Study on advanced electrolytes for improving electrochemical performance of sodium and lithium metal batteries

Department of Energy Engineering (Battery Science and Technology)Alkali metals such as lithium (Li) and sodium (Na) have been considered as an ideal anode for rechargeable batteries in next generation. Li metal has the high specific capacity of 3860 mAh g-1 with reduction potential of -3.04 (versus standard hydrogen electrode) and low density of 0.534 g cm-3. And Na metal has the high specific capacity of 1165 mAh g-1 with reduction potential of -2.71 V (versus standard hydrogen electrode). Nevertheless, the practical application of Na metal and Li metal batteries is quite challenging because the high chemical and electrochemical reactivity of Na metal and Li metal electrodes with organic liquid electrolytes leads to low Coulombic efficiencies and limited cycling performance. Severe electrolyte decomposition at the reactive metal electrode results in the formation of a resistive and nonuniform surface film, leading to dendritic metal growth. To control the Na metal and Li metal electrode???electrolyte interfaces for high performance Na metal and Li metal batteries, considerable efforts will be made to find electrolyte systems that are stable at the metal electrode. Moreover, the underlying mechanism of electrolytes at the electrode???electrolyte interface will be clearly elucidated through electrochemical method and characterization of the electrode???electrolyte interface. I) For the electrolytes of Na metal batteries, degradation mechanism of Na metal in conventional carbonate???based electrolyte is studied. To mitigate the parasitic reaction between Na metal anode and electrolyte, fluoroethylene carbonate (FEC) is employed as additive and solvent in electrolyte to construct the protective layer on Na metal anode. The underlying mechanism of FEC at the electrode???electrolyte interface is clearly demonstrated by 13C nuclear magnetic resonance, X-ray photoelectron spectroscopy, in???situ differential electrochemical microscopy and in???situ optical microscopy. II) For the electrolytes of Li metal batteries, fluorinated compounds can be employed as interface modifiers to extend the applicable voltage range of ether-based electrolytes, which are commonly used in Li metal batteries with charging cut-off voltages of lower than 4 V (vs. Li/Li+). In particular, we reveal that 1,1,2,2-tetrafluoroethyl-2,2,3,3-tetrafluoropropyl ether promotes the construction of a solid electrolyte interphase as a shield accommodating the destructive stress induced by Li plating and stripping on the Li metal anode, while FEC makes the interface of the Ni-rich cathode electrochemically robust and prevents severe intergranular cracking of the cathode during pre-cycling. Thus, this study provides a promising method of tackling the reductive and oxidative decomposition of labile ether-based electrolytes and allows one to enhance the electrochemical performance of Li metal anodes and Ni-rich cathodes.ope

Doctor of Philosophy

dissertationRemodeling of cell surface to install new features has continuously attracted attention for cell therapy. This dissertation focuses on a method of cell surface engineering using bioactive molecules to transiently award distinct functions to ordinary cells. Spontaneous incorporation of lipid-conjugated biomaterials to the cell membrane through hydrophobic interaction provides the basis for noninvasive cell surface modification. First, mesenchymal stem cells (MSCs) were surface-engineered to embed a recombinant protein, stromal-derived factor 1 (SDF-1), for an enhanced target-specific homing effect. The SDF-1-embedded MSCs showed augmented migration towards the concentration gradient of their molecular target, CXC chemokine receptor 4 (CXCR4). Next, Jurkat cells were surface-engineered with magnetic resonance imaging (MRI) contrast agents to demonstrate the suitability of surface engineering in cell tracking. The contrast agent-embedded Jurkat cells were detectable by MRI. To demonstrate the applicability of this technology in translational research, immune effector cells were surface-engineered with antibody-drug conjugates (ADCs) and their combined efficacy was examined in animal tumor models. This combination of chemotherapy and immunotherapy showed significant efficacy in treating cancers; however, the immunomodulatory effects of chemotherapy were difficult to control. This observation was due to the off-target toxicity of chemotherapy that damages the host immune cells: many cancer patients often require replenishment of immune cells after a series of chemotherapy in order to benefit from immunotherapy. In order to overcome the challenge, new chemoimmunotherapeutic strategies require sufficient immunomodulatory ability of chemotherapy, targeted chemotherapy for reduced toxicity, and enhanced recruitment of immune cells to the tumor tissue. Surface engineering to affix chemotherapeutic agents on the cell membrane of immune effector cells is therefore an attractive approach. In the main study of this dissertation, natural killer 92 (NK92) cells were surface-engineered to carry ADCs on their membranes. A lipid-conjugated model ADC, trastuzumab-DM1 (T-DM1), homogeneously modified the allogeneic NK92 cells without affecting the viability of NK92 cells. T-DM1-embedded NK92 (SE-NK/T-DM1) cells exerted strong anti-cancer activity through targeted chemoimmunotherapy. Although a wide range of experimental observations has proven that the SE-NK/T-DM1 cells are effective over the co-treatment of T-DM1 and NK92 cells, further investigations should be conducted to validate their potential for clinical application

CO2 Flux from Tundra Lichen, Moss, and Tussock, Council, Alaska: Assessment of Spatial Representativeness

CO2 flux-measurement in dominant tundra vegetation on the Seward Peninsula of Alaska was examined for spatial representativeness, using a manual chamber system. In order to assess the representativeness of CO2 flux, a 40 m × 40 m (5-m interval; 81 total points) plot was used in June, August, and September of 2011. Average CO2 fluxes in lichen, moss, and tussock tundra were 3.4 ± 2.7, 4.5 ± 2.9, and 7.2 ± 5.7 mgCO2/m2/m during growing season, respectively, suggesting that tussock tundra is a significant CO2 source, especially considering the wide distribution of tussock tundra in the circumpolar region. Further, soil temperature, rather than soil moisture, held the key role in regulating CO2 flux at the study site: CO2 flux from tussock increased linearly as soil temperature increased, while the flux from lichen and moss followed soil temperature nearly exponentially, reflecting differences in surface area covered by the chamber system. Regarding sample size, the 81 total sampling points over June, August, and September satisfy an experimental average that falls within ±10% of full sample average, with a 95% confidence level. However, the number of sampling points for each variety of vegetation during each month must provide at least ±20%, with an 80% confidence level. In order to overcome the logistical constraints, we were required to identify the site’s characteristics with a manual chamber system over a 40 m × 40 m plot and to subsequently employ an automated chamber for spatiotemporal representativeness.This work was supported by the National Research Foundation of Korea Grant funded by the Korean Government (MEST) (NRF-C1ABA001-2011-0021063

Applying Topographic Classification, Based on the Hydrological Process, to Design Habitat Linkages for Climate Change

The use of biodiversity surrogates has been discussed in the context of designing habitat linkages to support the migration of species affected by climate change. Topography has been proposed as a useful surrogate in the coarse-filter approach, as the hydrological process caused by topography such as erosion and accumulation is the basis of ecological processes. However, some studies that have designed topographic linkages as habitat linkages, so far have focused much on the shape of the topography (morphometric topographic classification) with little emphasis on the hydrological processes (generic topographic classification) to find such topographic linkages. We aimed to understand whether generic classification was valid for designing these linkages. First, we evaluated whether topographic classification is more appropriate for describing actual (coniferous and deciduous) and potential (mammals and amphibians) habitat distributions. Second, we analyzed the difference in the linkages between the morphometric and generic topographic classifications. The results showed that the generic classification represented the actual distribution of the trees, but neither the morphometric nor the generic classification could represent the potential animal distributions adequately. Our study demonstrated that the topographic classes, according to the generic classification, were arranged successively according to the flow of water, nutrients, and sediment; therefore, it would be advantageous to secure linkages with a width of 1 km or more. In addition, the edge effect would be smaller than with the morphometric classification. Accordingly, we suggest that topographic characteristics, based on the hydrological process, are required to design topographic linkages for climate change

Deep learning-based classification with improved time resolution for physical activities of children

Background The proportion of overweight and obese people has increased tremendously in a short period, culminating in a worldwide trend of obesity that is reaching epidemic proportions. Overweight and obesity are serious issues, especially with regard to children. This is because obese children have twice the risk of becoming obese as adults, as compared to non-obese children. Nowadays, many methods for maintaining a caloric balance exist; however, these methods are not applicable to children. In this study, a new approach for helping children monitor their activities using a convolutional neural network (CNN) is proposed, which is applicable for real-time scenarios requiring high accuracy. Methods A total of 136 participants (86 boys and 50 girls), aged between 8.5 years and 12.5 years (mean 10.5, standard deviation 1.1), took part in this study. The participants performed various movement while wearing custom-made three-axis accelerometer modules around their waists. The data acquired by the accelerometer module was preprocessed by dividing them into small sets (128 sample points for 2.8 s). Approximately 183,600 data samples were used by the developed CNN for learning to classify ten physical activities : slow walking, fast walking, slow running, fast running, walking up the stairs, walking down the stairs, jumping rope, standing up, sitting down, and remaining still. Results The developed CNN classified the ten activities with an overall accuracy of 81.2%. When similar activities were merged, leading to seven merged activities, the CNN classified activities with an overall accuracy of 91.1%. Activity merging also improved performance indicators, for the maximum case of 66.4% in recall, 48.5% in precision, and 57.4% in f1 score . The developed CNN classifier was compared to conventional machine learning algorithms such as the support vector machine, decision tree, and k-nearest neighbor algorithms, and the proposed CNN classifier performed the best: CNN (81.2%) > SVM (64.8%) > DT (63.9%) > kNN (55.4%) (for ten activities); CNN (91.1%) > SVM (74.4%) > DT (73.2%) > kNN (65.3%) (for the merged seven activities). Discussion The developed algorithm distinguished physical activities with improved time resolution using short-time acceleration signals from the physical activities performed by children. This study involved algorithm development, participant recruitment, IRB approval, custom-design of a data acquisition module, and data collection. The self-selected moving speeds for walking and running (slow and fast) and the structure of staircase degraded the performance of the algorithm. However, after similar activities were merged, the effects caused by the self-selection of speed were reduced. The experimental results show that the proposed algorithm performed better than conventional algorithms. Owing to its simplicity, the proposed algorithm could be applied to real-time applicaitons

Continuous monitoring of soil gas efflux with Forced Diffusion (FD) chamber technique in a tundra ecosystem, Alaska

We deployed the FD chamber system in a tundra ecosystem over the discontinuous permafrost regime of Council, Alaska. The representative understory plants are tussock (17 %), lichen (32 %), and moss (51 %), within a 40 × 40 m plot at an interval of five meters (81 points total) for efflux-measurement by dynamic chamber. The FD chamber monitored soil CO2 effluxes from moss, lichen, and tussock regimes at an interval of 30 min during the growing season of 2015. As the results, mean soil CO2 effluxes in intact and infected sphagnum moss, lichen, and tussock were 0.42 ± 0.17, 0.39 ± 0.22, 0.76 ± 0.21, and 0.87 ± 0.41 μmol/m2/s during June 25 to September 21 2015, respectively. Mean simulated soil CO2 efflux normalized by air temperature of 10°C were 0.40 ± 0.17, 0.36 ± 0.16, 0.77 ± 0.13, and 0.85 ± 0.30 μmol/m2/s from four plants, respectively, suggesting there are not significant differences between measured and simulated CO2 effluxes.This study was supported by a National Research Foundation of Korea grant funded by the South Korean Government (MSIP) (NRF-C1ABA001-2011-0021063) (Establishment of Circum-Arctic Permafrost Environment Change Monitoring Network and Future Prediction Techniques (CAPEC Project)). This research was conducted under the JAMSTEC-IARC Collaboration Study, with funding provided by the Japan Agency for Marine-Earth Science and Technology (JAMSTEC)

TRAF2 Is Essential for JNK but Not NF-κB Activation and Regulates Lymphocyte Proliferation and Survival

AbstractTRAF2 is believed to mediate the activation of NF-κB and JNK induced by the tumor necrosis factor receptor (TNFR) superfamily, which elicits pleiotropic responses in lymphocytes. We have investigated the physiological roles of TRAF2 in these processes by expressing a lymphocyte-specific dominant negative form of TRAF2, thereby blocking this protein's effector function. We find that the TNFR superfamily signals require TRAF2 for activation of JNK but not NF-κB. In addition, we show that TRAF2 induces NF-κB–independent antiapoptotic pathways during TNF-induced apoptosis. Inhibition of TRAF2 leads to splenomegaly, lymphadenopathy, and an increased number of B cells. These findings indicate that TRAF2 is involved in the regulation of lymphocyte function and growth in vivo