Globally, the issue of oil-water emulsion is continuously tickling in the minds of researchers. The oil-water emulsion is hazardous to marine ecological safety and human health around the globe, indicating that we urgently need to develop efficient strategies to tackle emulsion issues. Materials with porous morphology and selective superwettability towards certain liquids are essential in separating oil-water emulsions. Recently, there has been increased demand for smart oil-absorbent and water-absorbent superwetting porous materials. Over the past decade, several innovative advancements and promising industrial developments to separate oil-water emulsions have been spotted. This review article overviews the latest advancements in the design of superwetting materials for oil-water emulsion separation and thoroughly discusses oil-absorbent and water-absorbent superwetting porous materials, demulsification, and superamphiphilic materials. In each section, the design strategies, base materials and oil-water emulsion separation performance of superwetting porous materials have been systematically discussed. Then, common challenges of superwetting materials in practical application are discussed. Finally, conclusions and future perspectives concerning oil-water emulsion separation based on superwetting porous materials are summarized. This review article can allow helping hand to early career scientists to elegantly understand the oil-water emulsion separation issues. 

Efficient Separation of Oil-Water Emulsions: Competent Design of Superwetting Materials for Practical Applications

Chemical separation technologies such as water purification, gas separation, and solvent recovery have increasingly adopted membrane-based methods for their efficiency and adaptability. However, the cost and environmental consequences of traditional membrane synthesis, including the use of fossil-based polymers and toxic solvents are hindering their scaleup and large-scale adoption. Recognizing this, efforts to replace polymers with sustainable biopolymers and recycle/upcycle waste polymers are underway. Furthermore, the need to replace toxic solvents, such as hexane, with green alternatives, like deep eutectic solvents (DES), is being realized. Although the efficacy of using sustainable alternatives to membrane materials are reviewed in some studies, a comprehensive review that unifies these approaches has been lacking. This review aims to fill this gap by presenting a detailed analysis of sustainable membrane technologies, examining the use of bio-based polymers, green solvents, recycled waste materials, and natural additives. The work emphasizes the vast application potential of these environmentally responsible alternatives across various separation processes and highlights innovative techniques for waste valorization in membrane synthesis. While showcasing prospects, the review also identifies persistent challenges, particularly the dependency on fossil-based inputs. By presenting an integrated perspective on the development and implementation of green membranes, this study is expected to guide future research in the field and contribute to promoting the circular economy and achieving sustainable development goals (SDGs). 

Sustainable and green membranes for chemical separations: A review

PD-adaptive variable impedance constant force control of macro-mini robot for compliant grinding and polishing

Peritoneal metastasis is a malignant disease which originated from several gastrointestinal and gynecological carcinomas and has been leading to a suffering condition in patients for decades. Currently, as people have gradually become more aware of the severity of peritoneal carcinomatosis, new molecular mechanisms for targeting and new treatments have been proposed. However, due to the uncertainty of influencing factors involved and a lack of a standardized procedure for this treatment, as well as a need for more clinical data for specific evaluation, more research is needed, both for preventing and treating. We aim to summarize backgrounds, mechanisms and treatments in this area and conclude limitations or new aspects for treatments.

/pdf/development-of-the-peritoneal-metastasis-a-review-of-back-1hbqbidr.pdf

Development of the Peritoneal Metastasis: A Review of Back-Grounds, Mechanisms, Treatments and Prospects

Photocatalytic self-cleaning membrane with polyaniline/NH2-MIL-125 heterojunction for highly oil-water/seawater separation and bacterial inactivation

Gastric cancer (GC) is the most common gastrointestinal malignant tumor, and distant metastasis is a critical factor in the prognosis of patients with GC. Understanding the mechanism of GC metastasis will help improve patient prognosis. Studies have confirmed that urokinase-type plasminogen activator receptor (PLAUR) promotes GC metastasis; however, its relationship with anoikis resistance and associated mechanisms remains unclear. In this study, we demonstrated that PLAUR promotes the anoikis resistance and metastasis of GC cells and identified transcription Factor 7 Like 2 (TCF7L2) as an important transcriptional regulator of PLAUR. We also revealed that TCF7L2 is highly expressed in GC and promotes the anoikis resistance and metastasis of GC cells. Moreover, we found that TCF7L2 transcription activates PLAUR. Finally, we confirmed that TCF7L2 is an independent risk factor for poor prognosis of patients with GC. Our results show that TCF7L2 and PLAUR are candidate targets for developing therapeutic strategies for GC metastasis.

/pdf/tcf7l2-promotes-anoikis-resistance-and-metastasis-of-gastric-23zl6smt.pdf

TCF7L2 promotes anoikis resistance and metastasis of gastric cancer by transcriptionally activating PLAUR

Waste to treasure: A superwetting fiber membrane from waste PET plastic for water-in-oil emulsion separation

This study aimed to establish and compare the radiomics machine learning (ML) models based on non-contrast enhanced computed tomography (NECT) and clinical features for predicting the simplified risk categorization of thymic epithelial tumors (TETs).A total of 509 patients with pathologically confirmed TETs from January 2009 to May 2018 were retrospectively enrolled, consisting of 238 low-risk thymoma (LRT), 232 high-risk thymoma (HRT), and 39 thymic carcinoma (TC), and were divided into training (n = 433) and testing cohorts (n = 76) according to the admission time. Volumes of interest (VOIs) covering the whole tumor were manually segmented on preoperative NECT images. A total of 1218 radiomic features were extracted from the VOIs, and 4 clinical variables were collected from the hospital database. Fourteen ML models, along with varied feature selection strategies, were used to establish triple-classification models using the radiomic features (radiomic models), while clinical-radiomic models were built after combining with the clinical variables. The diagnostic accuracy, sensitivity, specificity, and the area under the receiver operating characteristic curve (AUC) of radiologist assessment, the radiomic and clinical-radiomic models were evaluated on the testing cohort.The Support Vector Machine (SVM) clinical-radiomic model demonstrated the highest AUC of 0.841 (95% CI 0.820 to 0.861) on the cross-validation result and reached an AUC of 0.844 (95% CI 0.793 to 0.894) in the testing cohort. For the one-vs-rest question of LRT vs HRT + TC, the sensitivity, specificity, and accuracy reached 80.00%, 63.41%, and 71.05%, respectively. For HRT vs LRT + TC, they reached 60.53%, 78.95%, and 69.74%. For TC vs LRT + HRT they reached 33.33%, 98.63%, and 96.05%, respectively. Compared with the radiomic models, superior diagnostic efficacy was demonstrated for most clinical-radiomics models, and the AUC of the Bernoulli Naive Bayes model was significantly improved. Radiologist2's assessment achieved a higher AUC of 0.813 (95% CI: 0.756-0.8761) than other radiologists, which was slightly lower than the SVM clinical-radiomic model. Combined with other evaluation indicators, SVM, as the best ML model, demonstrated the potential of predicting the simplified risk categorization of TETs with superior predictive performance to that of radiologists' assessment.Most of the ML models are promising in predicting the simplified TETs risk categorization with superior efficacy to that of radiologists' assessment, especially the SVM models, demonstrated the integration of ML with NECT may be valuable in aiding the diagnosis and treatment planning. 

http://www.lungcancerjournal.info/article/S0169500222003762/pdf

Optimizing the radiomics-machine-learning model based on non-contrast enhanced CT for the simplified risk categorization of thymic epithelial tumors: A large cohort retrospective study.

Converting wastes to resource: Preparation of NiO@γ-Al2O3 sludge composite from aluminum-containing sludge for cadmium removal from wastewater

Real-time normal contact force control for robotic surface processing of workpieces without a priori geometric model

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