Institution
Donghua University
Education•Shanghai, China•
About: Donghua University is a education organization based out in Shanghai, China. It is known for research contribution in the topics: Fiber & Nanofiber. The organization has 21155 authors who have published 21841 publications receiving 393091 citations. The organization is also known as: Dōnghuá Dàxué & China Textile University.
Topics: Fiber, Nanofiber, Membrane, Electrospinning, Catalysis
Papers published on a yearly basis
Papers
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TL;DR: In this article, a sandwich-structured PVdF/PMIA/PVdF nanofibrous battery separators with robust mechanical strength and thermal stability are fabricated via a sequential electrospinning technique.
Abstract: Novel, sandwich-structured PVdF/PMIA/PVdF nanofibrous battery separators with robust mechanical strength and thermal stability are fabricated via a sequential electrospinning technique. The nanofibers of the PVdF and the PMIA layers are bonded and interconnected on the interface boundary without any polymer binder or post-treatment. Benefiting from the high porosity of the as-prepared membranes and the introduction of PMIA, the PVdF/PMIA/PVdF composite membranes exhibit high ionic conductivity (2.3 times higher than that of the Celgard membrane), robust tensile strength (13.96 MPa), and excellent thermal stability, sustaining insulation after closing the pores in the PVdF layer. Hot oven testing reveals that the composite membranes exhibit no dimension shrinkage after being exposed to 180 °C for 1 h. Furthermore, the as-prepared-membrane-based Li/LiCoO2 cell shows a higher capacity retention of 93.10% after 100 cycles and better rate performance compared with the cell using the Celgard membrane, providing new insight into the design and development of high-performance rechargeable lithium ion batteries.
180 citations
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TL;DR: The advantage by incorporating the complexity measure factor into the pseudo amino acid composition as one of its components is that it can catch the essence of the overall sequence pattern of a protein and hence more effectively reflect its sequence‐order effects.
Abstract: The structural class is an important feature widely used to characterize the overall folding type of a protein. How to improve the prediction quality for protein structural classification by effectively incorporating the sequence-order effects is an important and challenging problem. Based on the concept of the pseudo amino acid composition [Chou, K. C. Proteins Struct Funct Genet 2001, 43, 246; Erratum: Proteins Struct Funct Genet 2001, 44, 60], a novel approach for measuring the complexity of a protein sequence was introduced. The advantage by incorporating the complexity measure factor into the pseudo amino acid composition as one of its components is that it can catch the essence of the overall sequence pattern of a protein and hence more effectively reflect its sequence-order effects. It was demonstrated thru the jackknife crossvalidation test that the overall success rate by the new approach was significantly higher than those by the others. It has not escaped our notice that the introduction of the complexity measure factor can also be used to improve the prediction quality for, among many other protein attributes, subcellular localization, enzyme family class, membrane protein type, and G-protein couple receptor type.
180 citations
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TL;DR: In this paper, the authors harnessed β-cyclodextrin enhanced triboelectrification for self-powered phenol detection as well as electrochemical degradation, achieving a detection sensitivity of 0.01 μM−1 in the sensing range of 10 μM to 100 μM.
Abstract: We report a unique route that creatively harnessed β-cyclodextrin enhanced triboelectrification for self-powered phenol detection as well as electrochemical degradation. A detection sensitivity of 0.01 μM−1 was demonstrated in the sensing range of 10 μM to 100 μM. In addition, β-cyclodextrin enhanced triboelectrification was designed to harvest kinetic impact energy from wastewater waves to electrochemically degrade the phenol in a self-powered manner without using an external power source.
179 citations
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TL;DR: High thermoelectric performance and stretchability in interlocked fiber-based modules for wearable devices in true textiles, proving active thermoelectedrics can be woven into various fabric architectures for sensing, energy harvesting, or thermal management.
Abstract: Assembling thermoelectric modules into fabric to harvest energy from body heat could one day power multitudinous wearable electronics. However, the invalid 2D architecture of fabric limits the application in thermoelectrics. Here, we make the valid thermoelectric fabric woven out of thermoelectric fibers producing an unobtrusive working thermoelectric module. Alternately doped carbon nanotube fibers wrapped with acrylic fibers are woven into π-type thermoelectric modules. Utilizing elasticity originating from interlocked thermoelectric modules, stretchable 3D thermoelectric generators without substrate can be made to enable sufficient alignment with the heat flow direction. The textile generator shows a peak power density of 70 mWm−2 for a temperature difference of 44 K and excellent stretchability (~80% strain) with no output degradation. The compatibility between body movement and sustained power supply is further displayed. The generators described here are true textiles, proving active thermoelectrics can be woven into various fabric architectures for sensing, energy harvesting, or thermal management. Despite recent advances in flexible thermoelectric generators for wearable devices, current designs are unable to efficiently harvest heat flowing from human body. Here, the authors report high thermoelectric performance and stretchability in interlocked fiber-based modules for wearable devices.
179 citations
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TL;DR: A review of the recent C F bond cleavage examples and further transformation of compounds bearing with an aliphatic fluoride, difluoromethylene group or trifluoricomethyl groups can be found in this paper.
179 citations
Authors
Showing all 21321 results
Name | H-index | Papers | Citations |
---|---|---|---|
Dongyuan Zhao | 160 | 872 | 106451 |
Xiang Zhang | 154 | 1733 | 117576 |
Seeram Ramakrishna | 147 | 1552 | 99284 |
Kuo-Chen Chou | 143 | 487 | 57711 |
Shuai Liu | 129 | 1095 | 80823 |
Chao Zhang | 127 | 3119 | 84711 |
Tao Zhang | 123 | 2772 | 83866 |
Zidong Wang | 122 | 914 | 50717 |
Xinchen Wang | 120 | 349 | 65072 |
Zhenyu Zhang | 118 | 1167 | 64887 |
Benjamin S. Hsiao | 108 | 602 | 41071 |
Qian Wang | 108 | 2148 | 65557 |
Jian Zhang | 107 | 3064 | 69715 |
Yan Zhang | 107 | 2410 | 57758 |
Richard B. Kaner | 106 | 557 | 66862 |