Intelligence analysis of membrane distillation via machine learning models for pharmaceutical separation.

This study investigates simulation of pharmaceutical separation via membrane distillation process by computational simulation and machine learning modeling strategy. The efficacy of three regression models, i.e., Multi-layer Perceptron (MLP), Gamma Regression, and Support Vector Regression (SVR) in predicting the solute concentration, C(mol/m³), was evaluated. The hyper-parameters were optimized by fine-tuning the models using the Red Deer Algorithm (RDA). Computational analyses were carried out for removal of pharmaceuticals from solution by membrane distillation in continuous mode. Mass transfer and machine learning models were implemented focusing on concentration of solute in the feed section of membrane. Results indicate that the Multi-layer Perceptron model achieved great accuracy with an R2 of 0.9955, an MAE of 0.0084, and an RMSE of 0.0148, effectively capturing complex nonlinear relationships in the data. Gamma Regression also performed acceptably, with fitting R2 of 0.9214, showing its suitability for positively skewed data. The Support Vector Regression model, while capturing the general trend, showed the lowest performance with an R2 of 0.8710. These findings suggest that the Multi-layer Perceptron is the most accurate model for this dataset, followed by Gamma Regression and Support Vector Regression. This underscores the importance of careful model selection and optimization in regression analysis in combination with computational simulation of membrane processes.

Learning lightweight tea detector with reconstructed feature and dual distillation.

Currently, image recognition based on deep neural networks has become the mainstream direction of research; therefore, significant progress has been made in its application in the field of tea detection. Many deep models exhibit high recognition rates in tea leaves detection. However, deploying these models directly on tea-picking equipment in natural environments is impractical; the extremely high parameters and computational complexity of these models make it challenging to perform real-time tea leaves detection. Meanwhile, lightweight models struggle to achieve competitive detection accuracy; therefore, this paper addresses the issue of computational resource constraints in remote mountain areas and proposes Reconstructed Feature and Dual Distillation (RFDD) to enhance the detection capability of lightweight models for tea leaves. In our method, the Reconstructed Feature selectively masks the feature of the student model based on the spatial attention map of the teacher model; it utilizes a generation block to force the student model to generate the teacher's full feature. The Dual Distillation comprises Decoupled Distillation and Global Distillation. Decoupled Distillation divides the reconstructed feature into foreground and background features based on the Ground-Truth. This compels the student model to allocate different attention to foreground and background, focusing on their critical pixels and channels. However, Decoupled Distillation leads to the loss of relation knowledge between foreground and background pixels. Therefore, we further perform Global Distillation to extract this lost knowledge. Since RFDD only requires loss calculation on feature map, it can be easily applied to various detectors. We conducted experiments on detectors with different frameworks, using a tea dataset collected at the Huangshan Houkui Tea Plantation. The experimental results indicate that, under the guidance of RFDD, the student detectors have achieved performance improvements to varying degrees. For instance, a one-stage detector like RetinaNet (ResNet-50) experienced a 3.14% increase in Average Precision (AP) after RFDD guidance. Similarly, a two-stage model like Faster RCNN (ResNet-50) obtained a 3.53% improvement in AP. This offers promising prospects for lightweight models to efficiently perform real-time tea leaves detection tasks.

A new approach on the management of landfill leachate reverse osmosis concentrate: Solar distillation coupled with struvite recovery and biological treatment.

The management of reverse osmosis (RO) concentrate remains a challenging task for operators of Landfill Leachates Treatment Plants. In this article we suggest an integrated treatment scheme for RO concentrate that combines solar distillation, struvite precipitation to reduce ammonia content of the distillate and biological treatment of the supernatant either with mixed cultures of bacteria or with microalgae. Experiments in a pilot-scale solar still, equipped with underfloor heating system, showed that the production rate of the distillate ranged up to 3.17 L/d m2. The distillate was characterized by elevated average concentrations of ammonium nitrogen; 2028 mg/L and 1358 mg/L in the two experiments conducted, respectively. A decreasing trend on concentrations of NH4+-N was noticed during these experiments, while the opposite was observed for COD. Struvite recovery experiments showed that the optimum Mg:NH4:PO3 ratio was that of 2:1:5.8. Under these conditions, the NH4+-N removal reached 88%. Further treatment of the process supernatant into a 4-L hybrid sequencing batch reactor with biocarriers and activated sludge achieved NH4+-N removal higher than 98% in Phases C and D, where 450 and 600 mL of supernatant were added, respectively. Similar removal was also observed in a 2-L bioreactor with microalgae Chlorella sorokiniana when 150 mL of struvite supernatant were added (Phase B) while further increase of the amount of added supernatant to 200 mL resulted to a sharp stop of NH4+-N consumption (Phase C). Calculations for a landfill serving 20,000 inhabitants and a daily RO concentrate production of 6 m3/d showed that the required area for the construction of the solar still was 1893 m2 and the volumes of the hybrid and the microalgae reactor were 54 m3 and 60 m3, respectively. The recovered solid material of struvite process, after characterization for heavy metals and organic micropollutants, could be reused to the fertilizers industry.

Modeling and validation of purification of pharmaceutical compounds via hybrid processing of vacuum membrane distillation.

This study provides an in-depth examination of forecasting the concentration of pharmaceutical compounds utilizing the input features (coordinates) r and z through a range of machine learning models. Purification of pharmaceuticals via vacuum membrane distillation process was carried out and the model was developed for prediction of separation efficiency based on hybrid approach. Dataset was collected from mass transfer analysis of process to obtain concentration distribution in the feed side of membrane distillation and used it for machine learning models. The dataset has undergone preprocessing, which includes outlier detection using the Isolation Forest algorithm. Three regression models were used including polynomial regression (PR), k-nearest neighbors (KNN), and Tweedie regression (TWR). These models were further enhanced using the Bagging ensemble technique to improve prediction accuracy and reduce variance. Hyper-parameter optimization was conducted using the Multi-Verse Optimizer algorithm, which draws inspiration from cosmological concepts. The Bagging-KNN model had the highest predictive accuracy (R2 = 0.99923) on the test set, indicating exceptional precision. The Bagging-PR model displayed satisfactory performance, with a slightly reduced level of accuracy. In contrast, the Bagging-TWR model showcased the least accuracy among the three models. This research illustrates the effectiveness of incorporating bagging and advanced optimization methods for precise and dependable predictive modeling in complex datasets.

Evaluation of lavender essential oils and by-products using microwave hydrodistillation and conventional hydrodistillation.

This study investigated the impact of two extraction methods, traditional hydrodistillation (TDH) and microwave-assisted hydrodistillation (MAH), on the essential oil yield and chemical profile of Lavandula angustifolia L., as well as the bioactive potential of the resulting wastewater. Essential oil composition was analyzed via GC-MS, revealing similar qualitative and quantitative profiles for both methods, with α-terpinolene and (-)borneol as major constituents. Wastewater analysis via LC-MS/MS and spectrophotometric assays demonstrated the presence of significant total phenolic content (3.29-1.78 mg GAE/g) and 32 individual phenolics (463.1 µg/kg for TDH; 479.33 µg/kg for MAH). These findings suggest that both essential oil and wastewater obtained by either method possess considerable bioactive potential, with the MAH method potentially offering advantages over TDH for essential oil extraction. Further exploration of wastewater applications in various industrial sectors is warranted.

Decoding Long-Chain Fatty Acid Ethyl Esters during the Distillation of Strong Aroma-Type Baijiu and Exploring the Adsorption Mechanism with Magnetic Nanoparticles.

Simultaneous detection of the dynamic distribution of long-chain fatty acid ethyl esters (LCFAEEs) during Baijiu distillation is crucial for optimizing its flavor and health attributes. In this study, we synthesized a simple, cost-effective Fe3O4@NH2 adsorbent to simultaneously extract eight LCFAEEs from Baijiu. Through density functional theory and adsorption experiments, we elucidated 1,6-hexanediamine as a surface modifier, with the -NH2 groups providing adsorption sites for the LCFAEEs via hydrogen-bonding interactions and van der Waals forces. Additionally, we established the magnetic solid-phase extraction-GC-MS extraction technique combined with stable isotope dilution analysis to analyze LCFAEEs. This method revealed the dynamic distribution patterns of LCFAEEs during strong aroma-type Baijiu (SAB) distillation. We observed that the concentrations of the eight LCFAEEs gradually decreased with prolonged distillation and were significantly correlated with ethanol concentration. To ensure optimal flavor and clarity in SAB, it is recommended to select the heart-stage base Baijiu with an alcohol content of 58%-63%.

Lightweight CNN combined with knowledge distillation for the accurate determination of black tea fermentation degree.

Black tea is the second most common type of tea in China. Fermentation is one of the most critical processes in its production, and it affects the quality of the finished product, whether it is insufficient or excessive. At present, the determination of black tea fermentation degree completely relies on artificial experience. It leads to inconsistent quality of black tea. To solve this problem, we use machine vision technology to distinguish the degree of fermentation of black tea based on images, this paper proposes a lightweight convolutional neural network (CNN) combined with knowledge distillation to discriminate the degree of fermentation of black tea. After comparing 12 kinds of CNN models, taking into account the size of the model and the performance of discrimination, as well as the selection principle of teacher models, Shufflenet_v2_x1.0 is selected as the student model, and Efficientnet_v2 is selected as the teacher model. Then, CrossEntropy Loss is replaced by Focal Loss. Finally, for Distillation Loss ratios of 0.6, 0.7, 0.8, 0.9, Soft Target Knowledge Distillation (ST), Masked Generative Distillation (MGD), Similarity-Preserving Knowledge Distillation (SPKD), and Attention Transfer (AT) four knowledge distillation methods are tested for their performance in distilling knowledge from the Shufflenet_v2_x1.0 model. The results show that the model discrimination performance after distillation is the best when the Distillation Loss ratio is 0.8 and the MGD method is used. This setup effectively improves the discrimination performance without increasing the number of parameters and computation volume. The model's P, R and F1 values reach 0.9208, 0.9190 and 0.9192, respectively. It achieves precise discrimination of the fermentation degree of black tea. This meets the requirements of objective black tea fermentation judgment and provides technical support for the intelligent processing of black tea.

Cross-modal knowledge distillation for continuous sign language recognition.

Continuous Sign Language Recognition (CSLR) is a task which converts a sign language video into a gloss sequence. The existing deep learning based sign language recognition methods usually rely on large-scale training data and rich supervised information. However, current sign language datasets are limited, and they are only annotated at sentence-level rather than frame-level. Inadequate supervision of sign language data poses a serious challenge for sign language recognition, which may result in insufficient training of sign language recognition models. To address above problems, we propose a cross-modal knowledge distillation method for continuous sign language recognition, which contains two teacher models and one student model. One of the teacher models is the Sign2Text dialogue teacher model, which takes a sign language video and a dialogue sentence as input and outputs the sign language recognition result. The other teacher model is the Text2Gloss translation teacher model, which targets to translate a text sentence into a gloss sequence. Both teacher models can provide information-rich soft labels to assist the training of the student model, which is a general sign language recognition model. We conduct extensive experiments on multiple commonly used sign language datasets, i.e., PHOENIX 2014T, CSL-Daily and QSL, the results show that the proposed cross-modal knowledge distillation method can effectively improve the sign language recognition accuracy by transferring multi-modal information from teacher models to the student model. Code is available at https://github.com/glq-1992/cross-modal-knowledge-distillation_new.

Integrated supercritical fluid extraction of essential oils.

Supercritical fluid extraction (SFE) stands out as an incredibly efficient, environmentally conscious, and fast method for obtaining essential oils (EOs) from plants. These EOs are abundant in aromatic compounds that play a crucial role in various industries such as food, fragrances, cosmetics, perfumery, pharmaceuticals, and healthcare. While there is a wealth of existing literature on using supercritical fluids for extracting plant essential oils, there's still much to explore in terms of combining different techniques to enhance the SFE process. This comprehensive review presents a sophisticated framework that merges SFE with EO extraction methods. This inclusive categorization encompasses a range of methods, including the integration of pressurized liquid processes, ultrasound assistance, steam distillation integration, microfluidic techniques, enzyme integration, adsorbent facilitation, supercritical antisolvent treatments, molecular distillation, microwave assistance, milling process and mechanical pressing integration. Throughout this in-depth exploration, we not only elucidate these combined techniques but also engage in a thoughtful discussion about the challenges they entail and the array of opportunities they offer within the realm of SFE for EOs. By dissecting these complexities, our objective is to tackle the current challenges associated with enhancing SFE for commercial purposes. This endeavor will not only streamline the production of premium-grade essential oils with improved safety measures but also pave the way for novel applications in various fields.

Membrane electrolysis distillation for volatile fatty acids extraction from pH-neutral fermented wastewater.

Volatile fatty acids (VFAs) serve as building blocks for a wide range of chemicals, but it is difficult to extract VFAs from pH-neutral wastewater using evaporation methods because of the ionized form. This study presents a new membrane electrolysis distillation (MED) process that extracts VFAs from such fermentation solutions. MED uniquely integrates pH regulation and joule heating to facilitate the efficient evaporation of VFAs. This integration occurs alongside a hydrophobic membrane that ensures effective gas-liquid phase separation. Operating solely on electricity, MED achieved an acid flux rate of 12.03 g/m2/h at 6V. In contrast, the control results without the joule heating or pH swing only obtained a 0.23 g/m2/h and 0.32 g/m2/h flux, respectively. In addition, a physicochemical model was developed to assess the impacts of temperature on membrane surface pH. This system enhances resource recovery from waste streams and helps achieve a circular carbon economy.

Treatment of wastewater from oil palm industry in Malaysia using polyvinylidene fluoride-bentonite hollow fiber membranes via membrane distillation system.

Membrane distillation (MD) is gaining increasing recognition within membrane-based processes for palm oil mill effluent (POME) treatment. This study aims to alter the physicochemical characteristics of polyvinylidene fluoride (PVDF) membranes through the incorporation of bentonite (B) at varying weight concentrations (ranging from 0.25 wt% to 1.0 wt%). Characterization was conducted to evaluate changes in morphology, thermal stability, surface characteristics and wetting properties of the resulting membranes. The resulting membranes were also tested using direct contact membrane distillation (DCMD) with POME as the feed solution, aiming to generate high-purity water. Results indicated that the PVDF-0.3B and PVDF-0.5B membranes achieved the highest water vapor flux. The finger-like structure and macrovoids present in these membranes aid in minimizing mass resistance during vapor transport and enhancing permeate flux. All membranes demonstrated exceptional performance in removing contaminants, eliminating total dissolved solids (TDS) and achieving over 99% rejection of chemical oxygen demand (COD), nitrate nitrogen (NN), color, and turbidity from the feed solution. The permeate water analysis showed that the PVDF-0.3B membrane had superior removal efficiency and met the standards set by the local Department of Environment (DOE). The PVDF-0.3B membrane was chosen as the preferred option because of its consistent flux and high removal efficiency. This study demonstrated that incorporating bentonite into PVDF membranes significantly enhanced their properties and performance for POME treatment.

Gravity-driven membrane integrated with membrane distillation for efficient shale gas produced water treatment.

Substantial volumes of hazardous shale gas produced water (SGPW) generated in unconventional natural gas exploration. Membrane distillation (MD) is a promising approach for SGPW desalination, while membrane fouling, wetting, and permeate deterioration restrict MD application. The integration of gravity-driven membrane (GDM) with MD process was proposed to improve MD performance, and different pretreatment methods (i.e., oxidation, coagulation, and granular filtration) were systematically investigated. Results showed that pretreatment released GDM fouling and improved permeate quality by enrich certain microbes' community (e.g., Proteobacteria and Nitrosomonadaceae), greatly ensured the efficient desalination of MD. Pretreatment greatly influences GDM fouling layer morphology, leading to different flux performance. Thick/rough/hydrophilic fouling layer formed after coagulation, and thin/loose fouling layer formed after silica sand filtration improved GDM flux by 2.92 and 1.9 times, respectively. Moreover, the beneficial utilization of adsorption-biodegradation effects significantly enhanced GDM permeate quality. 100 % of ammonia and 53.99 % of UV254 were efficiently removed after zeolite filtration-GDM and granular activated carbon filtration-GDM, respectively. Compared to the surged conductivity (41.29 µS/cm) and severe flux decline (>82 %) under water recovery rate of 75 % observed in single MD for SGPW treatment, GDM economically controlled permeate conductivity (1.39-19.9 µS/cm) and MD fouling (flux decline=8.3 %-27.5 %). Exploring the mechanisms, the GDM-MD process has similarity with Janus MD membrane in SGPW treatment, significantly reduced MD fouling and wetting.

Comparative Analysis of Hydrosol Volatile Components of Citrus × Aurantium 'Daidai' and Citrus × Aurantium L. Dried Buds with Different Extraction Processes Using Headspace-Solid-Phase Microextraction with Gas Chromatography-Mass Spectrometry.

This work used headspace solid-phase microextraction with gas chromatography-mass spectrometry (HS-SPME-GC-MS) to analyze the volatile components of hydrosols of Citrus × aurantium 'Daidai' and Citrus × aurantium L. dried buds (CAVAs and CADBs) by immersion and ultrasound-microwave synergistic-assisted steam distillation. The results show that a total of 106 volatiles were detected in hydrosols, mainly alcohols, alkenes, and esters, and the high content components of hydrosols were linalool, α-terpineol, and trans-geraniol. In terms of variety, the total and unique components of CAVA hydrosols were much higher than those of CADB hydrosols; the relative contents of 13 components of CAVA hydrosols were greater than those of CADB hydrosols, with geranyl acetate up to 15-fold; all hydrosols had a citrus, floral, and woody aroma. From the pretreatment, more volatile components were retained in the immersion; the relative contents of linalool and α-terpineol were increased by the ultrasound-microwave procedure; and the ultrasound-microwave procedure was favorable for the stimulation of the aroma of CAVA hydrosols, but it diminished the aroma of the CADB hydrosols. This study provides theoretical support for in-depth exploration based on the medicine food homology properties of CAVA and for improving the utilization rate of waste resources.

Surface energy-induced anti-wetting and anti-fouling enhancement of Janus membrane for membrane distillation.

Membrane distillation (MD) presents a promising alternative to conventional desalination systems, particularly for the treatment of hypersaline wastewater. However, the large-scale application of MD is hindered by challenges such as membrane wetting, membrane fouling, and low permeate flux. Herein, we proposed an air/liquid interface deposition method to fabricate a Janus membrane, termed the PVDF-PDA/PEI-Si membrane. The membrane featured a nanosieving, superhydrophilic polydopamine/polyethylenimine (PDA/PEI) layer decorated with silica nanoparticles, coupled with a microporous, hydrophobic polyvinylidene fluoride (PVDF) layer. The introduction of a dense PDA/PEI-Si layer featuring high surface energy significantly enhanced the wetting and fouling resistance of the membrane, with a minor effect on the permeate flux. The performance enhancement was particularly evident when hypersaline water containing sodium dodecyl sulfate (SDS) and oily contaminants was used as the feed. The interactions between the membrane and contaminants were calculated using the XDLVO theory and molecular dynamics simulations to elucidate the mechanisms underlying the enhanced anti-wetting and anti-fouling properties, respectively. According to the XDLVO theory, a large energy barrier must be overcome for the SDS to attach onto the PDA/PEI-Si surface. Meanwhile, molecular dynamics simulations confirmed the weak interaction energy between the oily foulants and the PVDF-PDA/PEI-Si membrane due to its high surface energy. This study presents a promising approach for the fabrication of high-performance MD membranes and provides new insights into the mechanisms underlying the enhanced anti-wetting and anti-fouling properties.

Ammonia recovery via direct contact membrane distillation: Modeling and performance optimization.

Ammonia recovery from wastewater has positive environmental benefits, avoiding eutrophication and reducing production energy consumption, which is one of the most effective ways to manage nutrients in wastewater. Specifically, ammonia recovery by membrane distillation has been gradually adopted due to its excellent separation properties for volatile substances. However, the global optimization of direct contact membrane distillation (DCMD) operating parameters to maximize ammonia recovery efficiency (ARE) has not been attempted. In this work, three key operating factors affecting ammonia recovery, i.e., feed ammonia concentration, feed pH, and DCMD running time, were identified from eight factors, by a two-level Plackett-Burman Design (PBD). Subsequently, Box-Behnken design (BBD) under the response surface methodology (RSM) was used to model and optimize the significant operating parameters affecting the recovery of ammonia though DCMD identified by PBD and statistically verified by analysis of variance (ANOVA). Results showed that the model had a high coefficient of determination value (R2 = 0.99), and the interaction between NH4Cl concentration and feed pH had a significant effect on ARE. The optimal operating parameters of DCMD as follows: NH4Cl concentration of 0.46 g/L, feed pH of 10.6, DCMD running time of 11.3 h, and the maximum value of ARE was 98.46%. Under the optimized conditions, ARE reached up to 98.72%, which matched the predicted value and verified the validity and reliability of the model for the optimization of ammonia recovery by DCMD process.

Comparison of quantity, quality and antibacterial activity of essential oil Mentha longifolia (L.) L. under different traditional and modern extraction methods.

Extraction is the first and most important step in obtaining the effective ingredients of medicinal plants. Mentha longifolia (L.) L. is of considerable economic importance as a natural raw material for the food and pharmaceutical industries. Since the effect of different extraction methods (traditional and modern methods) on the quantity, quality and antimicrobial activity of the essential oil of this plant has not been done simultaneously; the present study was designed for the first time with the aim of identifying the best extraction method in terms of these features. For this purpose, extracting the essential oil of M. longifolia with the methods of hydrodistillation with Clevenger device (HDC), steam distillation with Kaiser device (SDK), simultaneous distillation with a solvent (SDE), hydrodistillation with microwave device (HDM), pretreatment of ultrasonic waves and Clevenger (U+HDC) and supercritical fluid (SF) were performed. Chemical compounds were identified by gas chromatography coupled with mass spectrometer (GC-MS). Antimicrobial activity of essential oils against various clinical microbial strains was evaluated by agar diffusion method and determination of the minimum inhibitory concentration and minimum bactericidal concentration (MIC and MBC). The results showed that the highest and lowest yields of M. longifolia leaf essential oil belonged to HDC (1.6083%) and HDM (0.3416%). The highest number of compounds belonged to SDK essential oil and was equal to 72 compounds (with a relative percentage of 87.13%) and the lowest number of compounds was related to the SF essential oil sample (7 compounds with a relative percentage of 100%). Piperitenone (25.2-41.38%), piperitenone oxide (22.02-0%), pulegone (10.81-0%) and 1,8-cineole (5-35.0%) are the dominant and main components of M. longifolia essential oil were subjected to different extraction methods. Antimicrobial activity results showed that the lowest MIC value belonged to essential oils extracted by HDM, SDK, SDE and U+HDC methods with a value of 1000 µg/mL was observed against Gram-negative bacteria Shigella dysenteriae, which was 5 times weaker than rifampin and 7 times weaker than gentamicin. Therefore, it can be concluded that in terms of efficiency of the HDC method, in terms of the percentage of compounds of the HDM method, and in terms of microbial activity, the SDK, HDM and U+HDC methods performed better.

Improving membrane distillation performance by Fe(II) activated sodium percarbonate oxidation during the treatment of shale gas produced water.

Membrane distillation (MD) offers promise for recycling shale gas produced water (SGPW), while membrane fouling is still a major obstacle in standalone MD. Herein, sodium percarbonate (SPC) oxidation was proposed as MD pretreatment, and the performance of the single MD, SPC-MD hybrid process and Fe(II)/SPC-MD hybrid process for SGPW treatment were systematically evaluated. Results showed that compared to raw SGPW, the application of SPC and Fe(II)/SPC led to the decrease of the fluorescent organics by 28.54 % and 54.52 %, respectively. The hydrophobic fraction decreased from 52.75 % in raw SGPW to 37.70 % and 27.20 % for SPC and Fe(II)/SPC, respectively, and the MD normalized flux increased from 0.19 in treating raw SGPW to 0.65 and 0.81, respectively. The superiority of SPC oxidation in reducing the deposited membrane foulants and restoring membrane properties was further confirmed through scanning electron microscopy observation, attenuated total reflection fourier transform infrared, water contact angle and surface tension analyses of fouled membranes. Correlation analysis revealed that hydrophobic/hydrophilic matters and fluorescent organics in SGPW took a crucial role in MD fouling. The mechanism of MD fouling mitigation by Fe(II)/SPC oxidation was attributed to the decrease in concentrations and hydrophobicity of organic by synergistic oxidation, coagulation and adsorption.

Innovative hyper-thermophilic aerobic submerged membrane distillation bioreactor for wastewater reclamation.

For the first time, a hyper-thermophilic aerobic (>60 °C) bioreactor has been integrated with direct submerged membrane distillation (MD), highlighting its potential as an advanced wastewater treatment solution. The hyper-thermophilic aerobic bioreactor, operating up to 65 °C, is tailored for high organic removal, while MD efficiently produces clean water. Throughout the study, high removal rates of 99.5% for organic matter, 96.4% for ammonia, and 100% for phosphorus underscored the impressive adaptability of microorganisms to challenging hyper-thermophilic conditions and a successful combination with the MD process. Despite the extreme temperatures and substantial salinity accumulation reaching up to 12,532 µS/cm, the biomass of microorganisms increased by 1.6 times over a 92-day period, representing their remarkable resilience. The distillation flux ranged from 6.15 LMH to 8.25 LMH, benefiting from the temperature gradient in the hyper-thermophilic setting and the design of the tubular submerged MD membrane module. The system also excels in pH control, utilizing fewer alkali and nutritional resources than conventional systems. Meiothermus, Firmicutes, and Bacteroidetes, the three dominant species, played a crucial role, showcasing their significance in adapting to high salinity and decomposing organic matter.

Influence of photothermal nanomaterials localization within the electrospun membrane structure on purification of saline oily wastewater based on photothermal vacuum membrane distillation.

Today, synergistic combination of special nanomaterials (NMs) and electrospinning technique has emerged as a promising strategy to address both water scarcity and energy concerns through the development of photothermal membranes for wastewater purification and desalination. This work was organized to provide a new perspective on membrane design for photothermal vacuum membrane distillation (PVMD) through optimizing membrane performance by varying the localization of photothermal NMs. Poly(vinylidene fluoride) omniphobic photothermal membranes were prepared by localizing graphene oxide nanosheets (GO NSh) (1) on the surface (0.2 wt%), (2) within the nanofibers structure (10 wt%) or (3) in both positions. Considering the case 1, after 7 min exposure to the 1 sun intensity light, the highest temperature (∼93.5 °C) was recorded, which is assigned to the accessibility of GO NSh upon light exposure. The case 3 yielded to a small reduction in surface temperature (∼90.4 °C) compared to the case 1, indicating no need to localize NMs within the nanofibers structure when they are localized on the surface. The other extreme belonged to the case 2 with the lowest temperature of ∼71.3 °C, which is consistent with the less accessibility of GO NSh during irradiation. It was demonstrated that the accessibility of photothermal NMs plays more pronounced role in the membrane surface temperature compared to the light trapping. However, benefiting from higher surface temperature during PVMD due to enhanced accessibility of photothermal NMs is balanced out by decrease in the permeate flux (case 1: 1.51 kg/m2 h and case 2: 1.83 kg/m2 h) due to blocking some membrane surface pores by the binder. A trend similar to that for flux was also followed by the efficiency. Additionally, no change in rejection was observed for different GO NSh localizations.

Preparation of High-Purity Docosahexaenoic Acid Ethyl Ester from Algal Oil through Enzymatic Ethanolysis.

Docosahexaenoic acid plays a crucial role in infant brain function, and the market demand of high-purity docosahexaenoic acid is continuously increasing. The availability of docosahexaenoic acid in natural fish oil is limited, prompting the exploration of alternative sources like microalgae. For algal oil, enzymatic ethanolysis is preferred to chemical methods because the former is milder and can avoid docosahexaenoic acid oxidation. However, enzymatic methods have generally low yield due to the poor substrate-specificity of lipase to long-chain polyunsaturated fatty acids, affecting the yield and purity of docosahexaenoic acid. Therefore, we developed an efficient process to produce high-purity docosahexaenoic acid ethyl ester from algal oil, by screening lipases, optimizing enzymatic ethanolysis and applying molecular distillation. Lipase UM1 was the best lipase to produce ethyl ester from algal oil with the highest ethyl ester yield (95.41%). Meanwhile, it was a catalyst for the reaction of long-chain polyunsaturated fatty acids with ethanol. The fatty acid docosahexaenoic acid conversion rates exceeded 90%. After molecular distillation, a final product containing 96.52% ethyl ester was obtained with a docosahexaenoic acid content up to 80.11%. Our findings provide an highly effective enzymatic method for the production of high-purity docosahexaenoic acid ethyl esters, with potential commercial applications.