Robust cellulose-carbon nanotube conductive fibers for electrical heating and humidity sensing

Multifunctional fibers have attracted widespread attention due to applications in flexible smart wearable devices. However, simultaneously obtaining a strong and functional woven fiber is still a great challenge owing to the conflict between the properties mentioned above. Herein, mechanically strong and highly conductive cellulose/carbon nanotube (CNT) composite fibers were spun using an aqueous alkaline/urea solution. The microstructure as well as physical properties of the resulting fibers were characterized via scanning electron microscopy, infrared spectroscopy, mechanical and electrical measurement. We demonstrated that carboxylic CNTs can be well dispersed in alkali/urea aqueous systems which also dissolved cellulose well. The subsequent wet spinning process aligned the CNTs and cellulose molecules inside the regenerated composite fiber well, enhancing the interaction between these two components and endowing the composite fiber having a 20% CNT loading with an excellent mechanical strength of 185 MPa. Benefiting from the formation of conductive paths, the composite fiber with the diameter of about 50 μm possessed an electrical conductivity value in the range of 64–1274 S/m for 5–20 wt% CNT loading. This excellent mechanical strength and high electrical conductivity enable the composite fiber to exhibit a great potential in joule heating; the heating temperature of cellulose/CNT-20 fiber reached more than 55 °C within 15 s at 9 V. In addition, the multifunctional filaments are further manufactured as a water sensor to measure humidity. This work provides a potential material that can be applied in the fields of wearable electronics and smart flexible fabrics.

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铈掺杂对超细LaMnO_(3+λ)催化性能的影响

以Ce2(CO3)3,La2O3和Mn(NO3)2为原料,用"溶胶-凝胶"法结合"超临界干燥"技术,将铈掺入超细LaMnO3+λ气凝胶中。用TG-DTA,XRD,TEM,FT-IR等手段对样品进行表征;并用"2CO+2NO=2CO2+N2"反应测试铈掺杂对超细LaMnO3+λ催化活性的影响。结果显示:260℃时,镧铈锰气凝胶为疏松、絮状且具有较好分散性的棕色粉末,由大量直径小于10 nm的球形颗粒组成;850℃热处理后,镧铈锰气凝胶为粒径小于20 nm的类球形颗粒,晶相成分为LaMnO3+λ,La2O3和CeO2;铈掺杂增加LaMnO3+λ晶格的氧空缺数量,改善氧化还原催化的气氛条件,提高超细LaMnO3+λ的催化活性。     

Variable knot-based spline approximation recursive Bayesian algorithm for the identification of Wiener systems with process noise

Wiener systems consist of a linear dynamic block in cascade with static nonlinearity. One of the challenging issues in the identification of a process noise disturbed Wiener system is that the influence of noise is difficult to eliminate. For Wiener systems with process noise, traditional algorithms will result in biased estimates. To solve this problem, a novel recursive Bayesian algorithm based on variable knot spline approximation is proposed in this paper. First, a spline function is taken to approximate the inverse function of the nonlinear part, which can achieve excellent extrapolation and eliminate oscillatory behaviors. A knot selection method is then presented to achieve accurate estimates. Furthermore, a knot variation strategy to improve the accuracy of the spline approximation is described. Finally, the proposed algorithm is validated through a numerical simulation.