An electrochromic-type electronic paper was prepared using nanocomposites that consisted of silica nanoparticles (silica 60 wt %) and polyamide pulp. Its light scattering, ion transport, and aqueous electrolyte retention characteristics were examined. As a result, the shape of the nanocomposites was completely self-standing, though it could be impregnated with about nine times as much water on a weight basis. Moreover, its light scattering property was extremely similar to paper. Because of the impregnation of a large amount of water, the ion transport property of the nanocomposites was the same as that of the electrolyte solution without the nanocomposites. The nanocomposites was impregnated using an aqueous solution in which bismuthyl perchlorate (redox species), copper perchlorate, perchloric acid, sodium perchlorate, hydroquinone (electron mediator) and 2-buthyne-1,4-diol (leveling agent) were dissolved. The electronic paper was then prepared by sandwiching the nanocomposites between an indium-tin-oxide transparent electrode and a copper sheet. This electronic paper utilizes the reversible codeposition reaction of black Bi-Cu from bismuthyl perchlorate and copper(II) ions. The characteristics of this electronic paper were examined, and excellent characteristics with a white reflectivity of 65%, black reflectivity of 6.4%, contrast ratio of 10:1, operating life of over 1 × 10 6 cycles and open-circuit memory of at least 1 month were obtained. In addition, its driving voltage was 1.2 V, and the write time was 500 ms.

In this study, U-Net based deep convolutional networks are used to achieve the segmentation of particle regions in a microscopic image of colorants. The material appearance of products is greatly affected by the distribution of the particle size. From that fact, it is important to obtain the distribution of the particle size to design the material appearance of products. To obtain the particle size distribution, it is necessary to segment particle regions in the microscopic image of colorants. Conventionally, this segmentation is performed manually using simple image processing. However, this manual processing leads to low reproducibility. Therefore, in this paper, to extract the particle region with high reproducibility, segmentation is performed using U-Net based deep convolutional networks. We improved deep convolutional U-Net type networks based on the feature maps trained for a microscopic image of colorants. As a result, we obtained more accurate segmentation results using the improved network than conventional U-Net.

Novel Display realizing true paper-like whiteness has been developed, which is rewritable by utilizing the electrochromic system. Nylon/silica nanocomposite pulp holding a great deal of electrolyte was used resulting in naturally white electrochromic display which has a higher white state reflectivity of 65%, a higher contrast ratio of 10:1, and can be driven with a higher response time of less than 500ms at a lower voltage of less than 1.5V.

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