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Synthesis of High-energy density Li- and Mn-rich Cathode Materials via Morphology Control and Surface Modification
Get PDFSchool of Energy and Chemical Engineering (Energy Engineering (Battery Science and Technology))Li- and Mn- rich cathodes (LMRs) are considered as next-generation cathode materials with their high theoretical specific capacity to realize high-energy-density Li-ion batteries. However, LMRs have difficulty in practical applications due to their intrinsic problems. Generally synthesized LMRs with small particle size have trouble achieving high volumetric energy density. Also, the presence of Li2MnO3 phase which requires chemical activation process at a high-voltage range triggers chemical irreversibility, resulting in low initial Coulombic efficiency (I.C.E), severe voltage decay and low rate capability. In order to solve these problems, a simultaneous modification is introduced to the particles: morphology control and surface coating. Flake-type morphology with increased primary particle size not only enables to achieve high electrode density nearly up to 3.0gcc-1, but also reduces the contact area with electrolyte to enhance the cycle stability. The flake-type LMRs exhibit a good capacity retention of 95.9% and a low voltage decay of 0.045V after 40 cycles. High irreversibility caused by large particle size is improved by the surface coating with AlF3. The Li2MnO3 phase can be activated rapidly in the first cycle with the AlF3 coating layer, which makes the initial reversible specific capacity much higher. As a result, AlF3-coated LMRs with flake-type shape show high I.C.E of 84.6% with a superior discharge capacity of 265mAh/g. Moreover, it is demonstrated that the AlF3 coating layer prevents the severe phase transition to the rock salt phase by mitigating the extraction of excess lithium ions and oxygen gas at the interface. This work suggests that AlF3 surface coating on flake-type shaped particles is an effective strategy for achieving both the high volumetric energy density and high performance LMR cathode materials.ope
What derives less readable 10-K reports: Investor vs Firm-complexity
Get PDFSchool of Business Administration (Management Engineering)In this study, I suggest a new channel to explain the negative association between the readability of 10-K reports and firm???s stock price volatility. I find that a high level of firm-complexity influences a decrease in readability of 10-K reports as a firm???s future uncertainty increases. Firms with higher complexity, such as growth firms, release more hard-to-read 10-K reports. The new suggested channel, firm-complexity channel, better explains poor readability of 10-K reports.ope
Collision-free active sensing for maximum seeking of unknown environment fields with Gaussian processes
No full textThis paper presents a collision-free active sensing algorithm that safely and efficiently searches for the max-imum point while reconstructing the unknown environment field. Bayesian optimization (BO) for optimizing the unknown function with Gaussian processes (GPs) is used for active sensing with a new acquisition function. Besides, the mobile sensor estimates Euclidean signed distance field using GPs to avoid obstacles with its fast collision checking capability. To mitigate the local maximum problem, Monte Carlo tree search (MCTS), one of state-of-the-art planning techniques, is adopted as a non-myopic planner. In particular, obstacle avoidance and active sensing are integrated into a unified framework using a safe BO algorithm (known as SafeOpt-MC) based on GPs and MCTS. Numerical simulations are performed to validate the feasibility and performance of the proposed framework with a diverse set of environments
Development of flapping wing robot and vision-based obstacle avoidance strategy
No full textDue to the flight characteristics such as small size, low noise, and high efficiency, studies on flapping wing robots are being actively conducted. In particular, the flapping wing robot is in the spotlight in the field of search and reconnaissance. Most of the research focuses on the development of flapping wing robots rather than autonomous flight. However, because of the unique characteristics of flapping wings, it is essential to consider the development of flapping wing robots and autonomous flight simultaneously. In this article, we describe the development of the flapping wing robot and computationally efficient vision-based obstacle avoidance algorithm suitable for the lightweight robot. We developed a 27 cm and 45 g flapping wing robot named CNUX Mini that features an X-type wing and tailed configuration to attenuate oscillation caused by flapping motion. The flight experiment showed that the robot is capable of stable flight for 1.5 min and changing its direction with a small turn radius in a slow forward flight condition. For the obstacle detection algorithm, the appearance variation cue is used with the optical flow-based algorithm to cope robustly with the motion-blurred and feature-less images obtained during flight. If the obstacle is detected during straight flight, the avoidance maneuver is conducted for a certain period, depending on the state machine logic. The proposed obstacle avoidance algorithm was validated in ground tests using a testbed. The experiment shows that the CNUX Mini performs a suitable evasive maneuver with 90.2% success rate in 50 incoming obstacle situations
Dual-gate thin film transistor lactate sensors operating in the subthreshold regime
No full textOrganic thin-film transistors (TFTs) with an electrochemically functionalized sensing gate are promising platforms for wearable health-monitoring technologies because they are light, flexible, and cheap. Achieving both high sensitivity and low power is highly demanding for portable or wearable devices. In this work, we present flexible printed dual-gate (DG) organic TFTs operating in the subthreshold regime with ultralow power and high sensitivity. The subthreshold operation of the gate-modulated TFT-based sensors not only increases the sensitivity but also reduces the power consumption. The DG configuration has deeper depletion and stronger accumulation, thereby further making the subthreshold slope sharper. We integrate an enzymatic lactate-sensing extended-gate electrode into the printed DG TFT and achieve exceptionally high sensitivity (0.77) and ultralow static power consumption (10 nW). Our sensors are successfully demonstrated in physiological lactate monitoring with human saliva. The accuracy of the DG TFT sensing system is as good as that of a high-cost conventional assay. The developed platform can be readily extended to various materials and technologies for high performance wearable sensing applications
Machine Learning for Object Recognition in Manufacturing Applications
No full textFeature recognition and manufacturability analysis from computer-aided design (CAD) models are indispensable technologies for better decision making in manufacturing processes. It is important to transform the knowledge embedded within a CAD model to manufacturing instructions for companies to remain competitive as experienced baby-boomer experts are going to retire. Automatic feature recognition and computer-aided process planning have a long history in research, and recent developments regarding algorithms and computing power are bringing machine learning (ML) capability within reach of manufacturers. Feature recognition using ML has emerged as an alternative to conventional methods. This study reviews ML techniques to recognize objects, features, and construct process plans. It describes the potential for ML in object or feature recognition and offers insight into its implementation in various smart manufacturing applications. The study describes ML methods frequently used in manufacturing, with a brief introduction of underlying principles. After a review of conventional object recognition methods, the study discusses recent studies and outlooks on feature recognition and manufacturability analysis using ML
Superslippery Long-Chain Entangled Polydimethylsiloxane Gel with Sustainable Self-Replenishment
No full textInspired by mucus-secreting organisms, biomimetic slippery surfaces have been studied in various engineering fields. The liquid-infused polymer surface (LIPS) has received considerable interest because of its ability to store lubricants inside the polymer itself, facile fabrication, and high scalability. However, the conventional LIPS easily loses its slippery property owing to its inability to secrete lubricants to the surface. In this study, a long-chain entangled polydimethylsiloxane (LEP) gel is proposed as a superslippery functional surface with sustainable self-replenishment. The developed LEP gel has large lubricant storage spaces and exhibits an extremely low sliding angle close to 0 degrees because of the low-viscosity oil layer formed on the surface. In addition, although conventional LIPSs easily undergo lubricant drought on their surfaces, the proposed LEP gel continuously secretes low-viscosity oil to the surface. The LEP gel with a superslippery surface shows nearly perfect antifouling performance and reduces 99.97% of bacteria compared with pure polydimethylsiloxane surface. It maintains slippery performance without deterioration even after exposure to harsh conditions, such as high-pressure and high-speed shear flow. The outstanding slippery performance of the proposed LEP gel would be usefully utilized in various engineering fields after further improvement in the future
Experimentally Validated Analytical Solutions to Homogeneous Problems of Electrical Impedance Tomography (EIT) on Rectangular Cement-Based Materials
No full textDiagnostic technologies using X-rays and/or acoustic emissions for concrete infrastructures containing internal pores, defects, and cracks have attracted considerable interest. However, computerized tomography (CT) for concrete is challenging due to its radiation shielding characteristics. Electrical impedance tomography (EIT), initially developed for medical use, has recently shown a potential for developing a macro-CT technique for concrete structures. This study derived EIT analytical solutions for rectangular cement-based samples and validated them with experimental data obtained from cubic mortar samples. The experimental validation of the three mathematical functions (Dirac delta, Heaviside step, and Gaussian) used as current injection models, the Gaussian function produced the lowest relative absolute error (4.02%). This study also explored appropriate experimental setups for cement-based materials, such as Shunt model, current flow paths, and potential distribution
Deep-learning based spatio-temporal generative model on assessing state-of-health for Li-ion batteries with partially-cycled profiles
No full textAccurately estimating the state-of-health (SOH) of lithium-ion batteries is emerging as a hot topic because of the rapid increase in electric appliance usage. However, versatile applicability to various battery compositions and diverse cycling conditions, and prediction only with partial data still remain challenges. In this paper, a Deep-learning-based Graphical approach to Estimation of Lithium-ion batteries SOH (D-GELS) was developed to predict the SOH covering three cathode materials, LiFePO4, LiNiCoAlO2, and LiNiCOMnO2. D-GELS shows an accurate performance for SOH prediction, less than 0.012 of RMSE, was predicted regardless of cathode materials, and its applicability was confirmed. Furthermore, D-GELS was capable of predicting the SOH using partially-cycled data, since less than 0.046 of RMSE was observed even with 50% of the image missing. When using partially-cycled profiles, significant economic benefits can be seen in used battery management, as the number of assessed batteries increases greatly, leading to cost savings
Deep-learning optimized DEOCSU suite provides an iterable pipeline for accurate ChIP-exo peak calling
No full textRecognizing binding sites of DNA-binding proteins is a key factor for elucidating transcriptional regulation in organisms. ChIP-exo enables researchers to delineate genome-wide binding landscapes of DNA-binding proteins with near single base-pair resolution. However, the peak calling step hinders ChIP-exo application since the published algorithms tend to generate false-positive and false-negative predictions. Here, we report the development of DEOCSU (DEep-learning Optimized ChIP-exo peak calling SUite), a novel machine learning-based ChIP-exo peak calling suite. DEOCSU entails the deep convolutional neural network model which was trained with curated ChIP-exo peak data to distinguish the visualized data of bona fide peaks from false ones. Performance validation of the trained deep-learning model indicated its high accuracy, high precision and high recall of over 95%. Applying the new suite to both in-house and publicly available ChIP-exo datasets obtained from bacteria, eukaryotes and archaea revealed an accurate prediction of peaks containing canonical motifs, highlighting the versatility and efficiency of DEOCSU. Furthermore, DEOCSU can be executed on a cloud computing platform or the local environment. With visualization software included in the suite, adjustable options such as the threshold of peak probability, and iterable updating of the pre-trained model, DEOCSU can be optimized for users??? specific needs