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Materials-Informatics-Assisted High-Yield Synthesis of 2D Nanomaterials through Exfoliation
摘要： A variety of inorganic and organic nanosheets with characteristic structures and properties can be synthesized through exfoliation of layered materials. However, in general, immense time and efforts are required for exploration of exfoliation conditions and characterization of nanosheets. In addition, it is challenging to improve the yield of nanosheets obtained through exfoliation. Here a materials-informatics-assisted high-yield synthesis of nanosheets is proposed, which does not require experience and intuition. Layered composites containing inorganic layers and interlayer organic guests are delaminated into nanosheets in a variety of dispersion media. First, an experimental screening is performed to find efficient exfoliation conditions and obtain a training dataset for the informatics approach. Sparse modeling is then used facilitating the extraction of important factors predicting the yield of nanosheets. High-yield (up to (cid:2)50%) synthesis of nanosheets is demonstrated in unknown systems in a minimum number of experiments. The yield is higher than those typically reported for layered materials. It is expected that the effective combination has potentials for not only discovery of compounds but also structure control of materials.
关键词： sparse modeling,layered materials,exfoliation,2D nanomaterials,materials informatics
更新于2025-09-23 15:22:29
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[IEEE 2018 IEEE 20th International Workshop on Multimedia Signal Processing (MMSP) - Vancouver, BC, Canada (2018.8.29-2018.8.31)] 2018 IEEE 20th International Workshop on Multimedia Signal Processing (MMSP) - Sparse Hartley Modeling for Fast Image Extrapolation
摘要： In many cases, image and video signal processing demands for high quality extrapolation algorithms, e.g., to solve inpainting problems or to increase image resolution. Indeed, a high computational load goes hand in hand with a good reconstruction quality as expensive models are calculated to estimate the missing data. To overcome this, the high-speed sparse Hartley modeling is introduced in this paper. This algorithm is based on Frequency Selective Extrapolation. In contrast to that, the model generation is carried out in the Hartley domain to exploit its real-valued transform properties. Due to this, it is possible to reduce the computational complexity significantly as no complex-valued arithmetic operations have to be conducted. In other words, a slightly higher reconstruction quality is obtained, while the proposed method is more than three times faster than the competing Frequency Selective Extrapolation.
关键词： Sparse Modeling,Inpainting,Signal Extrapolation,Error Concealment
更新于2025-09-11 14:15:04