Combined Molecular Design, Morphology Control, and Device Engineering Towards Superior Organic Semiconductors

Department of Energy Engineering (Energy Engineering)Owing to the valuable features of organic polymers, such as inexpensive materials, ease of mass production, light-flexible properties, in recent days, the organic semiconductors have attracted significant research interests of challenging scientists in conducting and semiconducting organic polymers. The main concept of the conjugation of conducting organic polymers has occurred from the continuously connected pz-orbitals of sp2(or sp)-hybridized carbon atoms from the alternating sequence of single and multi-bonds (double and triple bonds) in the polymeric backbone. Based on the concept of conjugation structure, until now, many ??-conjugated organic polymers and small molecules have been designed with great expectation for various advents of the electronic application, like as organic light-emitting diodes (OLEDs), organic photovoltaics (OPVs), and organic field-effect transistors (OFETs). Over the past decades, many pioneering research groups have paid attention to the design and development of novel organic semiconductors for next generated optoelectronic devices due to the aforementioned advantages of organic semiconductors. Moreover, many research interests have been concentrated on not only the invention of completely brand-new molecular structures but also fine-tuning of the existing molecular structures with marginal trade-off their outstanding own properties. In particular, the fine-tuning approaches are quite effective methods because the parent molecules had exhibited remarkable performances in optoelectronic devices. Therefore, the modified molecules usually have shown comparable or much better performances compared to parent molecules. In terms of modification of backbone, I present the article describes the synthesis and characterization as well as OFET characteristics of a collection of TBIG-based polymers with varied compositions between TBIG and IIG accepting segments and bithiophene counterpart donor. Secondly, based on the designed and synthesized with 2-ethylhexyl and 5-ethylnonyl side chains on the CPDT core, I demonstrated its effectiveness of side-chain engineering for the CPDT-based polymers and CPDT-based small molecules on the OFET performance and semi-transparent OPV performance, respectively. Finally, to fine-tuning of molecular properties, the substituents have been used without the sacrificial of the mainstream of organic semiconductors. The most universal substituent, fluorine with single proton, I investigated the constitutional isomeric effects on the photovoltaic performances via atomic level insight from the computational simulation and nano-second transient absorption spectroscopy.clos

Prediction of monthly Arctic sea ice concentrations using satellite and reanalysis data based on convolutional neural networks

Changes in Arctic sea ice affect atmospheric circulation, ocean current, and polar ecosystems. There have been unprecedented decreases in the amount of Arctic sea ice due to global warming. In this study, a novel 1-month sea ice concentration (SIC) prediction model is proposed, with eight predictors using a deep-learning approach, convolutional neural networks (CNNs). This monthly SIC prediction model based on CNNs is shown to perform better predictions (mean absolute error - MAE - of 2.28 %, anomaly correlation coefficient - ACC - of 0.98, root-mean-square error - RMSE - of 5.76 %, normalized RMSE - nRMSE - of 16.15 %, and NSE - Nash-Sutcliffe efficiency - of 0.97) than a random-forest-based (RF-based) model (MAE of 2.45 %, ACC of 0.98, RMSE of 6.61 %, nRMSE of 18.64 %, and NSE of 0.96) and the persistence model based on the monthly trend (MAE of 4.31 %, ACC of 0.95, RMSE of 10.54 %, nRMSE of 29.17 %, and NSE of 0.89) through hindcast validations. The spatio-temporal analysis also confirmed the superiority of the CNN model. The CNN model showed good SIC prediction results in extreme cases that recorded unforeseen sea ice plummets in 2007 and 2012 with RMSEs of less than 5.0 %. This study also examined the importance of the input variables through a sensitivity analysis. In both the CNN and RF models, the variables of past SICs were identified as the most sensitive factor in predicting SICs. For both models, the SIC-related variables generally contributed more to predict SICs over ice-covered areas, while other meteorological and oceanographic variables were more sensitive to the prediction of SICs in marginal ice zones. The proposed 1-month SIC prediction model provides valuable information which can be used in various applications, such as Arctic shipping-route planning, management of the fishing industry, and long-term sea ice forecasting and dynamics

リチウム二次電池に向けたマイクロメータ厚さのシリコン系多孔質負極の急速蒸着技術の開発

学位の種別: 課程博士審査委員会委員 : （主査）東京大学教授 山田 淳夫, 東京大学教授 堂免 一成, 東京大学准教授 辻 佳子, 東京大学准教授 大久保 將史, 早稲田大学教授 門間 聰之, 早稲田大学教授 野田 優University of Tokyo(東京大学

Delineation of high resolution climate regions over the Korean Peninsula using machine learning approaches

In this research, climate classification maps over the Korean Peninsula at 1 km resolution were generated using the satellite-based climatic variables of monthly temperature and precipitation based on machine learning approaches. Random forest (RF), artificial neural networks (ANN), k-nearest neighbor (KNN), logistic regression (LR), and support vector machines (SVM) were used to develop models. Training and validation of these models were conducted using in-situ observations from the Korea Meteorological Administration (KMA) from 2001 to 2016. The rule of the traditional Koppen-Geiger (K-G) climate classification was used to classify climate regions. The input variables were land surface temperature (LST) of the Moderate Resolution Imaging Spectroradiometer (MODIS), monthly precipitation data from the Tropical Rainfall Measuring Mission (TRMM) 3B43 product, and the Digital Elevation Map (DEM) from the Shuttle Radar Topography Mission (SRTM). The overall accuracy (OA) based on validation data from 2001 to 2016 for all models was high over 95%. DEM and minimum winter temperature were two distinct variables over the study area with particularly high relative importance. ANN produced more realistic spatial distribution of the classified climates despite having a slightly lower OA than the others. The accuracy of the models using high altitudinal in-situ data of the Mountain Meteorology Observation System (MMOS) was also assessed. Although the data length of the MMOS data was relatively short (2013 to 2017), it proved that the snowy, dry and cold winter and cool summer class (Dwc) is widely located in the eastern coastal region of South Korea. Temporal shifting of climate was examined through a comparison of climate maps produced by period: from 1950 to 2000, from 1983 to 2000, and from 2001 to 2013. A shrinking trend of snow classes (D) over the Korean Peninsula was clearly observed from the ANN-based climate classification results. Shifting trends of climate with the decrease/increase of snow (D)/temperate (C) classes were clearly shown in the maps produced using the proposed approaches, consistent with the results from the reanalysis data of the Climatic Research Unit (CRU) and Global Precipitation Climatology Centre (GPCC)

Arctic lead detection using a waveform mixture algorithm from CryoSat-2 data

We propose a waveform mixture algorithm to detect leads from CryoSat-2 data, which is novel and different from the existing threshold-based lead detection methods. The waveform mixture algorithm adopts the concept of spectral mixture analysis, which is widely used in the field of hyperspectral image analysis. This lead detection method was evaluated with high-resolution (250 m) MODIS images and showed comparable and promising performance in detecting leads when compared to the previous methods. The robustness of the proposed approach also lies in the fact that it does not require the rescaling of parameters (i.e., stack standard deviation, stack skewness, stack kurtosis, pulse peakiness, and backscatter sigma(0)), as it directly uses L1B waveform data, unlike the existing threshold-based methods. Monthly lead fraction maps were produced by the waveform mixture algorithm, which shows interannual variability of recent sea ice cover during 2011-2016, excluding the summer season (i.e., June to September). We also compared the lead fraction maps to other lead fraction maps generated from previously published data sets, resulting in similar spatiotemporal patterns