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Graphene Oxide Nanosheet-Composited Poly(N-isopropylacrylamide) Hydrogel for Cell Sheet Recovery
Yongqing Xia,Han Wu,Dachao Tang,Shuai Gao,Binghe Chen,Zhujun Zeng,Shengjie Wang,Meiwen Cao,Dongxiang Li 한국고분자학회 2019 Macromolecular Research Vol.27 No.7
Cell sheet engineering technique has been applied to treat various tissues without the use of a traditional scaffold. To date, methods for the cell sheet harvesting depend mostly on grafted poly(N-isopropylacrylamide) (pNIPAAm) thin layers, while the native pNIPAAm hydrogel, which possibly presents the easiest way to prepare thermo-responsive materials, is not suitable for the cell sheet harvesting due to its low cell attachment. In this study, the graphene oxide (GO) nanosheet was utilized as an additive to enhance the bio-compatibility of the pNIPAAm hydrogel. Different concentrations of GO nanosheets were added to prepare GO/pNIPAAm composite hydrogels through the in-situ free radical polymerization with polyethylene glycol dimethacrylate (PEGDA) as a cross-linker. The results indicated that the physical properties of the composite hydrogels had little difference with that of the native pNIPAAm hydrogel. However, the cell attachment, proliferation and detachment behaviors on the composite hydrogel surface were greatly enhanced. Monkey fibroblast COS7 cells attached and proliferated better on the GO/pNIPAAm composite hydrogel, while intact COS7 cell sheets could be harvested from the composite hydrogels by simply lowering the temperature. In contrast, the cells appeared as clusters on the native pNIPAAm hydrogel. Furthermore, when HeLa and COS7 cells were seeded successively onto the micropatterned GO/pNIPAAm hydrogel, there could be the formation of a patterned HeLa/COS7 cell layer. The geometrically patterned GO/pNIPAAm hydrogel may provide an easy-to-prepare material for releasing patterned cell sheets compared to the specific cell-adhesive proteins reported to make patterned cell layers.
Huafeng Xia,Yongqing Yang,Feng Ding 제어·로봇·시스템학회 2020 International Journal of Control, Automation, and Vol.18 No.2
This paper studies the identification problem of multivariable controlled autoregressive moving average systems. For the case with a parameter matrix and an unmeasurable vector in the system identification model, we transform the model into several submodels based on the number of the outputs. A maximum likelihood-based recursive least-squares algorithm is derived to identify the parameters of each submodel. A multivariable recursive extended least-squares algorithm is provided as a comparison. The effectiveness of the proposed identification algorithm is verified by simulation examples.