Immobilization of nano-scaled TiO 2 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 selfcleaning performance of the novel TiO 2 /PAA/PTFE UF membranes, which were coated via an innovative plasmaintensified coating strategy. Through careful characterizations, the enhanced engineering properties and the selfcleaning 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 TiO 2 /PAA/PTFE composite membranes could be recovered with simple hydraulic cleaning combined with UV irradiation. The mechanical properties and thermal stability of TiO 2 /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 TiO 2 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 layers.
17Mathematical models plays an important role in performance prediction and 18 optimization of ultrafiltration (UF) membranes fabricated via dry/wet phase inversion 19 in an efficient and economical manner. In this study, a systematic approach, namely, a 20 supervised, learning-based experimental data analytics framework, is developed to 21 model and optimize the flux and rejection rate of Poly (vinyl chloride) (PVC) and 22Polyvinyl butyral (PVB) blend UF membranes. Four supervised learning (SL) 23 approaches, namely, the multiple additive regression tree (MART), the neural 24 verified by experiments. We hope the approach is able to shed light on how to 34 systematically analyzing multi-objective optimization issues for fabrication conditions 35
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