Institution
Nanjing Tech University
Education•Nanjing, China•
About: Nanjing Tech University is a education organization based out in Nanjing, China. It is known for research contribution in the topics: Catalysis & Membrane. The organization has 21827 authors who have published 21794 publications receiving 364050 citations. The organization is also known as: Nangongda & Nánjīng Gōngyè Dàxúe.
Topics: Catalysis, Membrane, Adsorption, Chemistry, Microstructure
Papers published on a yearly basis
Papers
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TL;DR: This mini-review will focus on the importance of the endocytic pathway as well as the autophagy process in viral infection of several pathogenic CoVs inclusive of SARS- coV, MERS-CoV and the new CoV named as severe acute respiratory syndrome coronavirus 2 (SARS- CoV-2), and discuss the development of therapeutic agents by targeting these processes.
Abstract: Coronaviruses (CoVs) are a group of enveloped, single-stranded positive genomic RNA viruses and some of them are known to cause severe respiratory diseases in human, including Severe Acute Respiratory Syndrome (SARS), Middle East Respiratory Syndrome (MERS) and the ongoing coronavirus disease-19 (COVID-19). One key element in viral infection is the process of viral entry into the host cells. In the last two decades, there is increasing understanding on the importance of the endocytic pathway and the autophagy process in viral entry and replication. As a result, the endocytic pathway including endosome and lysosome has become important targets for development of therapeutic strategies in combating diseases caused by CoVs. In this mini-review, we will focus on the importance of the endocytic pathway as well as the autophagy process in viral infection of several pathogenic CoVs inclusive of SARS-CoV, MERS-CoV and the new CoV named as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), and discuss the development of therapeutic agents by targeting these processes. Such knowledge will provide important clues for control of the ongoing epidemic of SARS-CoV-2 infection and treatment of COVID-19.
359 citations
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TL;DR: Greater enhanced valley spitting in monolayer WSe2 is shown, utilizing the interfacial magnetic exchange field (MEF) from a ferromagnetic EuS substrate, which may enable valleytronic and quantum-computing applications.
Abstract: Exploiting the valley degree of freedom to store and manipulate information provides a novel paradigm for future electronics. A monolayer transition-metal dichalcogenide (TMDC) with a broken inversion symmetry possesses two degenerate yet inequivalent valleys, which offers unique opportunities for valley control through the helicity of light. Lifting the valley degeneracy by Zeeman splitting has been demonstrated recently, which may enable valley control by a magnetic field. However, the realized valley splitting is modest (∼0.2 meV T-1). Here we show greatly enhanced valley spitting in monolayer WSe2, utilizing the interfacial magnetic exchange field (MEF) from a ferromagnetic EuS substrate. A valley splitting of 2.5 meV is demonstrated at 1 T by magnetoreflectance measurements and corresponds to an effective exchange field of ∼12 T. Moreover, the splitting follows the magnetization of EuS, a hallmark of the MEF. Utilizing the MEF of a magnetic insulator can induce magnetic order and valley and spin polarization in TMDCs, which may enable valleytronic and quantum-computing applications.
349 citations
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TL;DR: PMS oxidation with CoMn2O4 is an efficient technique for remediation of organic contaminants in wastewater and could maintain its catalytic activity in the repeated batch experiments, and a rational mechanism was proposed.
348 citations
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TL;DR: Both Hazard Quotient values for single elements and Hazard Index values for all studied elements suggested potential non-carcinogenic health risk to children, but not to adults and SBET-extractable contents of elements were significantly correlated with their total contents and the dust properties.
341 citations
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TL;DR: The most recent advances in NPCs for AWE were systematically reviewed, emphasizing the application of in situ/operando experimental methods and density functional theory (DFT) calculations in their understanding and development.
Abstract: Recent years have witnessed an upsurge in the development of non-precious catalysts (NPCs) for alkaline water electrolysis (AWE), especially with the strides made in experimental and computational techniques. In this contribution, the most recent advances in NPCs for AWE were systematically reviewed, emphasizing the application of in situ/operando experimental methods and density functional theory (DFT) calculations in their understanding and development. First, we briefly introduced the fundamentals of the anode and cathode reaction for AWE, i.e., the oxygen evolution reaction (OER) and the hydrogen evolution reaction (HER), respectively. Next, the most popular in situ/operando approaches for characterizing AWE catalysts, including hard and soft XAS, ambient-pressure XPS, liquid and identical location TEM, electrochemical mass spectrometry, and Raman spectroscopy were thoroughly summarized. Subsequently, we carefully discussed the principles, computational methods, applications, and combinations of DFT with machine learning for modeling NPCs and predicting the alkaline OER and HER. With the improved understanding of the structure-property-performance relationship of NPCs for AWE, we proceeded to overview their current development, summarising state-of-the-art design strategies to boost their activity. In addition, advances in various extensively investigated NPCs for AWE were evaluated. By conveying these methods, progress, insights, and perspectives, this review will contribute to a better understanding and rational development of non-precious AWE electrocatalysts for hydrogen production.
338 citations
Authors
Showing all 22047 results
Name | H-index | Papers | Citations |
---|---|---|---|
Yi Chen | 217 | 4342 | 293080 |
Richard H. Friend | 169 | 1182 | 140032 |
Hua Zhang | 163 | 1503 | 116769 |
Wei Huang | 139 | 2417 | 93522 |
Jian Zhou | 128 | 3007 | 91402 |
Haiyan Wang | 119 | 1674 | 86091 |
Jian Liu | 117 | 2090 | 73156 |
Lain-Jong Li | 113 | 627 | 58035 |
Hong Wang | 110 | 1633 | 51811 |
Jun-Jie Zhu | 103 | 754 | 41655 |
Stefan Kaskel | 101 | 705 | 36201 |
Hong Liu | 100 | 1905 | 57561 |
Dirk De Vos | 96 | 642 | 33214 |
Peng Li | 95 | 1548 | 45198 |
Feng Liu | 95 | 1067 | 38478 |