oriented equatorially. The 4,6-trans-isomers of 5-[(alkylsulfanyl)methyl]-1,3-dioxanes are characterized by a rapid conformational inversion chair–chair.

Drone-Based Multispectral Remote Sensing Inversion for Typical Crop Soil Moisture under Dry Farming Conditions

Drone multispectral technology enables the real-time monitoring and analysis of soil moisture across vast agricultural lands. overcoming the time-consuming, labor-intensive, and spatial discontinuity constraints of traditional methods. This study establishes a rapid inversion model for deep soil moisture (0–200 cm) in dryland agriculture using data from drone-based multispectral remote sensing. Maize, millet, sorghum, and potatoes were selected for this study, with multispectral data, canopy leaf, and soil moisture content at various depths collected every 3 to 6 days. Vegetation indices highly correlated with crop canopy leaf moisture content (p < 0.01) and were identified using Pearson correlation analysis, leading to the development of linear and nonlinear regression models for predicting moisture content in canopy leaves and soil. The results show a significant linear correlation between the predicted and actual canopy leaf moisture levels for the four crops, according to the chosen vegetation indices. The use of canopy leaf moisture content to predict surface soil moisture (0–20 cm) demonstrated enhanced accuracy. The models designed for the top 20 cm of soil moisture successfully estimated deep soil moisture levels (up to 200 cm) for all four crops. The 20 cm range soil moisture model showed improvements over the 10 cm range model, with increases in Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Coefficient of Determination (R2), and Nash–Sutcliffe Efficiency Coefficient (NSE) by 0.4, 0.8, 0.73, and 0.34, respectively, in the corn area; 0.28, 0.69, 0.48, and 0.25 in the millet area; 0.4, 0.48, 0.22, and 0.52 in the sorghum area; and 1.14, 0.81, 0.73, and 0.56 in the potato area, all with an average Relative Error (RE) of less than 10% across the crops. Using drone-based multispectral technology, this study forecasts leaf water content via vegetation index analysis, facilitating swift and effective soil moisture inversion. This research introduces a novel method for monitoring and managing agricultural water resources, providing a scientific basis for precision farming and moisture variation monitoring in dryland areas.

A new automatic geo-electric self-potential imaging technique for diverse sustainable development scenarios

This study introduces a rapid and efficient inversion algorithm designed for the interpretation of self-potential responses originating from mineralized and ore sources and hydrothermal activity, specifically addressing spherical, vertical, and horizontal cylindrical structures. The algorithm leverages local wavenumber and correlation imaging techniques to enhance accuracy in modeling. The correlation factor (Cf value) is crucial in this approach, calculated as the correlation between the local wavenumber of the measured self-potential field and that of the computed field. The algorithm identifies the maximum correlation Cf value (CF-max) as indicative of the optimal true model parameters. To validate the proposed algorithm, it was applied to three theoretical examples—one with contamination from regional background and another with multiple sources with and without different types of noises (random Gaussian and white Gaussian noises). Additionally, the approach was tested on three distinct real field cases related to mining, ore investigation and hydrothermal activity in India, Germany and USA. Through a comprehensive analysis of results from theoretical and real-world scenarios, including comparisons with different available data and literature information, the study concludes that the method is effective, applicable to multiple sources, accurate, and does not necessitate prior knowledge of the source shape. This algorithm presents a promising advancement in the field of self-potential interpretation for mineral exploration and geothermal exploration.

Direct Synthesis of Polyimide Curly Nanofibrous Aerogels for High-Performance Thermal Insulation Under Extreme Temperature.

Maintaining human body temperature is one of the basic needs for living, which requires high-performance thermal insulation materials to prevent heat exchange with external environment. However, the most widely used fibrous thermal insulation materials always suffer from the heavy weight, weak mechanical property, and moderate capacity to suppress heat transfer, resulting in limited personal cold and thermal protection performance. Here, an ultralight, mechanically robust, and thermally insulating polyimide (PI) aerogel is directly synthesized via constructing 3D interlocked curly nanofibrous networks during electrospinning. Controlling the solution/water molecule interaction enables the rapid phase inversion of charged jets, while the multiple jets are ejected by regulating charge density of the fluids, thus synergistically allowing numerous curly nanofibers to interlock and cross-link with each other to form porous aerogel structure. The resulted PI aerogel integrates the ultralight property with density of 2.4mg cm-3, extreme temperature tolerance (mechanical robustness over -196 to 300 °C), and thermal insulation performance with ultralow thermal conductivity of 22.4mW m-1 K-1, providing an ideal candidate to keep human thermal comfort under extreme temperature. This work can provide a source of inspiration for the design and development of nanofibrous aerogels for various applications.

FLUID PROPERTIES OF COMPONENTS FOR DEVELOPMENT OF EUDRAGIT RS-BASED IN SITU-FORMING GEL AND MICROPARTICLE AND THEIR PHASE INVERSIONS

Solvent exchange-induced in situ-forming gel (isg) and in situ-forming microparticles (ism) have been currently applied as drug delivery approaches for periodontitis treatment. Doxycycline hyclate-loaded Eudragit RS-based isg and ism were prepared in this study. The basic properties such as density, pH, surface/interfacial tension of their solvents including dimethyl sulfoxide (DMSO), N-methyl pyrrolidone (NMP), and 2-pyrrolidone (PYR) in Eudragit RS solution and oils (olive oil and camellia oil) were assessed. This research also tested for apparent viscosity of solvent and drug-free and doxycycline hyclate-loaded ERS-based isg and ism, and their phase transformation rates. Density of PYR was higher than that of DMSO whereas these solvents exhibited the less wettability than oils on glass slide. All fabricated doxycycline hyclate-loaded Eudragit RS-based ism formulations showed good emulsion physical stability with no phase separation for 1 hour. The apparent viscosity of ism was significantly less than that of isg. DMSO-based isg and ism exhibited a significantly less viscosity than that of PYR-based formulations. Rapid phase inversion was evident after isg and ism exposure to agarose gel indicating fast solvent exchange. The loose barrier of camellia oil from its less viscosity facilitated a water diffusion inward Eudragit RS-based ism easier than using viscous olive oil. Solvent exchange-induced doxycycline hyclate-loaded Eudragit RS-based ism using DMSO as the solvent and camellia oil as the continuous phase exhibited various appropriate physicochemical characteristics for applying as drug delivery system for periodontitis treatment.

Fabrication of Low-Temperature Fast Gelation β-Cyclodextrin-Based Hydrogel-Loaded Medicine for Wound Dressings.

β-Cyclodextrin (β-CD) is often used as a drug carrier for biomedical materials due to its unique cavity structure. Herein, β-CD was modified by acryloyl chloride and further copolymerized with N-isopropylacrylamide (NIPAM) and acrylic acid (AA) to obtain PNIPAM-co-β-CD-AC. The results showed that the critical phase transition temperature of PNIPAM/β-CD-AC could be controlled at 19 °C, and the fast sol-gel phase transition was realized in 2-10 s. The hydrophobic drug carried in this hydrogel can constantly be released for more than 6 days at pH values (pH 5.5-8), and the duration may match the recovery of the wound. As a dressing hydrogel, its rapid gel formation and inversion as well as shear-thinning behavior prevent secondary wound damage. The β-CD-based hydrogel also has good biocompatibility and antioxidant properties, which provide a good potential choice for wound dressings, especially for exposed wounds in winter.

Multi-Parameter Health Assessment of Jujube Trees Based on Unmanned Aerial Vehicle Hyperspectral Remote Sensing

To address the current difficult problem of scientifically assessing the health status of date palm trees due to a single parameter for date palm health assessment, an imperfect index system, and low precision. In this paper, using jujube trees in 224 regiment of the 14th division of Xinjiang Production and Construction Corps “Kunyu city” as the research object, we carried out the inversion study of various physicochemical parameters of jujube trees (canopy chlorophyll content, leaf area index (LAI), tree height, canopy area) using the unmanned aerial vehicle (UAV) hyperspectral imagery of jujube trees during the period of fruit expansion, and put forward a model for assessing the health of jujube trees based on multiple physicochemical parameters. First, we calculated six spectral indices for inversion of chlorophyll content and four spectral index for inversion of LAI, analyzed the spectral index with high correlation with chlorophyll content and LAI of jujube trees canopy, and constructed the inversion models of chlorophyll content and LAI. Second, the Mask R-CNN model was used to achieve jujube trees’ canopy segmentation and area extraction, and the segmented canopy was matched with the Canopy Height Model (CHM) for jujube trees’ height extraction. Finally, based on the four physicochemical parameters of inversion, we construct four jujube trees’ health assessment models, namely, Partial Least Squares Regression Analysis (PLSR), Random Forest (RF), Support Vector Machines (SVM), and Decision Tree (DT). The results showed that the R2 of the PLSR tree health assessment model constructed based on the multi-physical and chemical parameters of chlorophyll content, LAI, tree height, and canopy area was 0.853, and the RMSE was 0.3. Compared with the jujube trees’ health assessment models constructed by RF, SVM, and DT, the R2 increased by 0.127, 0.386, and 0.165, and the RMSE decreased by 0.04, 0.175, and 0.063, respectively. This paper can achieve rapid and accurate inversion of multi-physical and chemical parameters of jujube trees with the help of UAV hyperspectral images, and the PLSR model constructed based on multi-physical and chemical parameters can accurately assess the health status of jujube trees and provide a reference for a scientific and reasonable assessment of jujube trees’ health.

Rapid source inversions of the 2023 SE Türkiye earthquakes with teleseismic and strong-motion data

We conducted rapid inversions of rupture process for the 2023 earthquake doublet occurred in SE Türkiye, the first with a magnitude of MW7.8 and the second with a magnitude of MW7.6, using teleseismic and strong-motion data. The teleseismic rupture models of the both events were obtained approximately 88 and 55 minutes after their occurrences, respectively. The rupture models indicated that the first event was an asymmetric bilateral event with ruptures mainly propagating to the northeast, while the second one was a unilateral event with ruptures propagating to the west. This information could be useful in locating the meizoseismal areas. Compared with teleseismic models, the strong-motion models showed relatively higher resolution. A noticeable difference was found for the MW7.6 earthquake, for which the strong-motion models shows a bilateral event, rather than a unilateral event, but the dominant rupture direction is still westward. Nevertheless, all strong-motion models are consistent with the teleseismic models in terms of magnitudes, durations, and dominant rupture directions. This suggests that both teleseismic and strong-motion data can be used for fast determination of major source characteristics. In contrast, the strong-motion data would be preferable in future emergency responses since they are recorded earlier and have a better resolution ability on the source ruptures.

Preparation of GO@DCN nanocomposite modified PVDF membranes for improved membrane performance and exploration of formation mechanism

Two-dimensional nanocomposite membranes (NCMs) have attracted increased attention, but the effects of nanomaterials on membranes and phase inversion mechanisms are still unclear. In this study, graphene oxide (GO) was combined with nitrogen defect-modified g-C3N4 (DCN) nanosheets by grinding to obtain GO@DCN, with subsequent doping into polyvinylidene fluoride (PVDF) by non-solvent induced phase separation (NIPS) to prepare a series of hydrophilic NCMs. The thermodynamic ternary phase diagram of NCMs is reported for the first time. Compared with pure PVDF, the phase inversion of NCMs was accelerated with the addition of DCN, GO and GO@DCN nanomaterials, which lead to sequential increment in porosity, providing channels for water molecules. Besides, the Density Function Theory(DFT) calculations indicated a strong interaction between PVDF and GO@DCN, which also contributes to the rapid phase inversion of the PVDF/GO@DCN. Furthermore, the extended Derjaguin-Landau-Verwey-Overbeek (XDLVO) theory demonstrates that the attraction between PVDF/GO@DCN2.0 and Bovine Serum Albumin (BSA) is halved compared to PVDF and BSA, indicating superior antifouling ability. At the same time, the negative zeta potential of NCMs rose, which enhanced the repulsion between NCMs and the negatively charged BSA contaminants in water. Thus, in contrast to PVDF, the permeate flux of PVDF/GO@DCN2.0 increased from 161.6 Lm−2h−1 to 387.8 Lm−2h−1 and the rejection of BSA from 80.1% to 91.6%, respectively. The flux recovery ratio (FRR) of PVDF/GO@DCN2.0 improved 30.4%, and irreversible fouling decreased 29.6%. After five cleaning cycles, the NCM still maintained excellent stability.

Facilely prepare high-performance loose nanofiltration membranes through regulation of polymer chain entanglements

The membrane forming process was fundamentally affected by the thermodynamics of polymer solution, the core of which involved the governance of polymer chain entanglements in the solution. In this work, a high-performance loose nanofiltration (LNF) membrane with high water permeance and high separation accuracy was facilely fabricated by the regulation of polymer chain entanglements through only changing CaCO3 nanoparticle (nano-CaCO3) size in the casting solution. The degree of polymer chain entanglements was characterized by the critical entanglement viscosity of the casting solution. The solution viscosity was markedly increased with the decrease of nano-CaCO3 size and then significantly reduced when nano-CaCO3 disappeared. While the mutual diffusion rate between the non-solvent (acidic water) and the solvent was increased to bring about rapid phase inversion with increasing the viscosity of the casting solution. The high solution viscosity and rapid phase inversion, which was a unconventional phenomenon, leaded to the formation of LNF membranes with outstanding performance. The LNF membranes obtained had a pore diameter of 1.4 nm (MWCO = 1625 Da) and a high pure water permeance of 98 L·m−2·h−1·bar−1, accompanying an excellent separation accuracy of small molecule dyes and NaCl mixtures (the rejection is 99.9% for eriochrome black T (EBT, Mw = 461 Da) and 4.5% for NaCl). This work provides general guidance for the structural regulation of high-performance separation membranes.