Nanjing University

About: Nanjing University is a education organization based out in Nanjing, China. It is known for research contribution in the topics: Catalysis & Adsorption. The organization has 85961 authors who have published 105504 publications receiving 2289036 citations. The organization is also known as: NJU & Nanking University.

Recent Advances in Electrochemiluminescence Analysis.

Abstract: ■ CONTENTS Novel ECL Systems 358 Novel Organic Luminophores 358 Novel Inorganic Luminophores 359 Novel Nanomaterial System 360 Detection Methodologies and Signaling Amplification Strategies 361 General Detection Methodologies 361 Novel Signal Amplification Strategies 361 ECL Applications 362 Metal Ions Detection 362 Small Molecules Detection 362 ECL Immunoassay 363 ECL Genosensors 365 ECL Cytosensors 366 Conclusions and Outlooks 368 Author Information 369 Corresponding Author 369 ORCID 369 Author Contributions 369 Notes 369 Biographies 369 Acknowledgments 369 References 369

Near-real-time monitoring of global CO2 emissions reveals the effects of the COVID-19 pandemic

Abstract: The COVID-19 pandemic is impacting human activities, and in turn energy use and carbon dioxide (CO2) emissions. Here we present daily estimates of country-level CO2 emissions for different sectors based on near-real-time activity data. The key result is an abrupt 8.8% decrease in global CO2 emissions (-1551 Mt CO2) in the first half of 2020 compared to the same period in 2019. The magnitude of this decrease is larger than during previous economic downturns or World War II. The timing of emissions decreases corresponds to lockdown measures in each country. By July 1st, the pandemic's effects on global emissions diminished as lockdown restrictions relaxed and some economic activities restarted, especially in China and several European countries, but substantial differences persist between countries, with continuing emission declines in the U.S. where coronavirus cases are still increasing substantially.

Tunable Unidirectional Sound Propagation through a Sonic-Crystal-Based Acoustic Diode

Abstract: Nonreciprocal wave propagation typically requires strong nonlinear materials to break time reversal symmetry. Here, we utilized a sonic-crystal-based acoustic diode that had broken spatial inversion symmetry and experimentally realized sound unidirectional transmission in this acoustic diode. These novel phenomena are attributed to different mode transitions as well as their associated different energy conversion efficiencies among different diffraction orders at two sides of the diode. This nonreciprocal sound transmission could be systematically controlled by simply mechanically rotating the square rods of the sonic crystal. Different from nonreciprocity due to the nonlinear acoustic effect and broken time reversal symmetry, this new model leads to a one-way effect with higher efficiency, broader bandwidth, and much less power consumption, showing promising applications in various sound devices.

Design for Efficiency Optimization and Voltage Controllability of Series–Series Compensated Inductive Power Transfer Systems

Abstract: Inductive power transfer (IPT) is an emerging technology that may create new possibilities for wireless power charging and transfer applications. However, the rather complex control method and low efficiency remain the key obstructing factors for general deployment. In a regularly compensated IPT circuit, high efficiency and controllability of the voltage transfer function are always conflicting requirements under varying load conditions. In this paper, the relationships among compensation parameters, circuit efficiency, voltage transfer function, and conduction angle of the input current relative to the input voltage are studied. A design and optimization method is proposed to achieve a better overall efficiency as well as good output voltage controllability. An IPT system design procedure is illustrated with design curves to achieve a desirable voltage transfer ratio, optimizing between efficiency enhancement and current rating of the switches. The analysis is supported with experimental results.

Hyperspectral Image Super-Resolution via Non-Negative Structured Sparse Representation

Abstract: Hyperspectral imaging has many applications from agriculture and astronomy to surveillance and mineralogy. However, it is often challenging to obtain high-resolution (HR) hyperspectral images using existing hyperspectral imaging techniques due to various hardware limitations. In this paper, we propose a new hyperspectral image super-resolution method from a low-resolution (LR) image and a HR reference image of the same scene. The estimation of the HR hyperspectral image is formulated as a joint estimation of the hyperspectral dictionary and the sparse codes based on the prior knowledge of the spatial-spectral sparsity of the hyperspectral image. The hyperspectral dictionary representing prototype reflectance spectra vectors of the scene is first learned from the input LR image. Specifically, an efficient non-negative dictionary learning algorithm using the block-coordinate descent optimization technique is proposed. Then, the sparse codes of the desired HR hyperspectral image with respect to learned hyperspectral basis are estimated from the pair of LR and HR reference images. To improve the accuracy of non-negative sparse coding, a clustering-based structured sparse coding method is proposed to exploit the spatial correlation among the learned sparse codes. The experimental results on both public datasets and real LR hypspectral images suggest that the proposed method substantially outperforms several existing HR hyperspectral image recovery techniques in the literature in terms of both objective quality metrics and computational efficiency.