Surface engineering and self-cleaning properties of the novel TiO2/PAA/PTFE ultrafiltration membranes

Immobilization of nano-scaled TiO2 onto polymeric ultrafiltration (UF) membrane offers desirable antifouling and self-cleaning properties to the membrane, which is practical in wastewater purification only if the mechanical strength and long-term self-cleaning durability are realized. This paper reported the surface roughness, mechanical properties, thermal stability, and recycling self-cleaning performance of the novel TiO2/PAA/PTFE UF membranes, which were coated via an innovative plasma-intensified coating strategy. Through careful characterizations, the enhanced engineering properties and the self-cleaning performance were correlated with the surface chemical composition and the creative coating technique. In the recycling photocatalytic self-cleaning tests in photodegradation of methylene blue (MB) solution, about 90 % MB photocatalytic capability of TiO2/PAA/PTFE composite membranes could be recovered with simple hydraulic cleaning combined with UV irradiation. The mechanical properties and thermal stability of TiO2/PAA/PTFE also satisfy the practical application in water and wastewater treatments, despite that the original engineering properties were slightly influenced by PAA grafting and TiO2 coating. The changed properties of the composite UF membrane relative to PTFE are reasonably attributed to the variation of the surface chemical species and chemical bonding, as well as the thickness and evenness of the surface functional layer

Fabrication of TiO2-modified polytetrafluoroethylene ultrafiltration membranes via plasma-enhanced surface graft pretreatment

Surface hydrophilic modification of polymer ultrafiltration membrane using metal oxide represents an effective yet highly challenging solution to improve water flux and antifouling performance. Via plasma-enhanced graft of poly acryl acid (PAA) prior to coating TiO2, we successfully fixed TiO2 functional thin layer on super hydrophobic polytetrafluoroethylene (PTFE) ultrafiltration (UF) membranes. The characterization results evidenced TiO2 attached on the PTFE-based UF membranes through the chelating bidentate coordination between surface-grafted carboxyl group and Ti4+. The TiO2 surface modification may greatly reduce the water contact angle from 115.8° of the PTFE membrane to 35.0° without degradation in 30-day continuous filtration operations. The novel TiO2/PAA/PTFE membranes also exhibited excellent antifouling and self-cleaning performance due to the intrinsic hydrophilicity and photocatalysis properties of TiO2, which was further confirmed by the photo-degradation of MB under Xe lamp irradiation

Novel g-C₃N₄/TiO₂/PAA/PTFE ultrafiltration membrane enabling enhanced antifouling and exceptional visible-light photocatalytic self-cleaning

Membrane fouling due to superhydropobicity of polytetrafluoroethylene ultrafiltration membranes (PTFE UFMs) represents a grand challenge for their practical applications in diverse water treatment industries. Surface immobilisation of hydrophilic and chemically stable inorganic metal oxides (TiO2, ZrO2, etc) has been developed to improve hydrophilicity of the PTFE UFMs, though they still suffer from expensive and repeating regenerations once fouled. To address such issues, we strive to firmly immobilize g-C3N4 modified TiO2 (g-C3N4/TiO2, hereafter CNTO) onto PTFE UFM via a facile plasma-enhanced surface graft technique using polyacrylic acid (PAA) as a bridging agent. As reported here, the obtained CNTO/PAA/PTFE UFM shows much smaller surface water contact angle (WCA) of 62.3° than that of bare PTFE UFM (115.8°), leading to enhanced water flux of 830 L m−2 h-1 in the initial ultrafiltration of modelled waste-water containing methylene blue (MB). The CNTO/PAA/PTFE UFM is highly resistant to fouling in the prolonged filtration of 1000 mg/L bovine serum albumin (BSA) solution, while the fouled CNTO/PAA/PTFE UFM is able to regenerate rapidly under either UV or visible-light irradiation. The enhanced performance of the novel CNTO/PAA/PTFE UFM is reasonably attributed to its high wettability and robust photocatalytic activity of the g-C3N4/TiO2 coating that follows different self-cleaning mechanisms under UV and visible light irradiations. </p

Modeling and optimizing the performance of PVC/PVB ultrafiltration membranes using supervised learning approaches

Mathematical models play an important role in performance prediction and optimization of ultrafiltration (UF) membranes fabricated via dry/wet phase inversion in an efficient and economical manner. In this study, a systematic approach, namely, a supervised, learning-based experimental data analytics framework, is developed to model and optimize the flux and rejection rate of poly(vinyl chloride) (PVC) and polyvinyl butyral (PVB) blend UF membranes. Four supervised learning (SL) approaches, namely, the multiple additive regression tree (MART), the neural network (NN), linear regression (LR), and the support vector machine (SVM), are employed in a rigorous fashion. The dependent variables representing membrane performance response with regard to independent variables representing fabrication conditions are systematically analyzed. By comparing the predicting indicators of the four SL methods, the NN model is found to be superior to the other SL models with training and testing R-squared values as high as 0.8897 and 0.6344, respectively, for the rejection rate, and 0.9175 and 0.8093, respectively, for the flux. The optimal combination of processing parameters and the most favorable flux and rejection rate for PVC/PVB ultrafiltration membranes are further predicted by the NN model and verified by experiments. We hope the approach is able to shed light on how to systematically analyze multi-objective optimization issues for fabrication conditions to obtain the desired ultrafiltration membrane performance based on complex experimental data characteristics

LUAI challenge 2021 on learning to understand aerial images

This report summarizes the results of Learning to Understand Aerial Images (LUAI) 2021 challenge held on ICCV'2021, which focuses on object detection and semantic segmentation in aerial images. Using DOTA-v2.0 [7] and GID-15 [35] datasets, this challenge proposes three tasks for oriented object detection, horizontal object detection, and semantic segmentation of common categories in aerial images. This challenge received a total of 146 registrations on the three tasks. Through the challenge, we hope to draw attention from a wide range of communities and call for more efforts on the problems of learning to understand aerial images

Are medical record front page data suitable for risk adjustment in hospital performance measurement? Development and validation of a risk model of in-hospital mortality after acute myocardial infarction

Objectives To develop a model of in-hospital mortality using medical record front page (MRFP) data and assess its validity in case-mix standardisation by comparison with a model developed using the complete medical record data.Design A nationally representative retrospective study.Setting Representative hospitals in China, covering 161 hospitals in modelling cohort and 156 hospitals in validation cohort.Participants Representative patients admitted for acute myocardial infarction. 8370 patients in modelling cohort and 9704 patients in validation cohort.Primary outcome measures In-hospital mortality, which was defined explicitly as death that occurred during hospitalisation, and the hospital-level risk standardised mortality rate (RSMR).Results A total of 14 variables were included in the model predicting in-hospital mortality based on MRFP data, with the area under receiver operating characteristic curve of 0.78 among modelling cohort and 0.79 among validation cohort. The median of absolute difference between the hospital RSMR predicted by hierarchical generalised linear models established based on MRFP data and complete medical record data, which was built as ‘reference model’, was 0.08% (10th and 90th percentiles: −1.8% and 1.6%). In the regression model comparing the RSMR between two models, the slope and intercept of the regression equation is 0.90 and 0.007 in modelling cohort, while 0.85 and 0.010 in validation cohort, which indicated that the evaluation capability from two models were very similar.Conclusions The models based on MRFP data showed good discrimination and calibration capability, as well as similar risk prediction effect in comparison with the model based on complete medical record data, which proved that MRFP data could be suitable for risk adjustment in hospital performance measurement