Plasmon-coupled CdSe/ZnS and CdTe/CdS/ZnS coreshells are investigated for their optoelectronic applications because of their high color purity, wide optical tunability, large PL enhancement, and compact and easy integration into electronic devices. The quantum confinement of carriers within quantum dots (QDs) with sizes near the exciton Bohr radius (CdSe ~ 5.8 nm, CdTe ~ 7 nm) exhibits the features of discrete energy states and blue-shift from the bulk bandgap (CdSe ~718 nm, CdTe ~ 863 nm) in the optical spectrum. While the fluorescence from the QDs is attributable to the exciton carrier recombination, large PL enhancement and fast emission time is achieved through plasmon-exciton coupling via the Coulomb interaction. Large PL enhancement of QDs in the vicinity of plasmonic particles was observed and attributed to the reduction of the non-radiative decay rate and large local field enhancement. The large PL enhancement and wide optical tunability along with high color purity from plasmon-coupled QDs enables the realization of hybrid LEDs.
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http://dx.doi.org/10.1166/jnn.2016.12031 | DOI Listing |
Sci Rep
December 2024
Department of Medical Device Development, Seoul National University College of Medicine, Seoul, Republic of Korea.
Vertebral collapse (VC) following osteoporotic vertebral compression fracture (OVCF) often requires aggressive treatment, necessitating an accurate prediction for early intervention. This study aimed to develop a predictive model leveraging deep neural networks to predict VC progression after OVCF using magnetic resonance imaging (MRI) and clinical data. Among 245 enrolled patients with acute OVCF, data from 200 patients were used for the development dataset, and data from 45 patients were used for the test dataset.
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December 2024
Department of Civil Engineering, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland.
Deep learning models are widely used for traffic forecasting on freeways due to their ability to learn complex temporal and spatial relationships. In particular, graph neural networks, which integrate graph theory into deep learning, have become popular for modeling traffic sensor networks. However, traditional graph convolutional networks (GCNs) face limitations in capturing long-range spatial correlations, which can hinder accurate long-term predictions.
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December 2024
Department of Mathematics, Texas A&M University, College Station, TX, 77843, USA.
The northern Gulf of Mexico (nGoM) receives water from over 50 rivers which are highly influenced by humans and include the largest river in the United States, the Mississippi River. To support large-scale data-driven research centered on the dynamic river-ocean system in the region, this study consolidated hydrogeochemical river and ocean data from across the nGoM. In particular, we harmonized 35 chemical solute parameters from 54 rivers and incorporated river discharge data to derive daily solute concentration and flux estimates throughout the nGoM.
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December 2024
Department of Civil and Environmental Engineering, and Research Centre for Resources Engineering towards Carbon Neutrality, The Hong Kong Polytechnic University, Hong Kong, China.
The feasibility of carbon mineralization relies on the carbonation efficiency of CO-reactive minerals, which is largely governed by the water content and state within material mesopores. Yet, the pivotal role of confined water in regulating carbonation efficiency at the nanoscale is not well understood. Here, we show that the maximum CO intake occurs at an optimal relative humidity (RH) when capillary condensation initiates within the hydrophilic mesopores.
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December 2024
State Key Laboratory of Intelligent Construction and Healthy Operation and Maintenance of Deep Underground Engineering, Sichuan University & Shenzhen University, Chengdu, P.R. China.
Electrochemical CO capture driven by renewable electricity holds significant potential for efficient decarbonization. However, the widespread adoption of this approach is currently limited by issues such as instability, discontinuity, high energy demand, and challenges in scaling up. In this study, we propose a scalable strategy that addresses these limitations by transforming the conventional single-step electrochemical redox reaction into a stepwise electrochemical-chemical redox process.
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