The liquid phase deposition (LPD) of metal oxide thin films at the liquid−liquid interface was investigated. Just after the start of the LPD reaction, depositions were observed continually at the liquid−liquid interface. The deposition grew two-dimensionally into a self-standing film with thickness of 1 μm and domain size of up to ca. 1 mm without any solid substrates. The self-standing film was formed with asymmetrical morphology, which consisted of flat surface on the side of the liquid−liquid interface and relatively rough surface on the side of the bulk solution. The structure was characterized by Raman spectroscopy, confirming that metastable ammonium titanium oxide fluoride (NH4TiOF3) was first deposited at the liquid−liquid interface, on which anatase-type titanium oxide (TiO2) was deposited second to forming the asymmetrical bilayer structure. It was suggested that the metal fluoride complex of the precursor was concentrated in the vicinity of the liquid−liquid interface compared with the solid−liquid interface due to the difference of the interfacial free energy, which could cause formation of the asymmetrical structure. The liquid−liquid interface could be confirmed as the specific reaction field for the LPD reaction. Also, the other metal oxide self-standing films such as tin oxide (SnO2) and iron hydroxide oxide (β-FeOOH) were obtained in this process. The process also has great potential not only for basic science but also in the engineering field such as a soft solution process for template-free synthesis.
ABSTRACT; Recently, managing water quality in distribution pipes has become an important subject for water supply systems. However, it is very difficult to grasp the spatial and hourly fluctuation of residual chlorine concentration which is important for water quality. This paper presents the modeling process for determining residual chlorine concentration in a water distribution network. First, the modeling methodology which we propose in this paper is presented.Regarding a water distribution network as a black box system, we make a water quality simulation model by neural networks. Our model includes the time lag between the input point and the output point. Second, we show the modeling process. This process consists of choosing input points and output points, pre-analysis for neural network structure, modeling, and estimation for results of the model. Third, we apply the modeling process to the actual distribution network as a case study, which proved that our model worked well. Our proposed modeling process can be applied in all seasons and at various places. Therefore, it will be a very useful tool for water quality management.
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