A scalable molecule-based magnetic thin film for spin-thermoelectric energy conversion

Spin thermoelectrics, an emerging thermoelectric technology, offers energy harvesting from waste heat with potential advantages of scalability and energy conversion efficiency, thanks to orthogonal paths for heat and charge flow. However, magnetic insulators previously used for spin thermoelectrics pose challenges for scale-up due to high temperature processing and difficulty in large-area deposition. Here, we introduce a molecule-based magnetic film for spin thermoelectric applications because it entails versatile synthetic routes in addition to weak spin-lattice interaction and low thermal conductivity. Thin films of Cr-II[Cr-III(CN)(6)], Prussian blue analogue, electrochemically deposited on Cr electrodes at room temperature show effective spin thermoelectricity. Moreover, the ferromagnetic resonance studies exhibit an extremely low Gilbert damping constant -(2.4 +/- 0.67) x10(-4), indicating low loss of heat-generated magnons. The demonstrated STE applications of a new class of magnet will pave the way for versatile recycling of ubiquitous waste heat

High Density, Localized Quantum Emitters in Strained 2D Semiconductors

Two-dimensional chalcogenide semiconductors have recently emerged as a host material for quantum emitters of single photons. While several reports on defect and strain-induced single photon emission from 2D chalcogenides exist, a bottom-up, lithography-free approach to producing a high density of emitters remains elusive. Further, the physical properties of quantum emission in the case of strained 2D semiconductors are far from being understood. Here, we demonstrate a bottom-up, scalable, and lithography-free approach to creating large areas of localized emitters with high density (~150 emitters/um2) in a WSe2 monolayer. We induce strain inside the WSe2 monolayer with high spatial density by conformally placing the WSe2 monolayer over a uniform array of Pt nanoparticles with a size of 10 nm. Cryogenic, time-resolved, and gate-tunable luminescence measurements combined with near-field luminescence spectroscopy suggest the formation of localized states in strained regions that emit single photons with a high spatial density. Our approach of using a metal nanoparticle array to generate a high density of strained quantum emitters opens a new path towards scalable, tunable, and versatile quantum light sources.Comment: 45 pages, 20 figures (5 main figures, 15 supporting figures

Deep learning-based EEG analysis to classify normal, mild cognitive impairment, and dementia: Algorithms and dataset

For automatic EEG diagnosis, this paper presents a new EEG data set with well-organized clinical annotations called Chung-Ang University Hospital EEG (CAUEEG), which has event history, patient’s age, and corresponding diagnosis labels. We also designed two reliable evaluation tasks for the low-cost, non-invasive diagnosis to detect brain disorders: i) CAUEEG-Dementia with normal, mci, and dementia diagnostic labels and ii) CAUEEG-Abnormal with normal and abnormal. Based on the CAUEEG dataset, this paper proposes a new fully end-to-end deep learning model, called the CAUEEG End-to-end Deep neural Network (CEEDNet). CEEDNet pursues to bring all the functional elements for the EEG analysis in a seamless learnable fashion while restraining non-essential human intervention. Extensive experiments showed that our CEEDNet significantly improves the accuracy compared with existing methods, such as machine learning methods and Ieracitano-CNN (Ieracitano et al., 2019), due to taking full advantage of end-to-end learning. The high ROC-AUC scores of 0.9 on CAUEEG-Dementia and 0.86 on CAUEEG-Abnormal recorded by our CEEDNet models demonstrate that our method can lead potential patients to early diagnosis through automatic screening

Optimum Geometric Transformation and Bipartite Graph-Based Approach to Sweat Pore Matching for Biometric Identification

Sweat pores on the human fingertip have meaningful patterns that enable individual identification. Although conventional automatic fingerprint identification systems (AFIS) have mainly employed the minutiae features to match fingerprints, there has been minimal research that uses sweat pores to match fingerprints. Recently, high-resolution optical sensors and pore-based fingerprint systems have become available, which motivates research on pore analysis. However, most existing pore-based AFIS methods use the minutia-ridge information and image pixel distribution, which limit their applications. In this context, this paper presents a stable pore matching algorithm which effectively removes both the minutia-ridge and fingerprint-device dependencies. Experimental results show that the proposed pore matching algorithm is more accurate for general fingerprint images and robust under noisy conditions compared with existing methods. The proposed method can be used to improve the performance of AFIS combined with the conventional minutiae-based methods. Since sweat pores can also be observed using various systems, removing of the fingerprint-device dependency will make the pore-based AFIS useful for wide applications including forensic science, which matches the latent fingerprint to the fingerprint image in databases

Energy Consumption Verification of SPD Smart Window, Controllable According to Solar Radiation in South Korea

Between 60% and 70% of the total energy load of a house or office occurs through the exteriors of the building, and in the case of offices, heat loss from windows and doors can approach 40%. A need for glass that can artificially control the transmittance of visible light has therefore emerged. Smart windows with suspended particle device (SPD) film can reduce energy consumption by responding to environmental conditions. To measure the effect of SPD windows on the energy requirements for cooling and heating in Korea, we installed a testbed with SPD windows. With TRNSYS18, the comparison between measurements and simulation has been made in order to validate the simulation model with respect to the modeling of an SPD window. Furthermore, the energy requirements of conventional and SPD-applied windows were compared and analyzed for a standard building that represented an actual office building. When weather for the city of Anseong and a two-speed heat pump were used to verify the simulation, the simulated electricity consumption error compared with the testbed was −1.0% for cooling and −0.9% for heating. The annual electricity consumption error was −0.9%. When TMY2 Seoul weather data were applied to the reference building, the decrease in electricity consumption for cooling in the SPD model compared with the non-SPD model was 29.1% and the increase for heating was 15.8%. Annual electricity consumption decreased by 4.1%

Magnetoresistance oscillations in topological insulator Bi2Te3 nanoscale antidot arrays

Nanoscale antidot arrays were fabricated on a single-crystal microflake of topological insulator Bi2Te3. The introduction of antidot arrays significantly increased the resistance of the microflake, yet the temperature dependence of the resistance remains metallic. We observed that small oscillations that are periodic in magnetic field B appeared on top of the weak anti-localization magnetoresistance. Since the electron coherence length at low temperature becomes comparable to the feature size in our device, we argued that the magnetoresistance oscillations are the manifestation of quantum interference induced by the nanostructure. Our work demonstrates that the transport of topological insulators could indeed be controlled by artificially created nanostructures, and paves the way for future technological applications of this class of materials