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Cho, Ara,Banu, Shahara,Cho, Yunae,Ahn, Seung Kyu,Yun, Jae Ho,Cho, Jun-Sik Elsevier 2019 SOLAR ENERGY -PHOENIX ARIZONA THEN NEW YORK- Vol.185 No.-
<P><B>Abstract</B></P> <P>Hybrid inks with a chelating agent were prepared and coated by a spin-coating method to form Cu<SUB>2</SUB>SnS<SUB>3</SUB> (CTS) thin films. After the coating, a subsequent sulfurizing process via rapid thermal annealing was performed. During the sulfurization, the Cu and Sn precursors in the hybrid inks exist in complex forms with chelates and these complexes help to form the CTS thin films by controlling the reaction rate of the metal precursors. Additionally, even though the complexes with chelates were formed, the oxidation numbers of the metal precursors were affected by the ionization tendency of each metal in the hybrid inks to form the semiconducting CTS thin films. After obtaining the optimum sulfurizing condition by controlling the reaction pressure and temperature, the CTS thin films were characterized and CTS solar cells were fabricated under these conditions. The best conversion efficiency of the fabricated cells was 2.953% and the temperature-dependent photovoltaic performances were also examined to investigate the carrier transport mechanisms of the devices. According to admittance spectroscopy, the dominant defect energy level was determined as 0.09 eV above the valence band minimum, which accords with the copper vacancy (<I>V<SUB>Cu</SUB> </I>) level. In addition, capacitance–voltage measurements and drive-level capacitance profiling were applied to demonstrate the carrier densities and defect behaviors.</P> <P><B>Highlights</B></P> <P> <UL> <LI> The chelating effect of metal-chelate complexes in hybrid ink can control reaction rate to form pure Cu<SUB>2</SUB>SnS<SUB>3</SUB> thin films. </LI> <LI> Metal-chelate complex helped to form pure Cu<SUB>2</SUB>SnS<SUB>3</SUB> by maintaining oxidation number of Cu and Sn. </LI> <LI> To investigate the carrier transport mechanisms, temperature-dependent As and DLCP analyses were conducted. </LI> <LI> The main defects were related to the <I>V<SUB>Cu</SUB> </I> acceptor. </LI> </UL> </P>
Application of Machine Learning and Deep Learning in Imaging of Ischemic Stroke
Ara Cho,Luu-Ngoc Do,김슬기,윤웅,백병현,박일우 대한자기공명의과학회 2022 Investigative Magnetic Resonance Imaging Vol.26 No.4
Timely analysis of imaging data is critical for diagnosis and decision-making for proper treatment strategy in the cases of ischemic stroke. Various efforts have been made to develop computer-assisted systems to improve the accuracy of stroke diagnosis and acute stroke triage. The widespread emergence of artificial intelligence technology has been integrated into the field of medicine. Artificial intelligence can play an important role in providing care to patients with stroke. In the past few decades, numerous studies have explored the use of machine learning and deep learning algorithms for application in the management of stroke. In this review, we will start with a brief introduction to machine learning and deep learning and provide clinical applications of machine learning and deep learning in various aspects of stroke management, including rapid diagnosis and improved triage, identifying large vessel occlusion, predicting time from stroke onset, automated ASPECTS (Alberta Stroke Program Early CT Score) measurement, lesion segmentation, and predicting treatment outcome. This work is focused on providing the current application of artificial intelligence techniques in the imaging of ischemic stroke, including MRI and CT.