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, Electrospinning, Membrane, Graphene
Papers published on a yearly basis
Papers
More filters
••
TL;DR: This work demonstrates the implementation of a laminated ultrathin CVD graphene film as a stretchable and transparent electrode for supercapacitors and demonstrates excellent frequency capability with small time constants under stretching.
Abstract: Due to their exceptional flexibility and transparency, CVD graphene films have been regarded as an ideal replacement of indium tin oxide for transparent electrodes, especially in applications where electronic devices may be subjected to large tensile strain. However, the search for a desirable combination of stretchability and electrochemical performance of such devices remains a huge challenge. Here, we demonstrate the implementation of a laminated ultrathin CVD graphene film as a stretchable and transparent electrode for supercapacitors. Transferred and buckled on PDMS substrates by a prestraininig-then-buckling strategy, the four-layer graphene film maintained its outstanding quality, as evidenced by Raman spectra. Optical transmittance of up to 72.9% at a wavelength of 550 nm and stretchability of 40% were achieved. As the tensile strain increased up to 40%, the specific capacitance showed no degradation and even increased slightly. Furthermore, the supercapacitor demonstrated excellent frequency capa...
233 citations
••
TL;DR: The loaded AMX within the n-HA/PLGA hybrid nanofibers shows a sustained release profile and a non-compromised activity to inhibit the growth of a model bacterium, Staphylococcus aureus.
233 citations
••
TL;DR: In the paper, an heuristical example is given to illustrate the basic idea of the homotopy perturbation method, which has made all that is necessary simple, andall that is complex unnecessary.
232 citations
••
TL;DR: Electrospun piezoelectric nanofibres may be useful for developing high-performance acoustic sensors and can precisely distinguish sound waves in low to middle frequency region, which makes them especially suitable for noise detection.
Abstract: Considerable interest has been devoted to converting mechanical energy into electricity using polymer nanofibres. In particular, piezoelectric nanofibres produced by electrospinning have shown remarkable mechanical energy-to-electricity conversion ability. However, there is little data for the acoustic-to-electric conversion of electrospun nanofibres. Here we show that electrospun piezoelectric nanofibre webs have a strong acoustic-to-electric conversion ability. Using poly(vinylidene fluoride) as a model polymer and a sensor device that transfers sound directly to the nanofibre layer, we show that the sensor devices can detect low-frequency sound with a sensitivity as high as 266 mV Pa(-1). They can precisely distinguish sound waves in low to middle frequency region. These features make them especially suitable for noise detection. Our nanofibre device has more than five times higher sensitivity than a commercial piezoelectric poly(vinylidene fluoride) film device. Electrospun piezoelectric nanofibres may be useful for developing high-performance acoustic sensors.
232 citations
••
TL;DR: A new predictor, called iLoc-Animal, has been developed that can be used to deal with the systems containing both single- and multi-label animal (metazoan except human) proteins and the outcomes achieved were quite encouraging, indicating that the predictor may become a useful tool in this area.
Abstract: Predicting protein subcellular localization is a challenging problem, particularly when query proteins have multi-label features meaning that they may simultaneously exist at, or move between, two or more different subcellular location sites. Most of the existing methods can only be used to deal with the single-label proteins. Actually, multi-label proteins should not be ignored because they usually bear some special function worthy of in-depth studies. By introducing the “multi-label learning” approach, a new predictor, called iLoc-Animal, has been developed that can be used to deal with the systems containing both single- and multi-label animal (metazoan except human) proteins. Meanwhile, to measure the prediction quality of a multi-label system in a rigorous way, five indices were introduced; they are “Absolute-True”, “Absolute-False” (or Hamming-Loss”), “Accuracy”, “Precision”, and “Recall”. As a demonstration, the jackknife cross-validation was performed with iLoc-Animal on a benchmark dataset of animal proteins classified into the following 20 location sites: (1) acrosome, (2) cell membrane, (3) centriole, (4) centrosome, (5) cell cortex, (6) cytoplasm, (7) cytoskeleton, (8) endoplasmic reticulum, (9) endosome, (10) extracellular, (11) Golgi apparatus, (12) lysosome, (13) mitochondrion, (14) melanosome, (15) microsome, (16) nucleus, (17) peroxisome, (18) plasma membrane, (19) spindle, and (20) synapse, where many proteins belong to two or more locations. For such a complicated system, the outcomes achieved by iLoc-Animal for all the aforementioned five indices were quite encouraging, indicating that the predictor may become a useful tool in this area. It has not escaped our notice that the multi-label approach and the rigorous measurement metrics can also be used to investigate many other multi-label problems in molecular biology. As a user-friendly web-server, iLoc-Animal is freely accessible to the public at the web-site http://www.jci-bioinfo.cn/iLoc-Animal.
232 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 |