Teacher-student collaborated multiple instance learning for pan-cancer PDL1 expression prediction from histopathology slides.

Programmed cell death ligand 1 (PDL1), as an important biomarker, is quantified by immunohistochemistry (IHC) with few established histopathological patterns. Deep learning aids in histopathological assessment, yet heterogeneity and lacking spatially resolved annotations challenge precise analysis. Here, we present a weakly supervised learning approach using bulk RNA sequencing for PDL1 expression prediction from hematoxylin and eosin (H&E) slides. Our method extends the multiple instance learning paradigm with the teacher-student framework, which assigns dynamic pseudo-labels for intra-slide heterogeneity and retrieves unlabeled instances using temporal ensemble model distillation. The approach, evaluated on 12,299 slides across 20 solid tumor types, achieves a weighted average area under the curve of 0.83 on fresh-frozen and 0.74 on formalin-fixed specimens for 9 tumors with PDL1 as an established biomarker. Our method predicts PDL1 expression patterns, validated by IHC on 20 slides, offering insights into histologies relevant to PDL1. This demonstrates the potential of deep learning in identifying diverse histological patterns for molecular changes from H&E images.

TCN-attention-HAR: human activity recognition based on attention mechanism time convolutional network.

Wearable sensors are widely used in medical applications and human-computer interaction because of their portability and powerful privacy. Human activity identification based on sensor data plays a vital role in these fields. Therefore, it is important to improve the recognition performance of different types of actions. Aiming at the problems of insufficient time-varying feature extraction and gradient explosion caused by too many network layers, a time convolution network recognition model with attention mechanism (TCN-Attention-HAR) was proposed. The model effectively recognizes and emphasizes the key feature information. The ability of extracting temporal features from TCN (temporal convolution network) is improved by using the appropriate size of the receiver domain. In addition, attention mechanisms are used to assign higher weights to important information, enabling models to learn and identify human activities more effectively. The performance of the Open Data Set (WISDM, PAMAP2 and USC-HAD) is improved by 1.13%, 1.83% and 0.51%, respectively, compared with other advanced models, these results clearly show that the network model presented in this paper has excellent recognition performance. In the knowledge distillation experiment, the parameters of student model are only about 0.1% of those of teacher model, and the accuracy of the model has been greatly improved, and in the WISDM data set, compared with the teacher's model, the accuracy is 0.14% higher.

Hypercrosslinked Hydrogel Composite Membranes Targeted for Removal of Volatile Organic Compounds via Selective Solution-Diffusion in Membrane Distillation.

Membrane distillation (MD) has attracted considerable interest in hypersaline wastewater treatment. However, its practicability is severely impeded by the ineffective interception of volatile organic compounds (VOCs), which seriously affects the product water quality. Herein, a hypercrosslinked alginate (Alg)/aluminum (Al) hydrogel composite membrane is facilely fabricated via Alg pregel formation and ionic crosslinking for efficient VOC interception. The obtained MD membrane shows a sufficient phenol rejection of 99.52% at the phenol concentration of 100 ppm, which is the highest rejection among the reported MD membranes. Moreover, the hydrogel composite membrane maintains a high phenol interception (>99%), regardless of the feed temperature, initial phenol concentration, and operating time. Diffusion experiments and molecular dynamics simulation verify that the selective diffusion is the dominant mechanism for VOCs-water separation. Phenol experiences a higher energy barrier to pass through the dense hydrogel layer compared to water molecules as the stronger interaction between phenol-Alg compared with water-Alg. Benefited from the dense and hydratable Alg/Al hydrogel layer, the composite membrane also exhibits robust resistance to wetting and fouling during long-term operation. The superior VOCs removal efficiency and excellent durability endow the hydrogel composite membrane with a promising application for treating complex wastewater containing both volatile and nonvolatile contaminants.

Advanced oxidation process/coagulation coupled with membrane distillation (AOP/Coag-MD) for efficient ammonia recovery: Elucidating biofouling control performance and mechanism.

The inadequate understanding of the biofouling formation mechanism and the absence of effective control have inhibited the commercial application of membrane distillation (MD). In this study, an advanced oxidation process (AOP)/coagulation-coupled (Coag) membrane distillation system was proposed and exhibited the potential for MD ammonia recovery (recovery rate: 94.1%). Extracellular polymeric substances (EPS) and soluble microbial products (SMP) components such as humic acid and tryptophan-like proteins were disrupted and degraded in the digestate. The curtailment and sterilizing efficiency of AOP on biofilm growth was also verified by optical coherence tomography (OCT) in situ real-time monitoring and confocal laser scanning microscopy (CLSM). Peroxymonosulfate (PMS) was activated to generate sulfate (SO4•-) and hydroxyl radicals (HO•), which altered the microbial community. After oxidative treatment, 16 S rRNA sequencing indicated that the dominant phylum of the microbial community evolved into Firmicutes. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis demonstrated that free radicals produced by PMS could disrupt cells' signaling molecules and interactions. In conjunction with these analyses, the mechanisms of response to free radical attack by Gram-negative bacteria, Gram-positive bacteria, and fungi were revealed. This research provided new insights into the field of membrane fouling control for membrane technology resource recovery processes, broadening the impact of AOP applications on microbiological response and fate in the environment.

Membrane distillation of wastewater: comparison of model and real organics.

Fouling behaviour in membrane distillation (MD) processes plays a crucial role in determining their widespread acceptability. Most studies have primarily focused on model organic foulants, such as humic acid (HA) and sodium alginate (SA). This study investigates the fouling of a polytetrafluoroethylene membrane in a direct contact MD (DCMD) using model organics (i.e., HA and SA) and real wastewater. The results indicated that the flux decline (5-60%) was only observed during the initial phase of the operation with model organic foulants. In contrast, real wastewater caused a gradual decline in flux throughout the experiment in both the concentrate (40%) and continuous (90%) modes. The study also found significant differences in the fouling layer morphology, composition, and hydrophobicity between the model organic foulants and real wastewater. Fourier transform infrared spectroscopy findings demonstrated that the fouling layer formed by real wastewater varied significantly from model organics, which primarily comprised of protein-like and polysaccharide-like functional groups. Finally, liquid chromatography-organic carbon detection revealed that the fouling layer of the MD membrane with real wastewater was composed of 40.7% hydrophobic and 59.3% hydrophilic organics. This study suggests that model organics may not accurately reflect real wastewater fouling.

The Flow Rate of the Condenser Cooling Water in the Distillation Process Increases the Quality and Quantity of Patchouli Oil.

Indonesia is an important essential oil-exporting country globally, where 40 types of essential oils have been traded on the international market and are products of Indonesia. However, the quality and quantity of patchouli oil produced in Indonesia are still low. Most essential oil processing units use simple or traditional technology and generally have limited production capacity. This study aimed to obtain the optimum water flow rate in a condenser system for patchouli oil production in Maluku, Indonesia. Patchouli oil extraction from fresh patchouli leaves and twigs was carried out by increasing the condenser water discharge rate. Patchouli oil extraction with a condenser cooling water discharge treatment of 1.74 L/min and drying time for 5 days produced the highest patchouli oil yield of 1.4%. The greater the condenser water discharge rate, the better the yield and accumulation of patchouli oil recovery obtained. In addition, based on the results of the analysis of the composition of patchouli oil compounds with GCMS, it can be seen that 13 compounds can be detected in patchouli oil. The three main components of patchouli oil in all condenser cooling water treatments were alpha-guaiene, delta-guaiene, and patchouli alcohol. Considering the results of all parameters mentioned above, the treatment of the condenser cooling water discharge of 1.74 L/min and drying time for 5 days increases the quality and quantity of patchouli oil.

Importance-aware adaptive dataset distillation.

Herein, we propose a novel dataset distillation method for constructing small informative datasets that preserve the information of the large original datasets. The development of deep learning models is enabled by the availability of large-scale datasets. Despite unprecedented success, large-scale datasets considerably increase the storage and transmission costs, resulting in a cumbersome model training process. Moreover, using raw data for training raises privacy and copyright concerns. To address these issues, a new task named dataset distillation has been introduced, aiming to synthesize a compact dataset that retains the essential information from the large original dataset. State-of-the-art (SOTA) dataset distillation methods have been proposed by matching gradients or network parameters obtained during training on real and synthetic datasets. The contribution of different network parameters to the distillation process varies, and uniformly treating them leads to degraded distillation performance. Based on this observation, we propose an importance-aware adaptive dataset distillation (IADD) method that can improve distillation performance by automatically assigning importance weights to different network parameters during distillation, thereby synthesizing more robust distilled datasets. IADD demonstrates superior performance over other SOTA dataset distillation methods based on parameter matching on multiple benchmark datasets and outperforms them in terms of cross-architecture generalization. In addition, the analysis of self-adaptive weights demonstrates the effectiveness of IADD. Furthermore, the effectiveness of IADD is validated in a real-world medical application such as COVID-19 detection.

Molecular insights into the adsorption and penetration of oil droplets on hydrophobic membrane in membrane distillation.

Membrane fouling induced by oily substances significantly constrains membrane distillation performance in treating hypersaline oily wastewater. Overcoming this challenge necessitates a heightened fundamental understanding of the oil fouling phenomenon. Herein, the adsorption and penetration mechanism of oil droplets on hydrophobic membranes in membrane distillation process was investigated at the molecular level. Our results demonstrated that the adsorption and penetration of oil droplets were divided into four stages, including the free stage, contact stage, spreading stage, and equilibrium stage. Due to the extensive non-polar surface distribution of the polytetrafluoroethylene (PTFE) membrane (comprising 95.41 %), the interaction between oil molecules and PTFE was primarily governed by van der Waals interaction. Continuous oil droplet membrane fouling model revealed that the new oil droplet molecules preferred to penetrate into membrane pores where oil droplets already existed. The penetration of resin (a component of medium-quality oil droplets) onto PTFE membrane pores required the "pre-paving" of light crude oil. Finally, the ΔE quantitative structure-activity relationships (QSAR) models were developed to evaluate the penetration mechanism of pollutant molecules on the PTFE membrane. This research provides new insights for improving sustainable membrane distillation technologies in treating saline oily wastewater.

EPANet-KD: Efficient progressive attention network for fine-grained provincial village classification via knowledge distillation.

OBJECTIVE: Fine-grained classification of historical traditional villages plays a crucial role in guiding the future development and construction of urban and rural areas. This study aims to propose a new dataset for fine-grained classification of traditional villages and to propose an efficient progressive attention network for the problem of low accuracy and efficiency of fine-grained traditional historical village classification. METHODS AND RESULTS: Firstly, in order to further study the long-standing problem of fine-grained classification of traditional villages, a new fine-grained classification dataset of traditional villages containing 4,400 images, referred to as PVCD, is proposed by crawling and hand-arranging. Secondly, a new Progressive Attention Module, abbreviated as PAM, is also proposed. PAM engages in attentional modeling of prominent spatial features within the spatial dimension, subsequently applying attentional modeling to channel features beneath the identified salient spatial features. This process involves salient spatial feature attention modeling of prominent channel features within the dimension to extract discriminative information for fine-grained classification, thereby enhancing the performance of classifying traditional villages with precision. Finally, a new knowledge distillation strategy of softened alignment distillation, or SAD for short, is proposed, which simply and efficiently transfers the knowledge of softened category probability distributions through. Notably, based on the above proposed PAM, the lightweight EPANet-Student and the heavyweight EPANet-Teacher are proposed. In addition, the heavyweight EPANet-Teacher transfers the knowledge of fine-grained categorization of traditional villages to the lightweight EPANet-Student through the proposed SAD, abbreviated as EPANet-KD. The experimental results show that the proposed EPANet-Teacher achieves state-of-the-art performance with an accuracy of 67.27%, and the proposed EPANet-KD achieves comparable performance to the proposed EPANet-Teacher with 3.32M parameters and 0.42G computation. CONCLUSION: The proposed EPANet-KD maintains a good balance of accuracy and efficiency in the fine-grained classification of traditional villages, considerably promoting the research on the fine-grained classification of traditional villages. In addition, it facilitates the digital preservation and development of traditional villages. All datasets, codes and benchmarking results are publicly available for the promotion of this research area. https://github.com/Jack13026212687/EPANet-KD.

Engineered eco-friendly composite membranes with superhydrophobic/hydrophilic dual-layer for DCMD system.

Considerable advancements have been made in the development of hydrophobic membranes for membrane distillation (MD). Nonetheless, the environmentally responsible disposal of these membranes poses a critical concern due to their synthetic composition. Herein, an eco-friendly dual-layered biopolymer-based membrane was fabricated for water desalination. The membrane was electrospun from two bio-polymeric layers. The top hydrophobic layer comprises polycaprolactone (PCL) and the bottom hydrophilic layer from cellulose acetate (CA). Additionally, silica nanoparticles (SiO2 NPs) were electrosprayed onto the top layer of the dual-layered PCL/CA membrane to enhance the hydrophobicity. The desalination performance of the modified PCL-SiO2/CA membrane was compared with the unmodified PCL/CA membrane using a direct contact membrane distillation (DCMD) unit. Results revealed that silica remarkably improves membrane hydrophobicity. The modified PCL-SiO2/CA membrane demonstrated a significant increase in water contact angle of 152.4° compared to 119° for the unmodified membrane. In addition, PCL-SiO2/CA membrane has a smaller average pore size of 0.23 ± 0.16 µm and an exceptional liquid entry pressure of water (LEPw), which is 3.8 times higher than that of PCL/CA membrane. Moreover, PCL-SiO2/CA membrane achieved a durable permeate flux of 15.6 kg/m2.h, while PCL/CA membrane showed unstable permeate flux decreasing approximately from 25 to 12 kg/m2.h over the DCMD test time. Furthermore, the modified PCL-SiO2/CA membrane achieved a high salt rejection value of 99.97% compared to a low value of 86.2% for the PCL/CA membrane after 24 h continuous DCMD operation. In conclusion, the proposed modified PCL-SiO2/CA dual-layer biopolymeric-based membrane has considerable potential to be used as an environmentally friendly membrane for the MD process.

Distillation of essential oils: An innovative technological approach focused on productivity, quality and sustainability.

Essential oil (EO) distillation units, which are commonly installed on farms, have difficultly accessing knowledge centers. The apparent simplicity of the process hides unwanted losses and deviations that go undetected and, therefore, not corrected. This article proposes improvements to the process that are based on "4.0" technologies in order to detect and correct two important deficiencies, with an impact on the yield, quality and environmental performance. The first deficiency comprises the steam channels that are formed through green mass (channeling), are well known and are considered normal by EO producers. Without detection and correction, this negatively affects the extraction results. The second is the lack of technology that is able to automatically determine the extraction endpoint. Smart sensing, control, self-configuration and the dynamic determination of improved process parameters make up a set of actions undertaken by a smart extraction plant (50-liter capacity). Nineteen experiments using lemongrass (Cymbopogon citratus) exhibited remarkable 24% and 10% improvements in the yield and quality, respectively. Energy consumption and a more than 50% reduction in the processing complete the set of results achieved. In addition to manufacturing costs and the utilization of capacity, better sustainability indicators are positive consequences of this technological updating.

Variation of the essential oil components of Citrus aurantium leaves upon using different distillation techniques and evaluation of their antioxidant, antidiabetic, and neuroprotective effect against Alzheimer's disease.

Citrus fruit essential oil is considered one of the widely studied essential oils while its leaves attract less attention although being rich in nearly the same composition as the peel and flowers. The leaves of bitter orange or sour orange (Citrus aurantium L.) were extracted using three different techniques namely; hydrodistillation (HD), steam distillation (SD), and microwave-assisted distillation (MV) to compare their chemical composition. The three essential oil samples were analyzed through GC/FID and GC/MS analyses. The samples were tested in vitro using different antioxidant techniques (DPPH, ABTS, CUPRAC, FRAP, PBD, and MCA), neuroprotective enzyme inhibitory activities (acetylcholine and butyl choline enzymes), and antidiabetic activities (α-amylase and α-glucosidase). The results showed that thirty-five volatile ingredients were detected and quantified. Monoterpenes represented the most abundant class in the three essential oils followed by sesquiterpenes. C. aurantium essential oil carried potential antioxidant activity where SD exhibited the highest antioxidant activity, with values arranged in the following order: FRAP (200.43 mg TE/g), CUPRAC (138.69 mg TE/g), ABTS (129.49 mg TE/g), and DPPH (51.67 mg TE/g). SD essential oil also presented the most potent α-amylase (0.32) inhibition while the MV essential oil showed the highest α-glucosidase inhibition (2.73 mmol ACAE/g), followed by HD (2.53 mmol ACAE/g), and SD (2.46 mmol ACAE/g). The SD essential oil exhibited the highest BChE and AChE inhibitory activities (3.73 and 2.06 mg GALAE/g), respectively). Thus, bitter orange essential oil can act as a potential source of potent antioxidant, antidiabetic, and neuroprotective activities for future drug leads.

Clean-In-Place (CIP) wastewater management using nanofiltration (NF)-forward osmosis (FO)-direct contact membrane distillation (DCMD): Effects of draw salt.

A substantial amount of water is being used during Clean-in-Place (CIP) operation, and is transformed into wastewater that can cause eutrophication to the nearby ecosystem. The present study proposed the Nanofiltration (NF) - Forward Osmosis (FO) - Direct Contact Membrane Distillation (DCMD) to recover the cleaning agents and reclaim freshwater from the model CIP wastewater. NF steps were suggested as prefiltration steps to remove organic compounds from the CIP wastewater. NF steps reduced the lactose and protein contents by 100 % and 95.6 %, respectively. The permeates from NF steps were further managed by the integrated FO-DCMD system. Several draw salts such as NaCl, KCl, MgCl2, and CaCl2 were compared to investigate the influence on FO and DCMD performance. It was found that monovalent salts (NaCl and KCl) outperformed the divalent salts (MgCl2 and CaCl2) in terms of water flux for both FO and DCMD. This can be attributed to the lower viscosity and higher mass transfer coefficient. In addition, the replenishment costs of each salt were evaluated since salts loss occurred during FO and DCMD operation. The cost evaluation revealed that NaCl is most the cheapest salts per reclaimed water. All of this observation indicates that NaCl is preferred in terms of water flux and replenishment cost. The NF permeate kept concentrated using the integrated FO-DCMD or single FO with 2 M of NaCl. Compared to a single FO that showed a consistent decline in draw solution concentration, FO-DCMD could maintain the concentration of the draw solution. Despite the constant concentration, flux decline of FO was observed due to fouling formation caused by the high-temperature operation. However, the FO-DCMD could accomplish the recovery of pure water. Finally, the cleaning agents recovered by the NF-FO-DCMD showed the cleaning efficacy comparable to the fresh NaOH. These results suggest the potential of the proposed system to manage the CIP wastewater.

A novel Multi-Level Refined (MLR) knowledge graph design and chatbot system for healthcare applications.

Imagine having a knowledge graph that can extract medical health knowledge related to patient diagnosis solutions and treatments from thousands of research papers, distilled using machine learning techniques in healthcare applications. Medical doctors can quickly determine treatments and medications for urgent patients, while researchers can discover innovative treatments for existing and unknown diseases. This would be incredible! Our approach serves as an all-in-one solution, enabling users to employ a unified design methodology for creating their own knowledge graphs. Our rigorous validation process involves multiple stages of refinement, ensuring that the resulting answers are of the utmost professionalism and solidity, surpassing the capabilities of other solutions. However, building a high-quality knowledge graph from scratch, with complete triplets consisting of subject entities, relations, and object entities, is a complex and important task that requires a systematic approach. To address this, we have developed a comprehensive design flow for knowledge graph development and a high-quality entities database. We also developed knowledge distillation schemes that allow you to input a keyword (entity) and display all related entities and relations. Our proprietary methodology, multiple levels refinement (MLR), is a novel approach to constructing knowledge graphs and refining entities level-by-level. This ensures the generation of high-quality triplets and a readable knowledge graph through keyword searching. We have generated multiple knowledge graphs and developed a scheme to find the corresponding inputs and outputs of entity linking. Entities with multiple inputs and outputs are referred to as joints, and we have created a joint-version knowledge graph based on this. Additionally, we developed an interactive knowledge graph, providing a user-friendly environment for medical professionals to explore entities related to existing or unknown treatments/diseases. Finally, we have advanced knowledge distillation techniques.

Separation techniques for manufacturing fruit spirits: From traditional distillation to advanced pervaporation process.

Separation process is one of the key processes in the production of fruit spirits, including the traditional distillation method and the new pervaporation membrane method. The separation process significantly determines the constituents and proportions of compounds in the fruit spirit, which has a significant impact on the spirit quality and consumer acceptance. Therefore, it is important and complex to reveal the changing rules of chemical substances and the principles behind them during the separation process of fruit spirits. This review summarized the traditional separation methods commonly used in fruit spirits, covering the types, principles, and corresponding equipment of distillation methods, focused on the enrichment or removal of aroma compounds and harmful factors in fruit spirits by distillation methods, and tried to explain the mechanism behind it. It also proposed a new separation technology for the production of fruit spirits, pervaporation membrane technology, summarized its working principle, operation, working parameters, and application in the production of fruit spirits, and outlined the impact of the separation method on the production of fruit spirits based on existing research, focusing on the separation of flavor compounds, sensory qualities, and hazard factors in fruit spirits, along with a preliminary comparison with distillation. Finally, according to the current researches of the separation methods and the development requirement of the separation process of fruit spirits, the prospect of corresponding research is put forward, in order to propose new ideas and development directions for the research in this field.

Unraveling the contributing factors of stale odor in Longjing tea through a sensomics approach.

Longjing tea is renowned for its fresh aroma and high value. However, during storage, the emergence of an off-flavor known as "stale odor" can significantly impact the flavor quality and economic benefits of Longjing tea. Yet, the specific volatiles responsible for this stale odor in Longjing tea remain unknown. In this study, Longjing tea samples with varying degrees of stale odor intensity were analyzed using simultaneous distillation extraction coupled with gas chromatography-mass spectrometry (SDE-GC-MS). Through odor activity value (OAV) and fractional omission testing, hexanoic acid and trans-2-nonenal were identified as the primary contributors to the stale odor. Moreover, the concentration of hexanoic acid was found to be valuable in predicting the intensity of the stale odor in Longjing tea. The oxidative degradation of linoleic acid was proved as the generation pathway of stale odor in Longjing tea. These findings provide essential theoretical principles for Longjing tea production and preservation.

Solar-driven surface-heating membrane distillation using Ti3C2Tx MXene-coated spacers.

The emergence of two-dimensional (2D) MXenes as efficient light-to-heat conversion materials offers significant potential for solar-based desalination, particularly in photothermal interfacial evaporation, enabling cost-effective solar-powered membrane distillation (MD). This study investigates solar-powered MD afforded by a photothermally functionalized spacer, which is built by spray-coating Ti3C2Tx MXene sheets on metallic spacers. 2D Ti3C2Tx MXene gives an ultrahigh photothermal conversion efficiency; thereby, by Ti3C2Tx MXene-coated metallic spacer, this rationally designed spacer allows for a localized photothermal conversion and interfacial feed heating effect on the membrane surface, especially for MD operation. As a feed spacer and a photothermal element, Ti3C2Tx MXene-coated metallic spacer exhibited stable enhanced water flux of up to 0.36 kg·m-2h-1 under one sun illumination for a feed salinity of 35 g·L-1, corresponding energy conversion efficiency of 28.3 %. Overall, the developed photothermal Ti3C2Tx MXene-coated spacers displayed great potential in enhancing the performance, scalability, and feasibility of solar-driven MD process, paving the way for further development of photothermal elements that can be implemented in solar MD applications.

Novel coiled hollow fiber module for high-performance membrane distillation.

Membrane distillation (MD) scale-up is challenged by ineffective heat recovery and the temperature polarization effect. Direct contact membrane distillation (DCMD) modules suffer high thermal conduction losses due to feed flow direction along the length of the membrane, resulting in low thermal efficiency. We propose a novel module design named coiled hollow fiber (CHF) to decouple the flow direction from the membrane surface in hollow fiber (HF) DCMD. Experimental and computational analyses were employed to compare the performance of CHF and the conventional design. The CHF module design successfully mitigates the TP effect in HF DCMD, increasing the flux by 148 % and 163 % in cross-flow and localized heating (LH) modes, respectively. Moreover, CHF operated in LH mode exhibits the lowest energy consumption of all configurations (81 % decrease) compared to the conventional design. This novel module design represents a new pathway for efficient and highly performing DCMD module.

Engineering Multinanochannel Polymer-Intercalated Graphene Oxide Membrane for Strict Volatile Sieving in Membrane Distillation.

Graphene oxide (GO) membranes enabled by subnanosized diffusion channels are promising to separate small species in membrane distillation (MD). However, the challenge of effectively excluding small volatiles in MD persists due to the severe swelling and subsequent increase in GO interlamination spacing upon direct contact with the hot feed. To address this issue, we implemented a design in which a polymer is confined between the GO interlaminations, creating predominantly 2D nanochannels centered around 0.57 nm with an average membrane pore size of 0.30 nm. Compared to the virginal GO membrane, the polymer-intercalated GO membrane exhibits superior antiswelling performance, particularly at a high feed temperature of 60 °C. Remarkably, the modified membrane exhibited a high flux of approximately 52 L m-2 h-1 and rejection rates of about 100% for small ions and 98% for volatile phenol, with a temperature difference of 40 °C. Molecular dynamics simulations suggest that the sieving mechanisms for ions and volatiles are facilitated by the narrowed nanochannels within the polymer network situated between the 2D nanochannels of GO interlaminations. Concurrently, the unrestricted permeation of water molecules through the multinanochannel GO membrane encourages high-flux desalination of complex hypersaline wastewater.

Performance investigation of poly(vinylidene fluoride-cohexafluoropropylene) membranes containing SiO2 nanoparticles in a newly designed single vacuum membrane distillation system.

The current study focuses on the development of a superhydrophobic poly(vinylidene fluoride-cohexafluoropropylene) nanocomposite membrane suitable for vacuum membrane distillation by incorporating SiO2 nanoparticles. At loading hydrophobic nano-SiO2 particle concentration (0.50-1.50 wt.%), the developed nanocomposite membranes are optimized in terms of vacuum membrane distillation performance. The influence of temperature, vacuum pressure, and feed water flow is studied for desalinating high-salinity brine. The results show that the developed vacuum distillation membrane is capable of 95% salt rejection during the treatment of a highly saline feed (65,000 ppm) at fixed flow rates of 120 L/h saline feed and different operating conditions consisting of feed inlet temperatures ranging from 40°C to 70°C and distillate inlet temperatures of 7-15°C. The vacuum membrane distillation process achieves 0.38-1.66% water recovery with increasing concentration factor, meaning that recovery is increased, and shows a specific electrical energy consumption of 5.16-23.90 kWh/m3 for product water. Overall, the newly designed membrane demonstrates suitability for a vacuum membrane distillation system. PRACTITIONER POINTS: Desalinate high-salinity brine (TDS > 35,000 ppm) using a vacuum membrane distillation system. A hydrophobic PVDF-HFP/SiO2 nanocomposite membrane development for vacuum membrane distillation. A newly designed single vacuum membrane distillation system for RO brine treatment.