Li metal anode is the “Holy Grail” material of advanced Lithium-ion-batteries (LIBs). However, it is plagued by uncontrollable dendrite growth resulting in poor cycling efficiency and short-circuiting of batteries. This has spurred a plethora of research to understand the underlying mechanism of dendrite formation. While experimental studies suggest that there are complex physical and chemical interactions between heterogeneous solid-electrolyte interphase (SEI) and dendrite growth, most of the studies do not reveal the mechanisms triggering these interactions. To deal with this knowledge gap, we propose a multiscale modeling framework which couples kinetic Monte Carlo and Molecular Dynamics simulations. Specifically, the model has been developed to account for (a) heterogeneous SEI, (b) dendrite-SEI interactions, and (c) effect of electrolyte on Li electrodeposition and potential dendrite formation. This allows the proposed computational model to be extended to various electrolytes and SEI species and generate results consistent with previous experimental studies. 相似文献
Two types of multi-walled carbon nanotube (MWNT)-based elastomer nanocomposites are used as a sensor material for the detection of gasoline spills by applying the interdigitated electrode (IDE) device. MWNT-g-polyisoprene (PI) and Si-MWNT/natural rubber (NR) are prepared by applying “grafting-from” and “grafting-to” process, respectively. When compared based on the identical condition of gasoline sensing test, the maximum response value to the exposure of gasoline is 17.5 for MWNT-g-PI sensor and 12.9 for Si-MWNT/NR sensor, which reach the maximum in less than 3 min. The MWNT-g-PI sensor selectively detects gasoline, and its response is completely reversible. It shows that the longer chain length of PI brings about the larger response of MWNT-g-PI sensor to gasoline. The sensitivity of MWNT-g-PI sensor highly depends on both how much gasoline is exposed to the sensor and what bias voltage is applied to the IDE device. The IDE sensor using MWNT-g-PI nanocomposites effectively detects gasoline spills. 相似文献
International Journal of Information Security - This paper deals with a well-known problem in the area of the smudge attacks: when a user draws a pattern to unlock the pattern lock on a smartphone... 相似文献
First examples of multichain (polycatenar) compounds, based on the π-conjugated [1]benzothieno[3,2-b]benzothiophene unit are designed, synthesized, and their soft self-assembly and charge carrier mobility are investigated. These compounds, terminated by the new fan-shaped 2-brominated 3,4,5-trialkoxybenzoate moiety, form bicontinuous cubic liquid crystalline (LC) phases with helical network structure over extremely wide temperature ranges (>200 K), including ambient temperature. Compounds with short chains show an achiral cubic phase with the double network, which upon increasing the chain length, is at first replaced by a tetragonal 3D phase and then by a mirror symmetry is broken triple network cubic phase. In the networks, the capability of bypassing defects provides enhanced charge carrier mobility compared to imperfectly aligned columnar phases, and the charge transportation is non-dispersive, as only rarely observed for LC materials. At the transition to a semicrystalline helical network phase, the conductivity is further enhanced by almost one order of magnitude. In addition, a mirror symmetry broken isotropic liquid phase is formed beside the 3D phases, which upon chain elongation is removed and replaced by a hexagonal columnar LC phase. 相似文献
Journal of Applied Electrochemistry - The detection of biologically important metal ions such as Cu(II) and Pb(II) ions using an electrochemical approach at sensitive level is gaining great... 相似文献
Food Science and Biotechnology - Rosa rugosa root is traditionally known to be effective in the treatment of diabetes in Korea. R. rugosa root-specific compounds also show antioxidant effects, and... 相似文献
The quality monitoring and control (QMC) has been an essential process in the manufacturing industries. With the advancements in data analytics, machine-learning based QMC has become popular in various manufacturing industries. At the same time, the cost effectiveness (CE) of the QMC is perceived as a main decision criterion that explicitly accounts for inspection efforts and has a direct relationship with the QMC capability. In this paper, the cost-effective support vector machine (CESVM)-based automated QMC system (QMCS) is proposed. Unlike existing models, the proposed CESVM explicitly incorporates inspection-related expenses and error types in the SVM algorithm. The proposed automated QMCS is verified and validated using an automotive door-trim manufacturing process. Next, we perform a design of experiment to assess the sensitivity analysis of the proposed framework. The proposed model is found to be effective and could be viewed as an alternative or complementary tool for the traditional quality inspection system.
Wireless Personal Communications - In cloud-assisted data outsourcing systems, the privacy of sensitive data is a major concern. Thus, data are uploaded in encrypted form in many cloud applications... 相似文献