並列タイトル等機能性薄膜と電気化学デバイスの作製と評価 及び機械学習の応用
一般注記In this study, WO3 NPs dispersion ink with excellent adhesion was developed for thefabrication of electrochromic devices capable of wet coating, device fabrication tests wereperformed using the developed materials, and performance was predicted anddemonstrated using machine learning.To improve the adhesion between the WO3 nanoparticles and the substrate of the ink,polyvinyl alcohol (PVA) was added as an additive. PVA was adjusted to 0-10 wt.%, andelectrochemical properties and EC properties were studied. As a result, the WO3 thin filmprepared with 1 wt.% PVA addition showed excellent adhesion between WO3nanoparticles and substrate. In addition, it was found that the composition of the WO3thin film exhibiting excellent electrochemical properties with the largest colorationefficiency of 35 cm2/C and electrochromic properties with a change in visible light (λ =633 nm) transmittance of 90%⇔13%.To fabricate the next-generation EC device, we investigated the fabrication of flexibleECD using a PET substrate. For the preparation of EC materials, coated WO3 and PBfilms were prepared using the WO3 synthesized in Chapter 3 and the PB ink originallydeveloped by Adhesion and Interface Research Group at National Institute of AdvancedIndustrial Science and Technology (AIST), and these were assembled to prepare a flexibleEC device. The prepared flexible PET EC device showed a dramatic color change fromtransparent to dark blue and showed higher coloring efficiency (123.32 cm2 / C) than theconventional glass EC device (86.44 cm2 / C). Furthermore, it showed electrochemicalstability without deterioration up to 100 cycles, and mechanically excellent durabilityagainst repeated bending and twisting experiments.Finally, machine learning was applied to obtain the optimal WO3 NPs dispersed inkpreparation condition for the application of EC materials. The predicted value obtainedthrough simulation showed an accuracy of more than 90% with the experimental value.In addition, the highest coloring efficiency value of 38.2 cm2/C obtained through thisstudy was improved by about 10% compared to the coloring efficiency value (35.0 cm2/C)of Chapter 3. Therefore, we believe that it is possible to develop an optimized process forthe preparation of EC materials using machine learning. In the future, we will focus onthe development of functional nano-dispersion inks optimized to prepare functional thinfilms using various parameters (materials, manufacturing conditions, atmosphere, etc.)through simulation prediction using more advanced algorithms.
コレクション（個別）国立国会図書館デジタルコレクション > デジタル化資料 > 博士論文
受理日（W3CDTF）2022-05-09T11:57:37+09:00
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