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Integrated System Built for Small-Molecule Semiconductors via High-Throughput Approaches
No full textHigh-throughput synthesis of solution-processable structurallyvariable small-molecule semiconductors is both an opportunity anda challenge. A large number of diverse molecules provide a possibilityfor quick material discovery and machine learning based on experimentaldata. However, the diversity of the molecular structure leads to thecomplexity of molecular properties, such as solubility, polarity,and crystallinity, which poses great challenges to solution processingand purification. Here, we first report an integrated system for thehigh-throughput synthesis, purification, and characterization of moleculeswith a large variety. Based on the principle "Like dissolveslike," we combine theoretical calculations and a robotic platformto accelerate the purification of those molecules. With this platform,a material library containing 125 molecules and their optical-electronicproperties was built within a timeframe of weeks. More importantly,the high repeatability of recrystallization we design is a reliableapproach to further upgrading and industrial production
A Whole-Body Integrated AVATAR System: Implementation of Telepresence with Intuitive Control and Immersive Feedback
No full textValidation of the Korean version of the Multidimensional Psychological Flexibility Inventory (K-MPFI) through assessment of university students
No full text??? ????????? ????????? ???????????? ????????? ????????? ??????(MPFI)??? ???????????? ???????????? ??????????????? ???????????? ???????????? ???????????? ????????? ???????????? ???????????? ????????? ????????? ??????(K-MPFI)??? ???????????? ?????????. ???????????? ????????? MPFI ???????????? ??????????????? ????????? ???????????? ?????????, ????????? ????????? ???????????? ????????? ???????????? ????????? ??????????????? 1??? ????????? ???????????? ???????????? ???????????? ????????? ????????? ?????? ????????? ?????? ???????????? ????????? ???????????? ?????? ????????? ???????????? ????????????. ??????????????? ???????????? ????????? ?????? ???????????? MPFI(K-MPFI)?????? ????????? ???????????????. ???????????? ????????? ????????? ?????? ????????? ??? 458?????? ???????????????. ???????????? ????????? K-MPFI ????????? ???????????????(????????? ??????-?????? ????????? ??? 2???, ????????? ????????? ??????) ??? ???????????????(????????? ????????????, ??????????????? ????????????, ????????? ????????? ?????? ??????-?????????)??? ???????????? ?????? ????????? ???????????????. ????????? ???????????? ??????, ?????? MPFI??? ??????????????? 2?????? ?????? ??????(????????? ????????? ?????????, ????????? ????????? ?????????)??? 12?????? ?????? ??????(??????, ?????? ??????, ?????????????????? ??????, ?????????, ??????, ????????????, ????????? ??????, ?????? ????????? ?????? ?????? ??????, ?????????????????? ??????, ??????, ????????????)??? ???????????????. K-MPFI??? ?????? ?????? ??? ?????? ???????????? ?????? ?????? ???????????? ????????????, ??????????????? ?????? ????????? ?????????????????? ??????????????? ?????? ????????? ????????? ????????????. ??????, ????????? ????????? ???????????? ?????? ?????? ??? ????????? ????????? ????????? ?????? ????????? ???????????? ?????? ?????? ???????????? ????????? ????????? ????????? ?????? ????????? ??????????????? ??????????????? ???????????? ?????????. ????????? ????????? ??? ???????????? ????????? ?????? ???????????? ????????? ??????????????? ???????????? ??????????????? ?????? ????????? ??????. ????????? ????????? ????????? ?????? ???????????? ????????? ????????? ?????? MPFI??? ????????? ?????? ????????? ?????? ?????? ????????? ????????? ????????????. ????????? ?????? ???????????? ????????? ????????????. ???????????????, ??? ????????? ???????????? ????????? ????????? ????????????
Fabrication of Curvy Devices and Fully Soft Electronics for Future Wearable Applications
No full textUnveiling the origin of n-type doping of natural MoS2: carbon
No full textMoS2 has attracted intense interest in many applications. Natural MoS2 and field-effect transistors made of it generally exhibit n-type characteristics, but its origin is unknown. Herein, we show that C is the origin of the universal n-type doping of natural MoS2. Photoemission spectroscopies reveal that while many MoS2 samples with C detected are n-type, some without C exhibit p-type characteristics. The C-free, p-type MoS2 changes to n-type over time with the concurrent appearance of C that is out-diffused from bulk, indicating that C induces the n-type doping. The C-origin is verified by C-deposition and supported by theoretical calculations. This carbon appears as nanometer-scale defects frequently observed in scanning tunneling microscopy. In addition, we propose, based on the calculations, that S vacancies are responsible for the p-type characteristics, which contrasts with the widespread belief. This work provides new perspectives on MoS2 doping and presents a new direction for fabricating reliable MoS2 devices
Predicting photoresist sensitivity using machine learning
No full textWe introduce a scheme for predicting photoresist sensitivity using machine learning (ML) work flow on the basis of previously reported experimental data. Different ML models, specifically Linear Regression, Kernel Ridge Regression, Gaussian Process Regressor, Random Forest Regressor, and Multilayer Perceptron Regressor, were evaluated to rapidly identify the best sensitivity prediction model. The experiment was carried out on the Google Colab platform using the Materials Simulation Toolkit for Machine Learning and Sci-kit Learn. Different ensemble models were utilized without splitting the dataset to determine the prediction accuracy of the ML models. The hyperparameter optimization was established with a 70/30 ratio, followed by a K-Fold cross-validation to improve the model prediction performance. The optimized ML model showed a prediction performance of R-2 = 0.83, RMSE = 10.53, and MARE = 0.68. Hence, by optimizing the hyperparameters used in the ML model, the sensitivity of the photoresist materials can be predicted with improved prediction performance
Water-repellent and self-attachable flexible conductive patch
No full textAchieving exceptional water-repellency and reliable reversible adhesion is crucial for the development of wearable flexible electronics. However, simultaneously achieving these properties presents a significant challenge, as water-repellency requires maximizing the presence of air while robust adhesion necessitates enhancing the solid fraction. In this study, we present a flexible and transparent conductive patch that addresses this challenge by offering simultaneous robust superhydrophobicity and strong adhesion in both dry and wet conditions. The device incorporates a unique combination of overhang micropillars, microgrids and a percolating network of carbon nanotubes. The proposed patch demonstrates outstanding water repellency with a contact angle exceeding 150 degrees, while delivering impressive dry adhesion (>200 kPa) and wet adhesion (>150 kPa) performance. Furthermore, the device exhibits tunable electrical conductivity and optical transmittance
Computational modeling of learning mechanisms and decision biases in finite sequential decision-making
No full textCatalyst Design of CO2 hydrogenation and Off-gas Upgrading
No full textThe steady increase of CO2 in the atmosphere owing to the excessive use of fossil fuels is seriously threatening the future of humankind by accelerating climate change. Among the proposed a carbon capture and utilization options, catalytic CO2 hydrogenation is attractive because the process is very similar to the well-established CO hydrogenation. The CO2 hydrogenation process usually involves two consecutive steps: the reverse water???gas shift (RWGS) and subsequent CO hydrogenation reactions. In CO2 hydrogenation over Fe-based catalysts, the Fe3O4 phase catalyzes the RWGS reaction, and its reduced form, the Ha??gg iron carbide (??-Fe5C2) phase, provides active sites for CO hydrogenation and chain growth. CO2 hydrogenation can produce a wide variety of products, including methane, light olefins, and heavier (C5+) hydrocarbons. Gaseous by-products produced by CO2 hydrogenation reaction contain large amounts of light olefins and paraffins (C2???C4). Hydroformylation not only simplifies the separation process by converting only olefins to liquid aldehydes and alcohols but also produces high-value-added chemicals with high commercial value. The newly developed Rh/CeO2-Al2O3 single atom catalyst was applied to upgrade the off-gas generated from CO2 hydrogenation by performing hydroformylation using a olefin/paraffin mixed gas