锐钛矿
金红石
拉曼光谱
化学工程
溅射沉积
相(物质)
化学
矿物学
薄膜
材料科学
分析化学(期刊)
纳米技术
溅射
光学
色谱法
光催化
生物化学
工程类
催化作用
物理
有机化学
作者
J. Jasiński,M. Lubas,K. Suchorab,Magdalena Gawęda,Ł. Kurpaska,Marcin Brykała,A. Kosińska,Maciej Sitarz,J. Jagielski
标识
DOI:10.1016/j.molstruc.2022.132803
摘要
The paper presents research on estimating concentrations and distribution of rutile and anatase on the surface and cross-section of TiO2 thin layers by Raman imaging. The TiO2 layers were formed by hybrid oxidation, which combines fluidised bed (FB) diffusive treatment at 640 °C for 8 h and magnetron sputtering. The rutile structure of the titania layer plays a vital role in biomaterials surface, inducing the apatite deposition depending on the lattice matching between rutile and apatite. However, a mixture of rutile and anatase has also been shown to have beneficial properties in terms of biomedical applications. According to this statement, the critical point is to estimate the rutile/anatase phase ratio in order to optimise the substrate preparation method to obtain the most favourable surface to create a permanent tissue-implant connection. The aforementioned phase composition is created during the proposed double-step synthesis of hybrid TiO2 layer by FB (rutile) and physical vapour deposition (PVD, rutile/anatase). In this work, scanning electron microscopy assisted with energy dispersive spectroscopy (SEM-EDS) was used for microstructural characterisation and elemental analysis. Moreover, Raman imaging was applied to determine phase distribution and estimate rutile/anatase concentration in TiO2 thin layers. Such an approach proposed by the authors presents new perspectives for the qualitative and semi-quantitative analysis of the TiO2 oxide phase concentration. It also demonstrates the significant potential of the Raman imaging technique for calculating surface composition determining homogeneity of the coating to evaluate material to obtain the best possible proportion of TiO2 phases, ideal for biomedical applications.
科研通智能强力驱动
Strongly Powered by AbleSci AI