Various kinds of nanostructured materials have been extensively investigated as lithium ion battery electrode materials derived from their numerous advantageous features including enhanced energy and power density and cyclability. However, little is known about the microscopic origin of how nanostructures can enhance lithium storage performance. Herein, we identify the microscopic origin of enhanced lithium storage in anatase TiO 2 nanostructure and report a reversible and stable route to achieve enhanced lithium storage capacity in anatase TiO 2 . We designed hollow anatase TiO 2 nanostructures composed of interconnected ∼5 nm sized nanocrystals, which can individually reach the theoretical lithium storage limit and maintain a stable capacity during prolonged cycling (i.e., 330 mAh g −1 for the initial cycle and 228 mAh g −1 for the 100th cycle, at 0.1 A g −1 ). In situ characterization by X-ray diffraction and X-ray absorption spectroscopy shows that enhanced lithium storage into the anatase TiO 2 nanocrystal results from the insertion reaction, which expands the crystal lattice during the sequential phase transition (anatase TiO 2 → Li 0.55 TiO 2 → LiTiO 2 ). In addition to the pseudocapacitive charge storage of nanostructures, our approach extends the utilization of nanostructured TiO 2 for significantly stabilizing excess lithium storage in crystal structures for long-term cycling, which can be readily applied to other lithium storage materials.
Noninvasive brain-computer interface (BCI) decodes brain signals to understand user intention. Recent advances have been developed for the BCI-based drone control system as the demand for drone control increases. Especially, drone swarm control based on brain signals could provide various industries such as military service or industry disaster. This paper presents a prototype of a brain-swarm interface system for a variety of scenarios using a visual imagery paradigm. We designed the experimental environment that could acquire brain signals under a drone swarm control simulator environment. Through the system, we collected the electroencephalogram (EEG) signals with respect to four different scenarios. Seven subjects participated in our experiment and evaluated classification performances using the basic machine learning algorithm. The grand average classification accuracy is higher than the chance level accuracy. Hence, we could confirm the feasibility of the drone swarm control system based on EEG signals for performing high-level tasks.
Non-invasive brain-computer interface (BCI) has been developed for understanding users' intentions by using electroencephalogram (EEG) signals. With the recent development of artificial intelligence, there have been many developments in the drone control system. BCI characteristic that can reflect the users' intentions led to the BCI-based drone control system. When using drone swarm, we can have more advantages, such as mission diversity, than using a single drone. In particular, BCI-based drone swarm control could provide many advantages to various industries such as military service or industry disaster. BCI Paradigms consist of the exogenous and endogenous paradigms. The endogenous paradigms can operate with the users' intentions independently of any stimulus. In this study, we designed endogenous paradigms (i.e., motor imagery (MI), visual imagery (VI), and speech imagery (SI)) specialized in drone swarm control, and EEG-based various task classifications related to drone swarm control were conducted. Five subjects participated in the experiment and the performance was evaluated using the basic machine learning algorithm. The grand-averaged accuracies were 51.1% (± 8.02), 53.2% (± 3.11), and 41.9% (± 6.09) in MI, VI, and SI, respectively. Hence, we confirmed the feasibility of increasing the degree of freedom for drone swarm control using various endogenous paradigms.
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