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Kang, Kisuk

Publications and source records attributed to Kang, Kisuk.

Design Principles in Engineering of Multigrain Nanocatalysts via Multiscale Electronic Structure Characterization

Engineering grain boundary (GB) strain provides a promising pathway to tune the catalytic properties of nanocrystals. However, structural heterogeneity from random grain orientation and geometry has limited clear structure–property correlations. Here, we utilize a multigrain Co3O4/Mn3O4 core/shell nanocrystal platform as a model system to systematically investigate how geometric misfit strain at GBs serves as catalytically active sites for the oxygen reduction reaction. Through precise subnanometer-level control over grain morphology and by integrating multiscale electronic structure characterization, we identify the electronic structural signature of GB defects and establish a direct correlation between localized strain fields and modified electronic states. Strain modulation at GBs alters the eg orbital energy levels, with elongation along the z-axis combined with shear strain stabilizing the eg states, in contrast to the destabilization observed under pure shear strain. This stabilization mechanism enhances the electrocatalytic activity and selectivity of strained GBs compared with strain-relaxed grain surfaces. Furthermore, we reveal that GBs exhibit a radial strain gradient, producing a spatial energy shift that further modulates local electronic structures, as resolved through the classification of electron energy loss spectroscopy data. Together, these findings demonstrate that geometric misfit strain enables precise tuning of grain geometry and the resulting electronic structures, offering a robust strategy for engineering next-generation nanocatalysts.

Cho, Min Gee↗

Revisiting the role of Zr doping in Ni-rich layered cathodes for lithium-ion batteries

We report the realization of high performance Ni-rich layered cathodes remains a challenge because of the multiple degradation factors that concurrently operate during battery cycling. In particular, depletion of oxygen charge and consequent lattice oxygen instability at deep charge state accelerate the subsequent chemomechanical degradation mechanisms. Among the proposed methodologies, doping has proven to be effective in enhancing the cathode cycle life by stabilizing the layered structure. Herein, we achieved the electrochemically stabilized Ni-rich LiNi 0.92 Co 0.04 Mn 0.04 O 2 through Zr doping, resulting in a 15% increase of the capacity retention after 100 cycles. In-depth investigations are conducted to unveil the effects of Zr doping on the layered cathode, and in particular, the critical role of Zr doping on the lattice oxygen stability is systematically studied. By combining state-of-the-art magnetometer characterization, X-ray analysis, and first-principles calculation, we reveal that Zr doping positively contributes the to lattice oxygen stability by alleviating the oxygen charge loss at deep charge, thereby improving the cathode electrochemical reversibility. Our findings provide an insight into the Zr doping mechanism and help to design the Ni-rich layered oxides for future applications.

25 ENERGY STORAGE↗

High-Voltage Phosphate Cathodes for Rechargeable Ca-Ion Batteries

Calcium-ion batteries (CIBs) are under investigation as next-generation energy storage devices due to their theoretically high operating potentials and lower costs tied to the high natural abundance of calcium. However, the development of CIBs has been limited by the lack of available positive electrode materials. In this study, for the first time, we report two functional polyanionic phosphate materials as high-voltage cathodes for CIBs at room temperature. NaV 2 (PO 4 ) 3 electrodes were found to reversibly intercalate 0.6 mol of Ca 2+ (81 mA h g –1 ) near 3.2 V (vs Ca 2+ /Ca) with stable cycling performance at a current density of 3.5 mA g –1 . The olivine framework material FePO 4 reversibly intercalates 0.2 mol of Ca 2+ (72 mA h g –1 ) near 2.9 V (vs Ca 2+ /Ca) at a current density of 7.5 mA g –1 in the first cycle. Structural, electronic, and compositional changes are consistent with reversible Ca 2+ intercalation into these two materials.

25 ENERGY STORAGE↗

Predicting the chemical reactivity of organic materials using a machine-learning approach

Stability and compatibility between chemical components are essential parameters that need to be considered in the selection of functional materials in configuring a system. In configuring devices such as batteries or solar cells, not only the functionality of individual constituting materials such as electrodes or electrolyte but also an appropriate combination of materials which do not undergo unwanted side reactions is critical in ensuring their reliable performance in long-term operation. While the universal theory that can predict the general chemical reactivity between materials is long awaited and has been the subject of studies with a rich history, traditional ways proposed to date have been mostly based on simple electronic properties of materials such as electronegativity, ionization energy, electron affinity and hardness/softness, and could be applied to only a small group of materials. Moreover, prediction has often been far from accurate and has failed to offer general implications; thus it was practically inadequate as a selection criterion from a large material database, i.e. data-driven material discovery. Herein, we propose a new model for predicting the general reactivity and chemical compatibility among a large number of organic materials, realized by a machine-learning approach. As a showcase, we demonstrate that our new implemented model successfully reproduces previous experimental results reported on side-reactions occurring in lithium–oxygen electrochemical cells. Furthermore, the mapping of chemical stability among more than 90 available electrolyte solvents and the representative redox mediators is realized by this approach, presenting an important guideline in the development of stable electrolyte/redox mediator couples for lithium–oxygen batteries.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗