Institution
China University of Petroleum
Education•Beijing, China•
About: China University of Petroleum is a education organization based out in Beijing, China. It is known for research contribution in the topics: Catalysis & Oil shale. The organization has 39802 authors who have published 39151 publications receiving 483760 citations. The organization is also known as: Zhōngguó Shíyóu Dàxué & China University of Petroleum (Beijing).
Topics: Catalysis, Oil shale, Adsorption, Fracture (geology), Source rock
Papers published on a yearly basis
Papers
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TL;DR: Co-teaching as discussed by the authors trains two deep neural networks simultaneously, and let them teach each other given every mini-batch: first, each network feeds forward all data and selects some data of possibly clean labels; secondly, two networks communicate with each other what data in this minibatch should be used for training; finally, each networks back propagates the data selected by its peer network and updates itself.
Abstract: Deep learning with noisy labels is practically challenging, as the capacity of deep models is so high that they can totally memorize these noisy labels sooner or later during training. Nonetheless, recent studies on the memorization effects of deep neural networks show that they would first memorize training data of clean labels and then those of noisy labels. Therefore in this paper, we propose a new deep learning paradigm called Co-teaching for combating with noisy labels. Namely, we train two deep neural networks simultaneously, and let them teach each other given every mini-batch: firstly, each network feeds forward all data and selects some data of possibly clean labels; secondly, two networks communicate with each other what data in this mini-batch should be used for training; finally, each network back propagates the data selected by its peer network and updates itself. Empirical results on noisy versions of MNIST, CIFAR-10 and CIFAR-100 demonstrate that Co-teaching is much superior to the state-of-the-art methods in the robustness of trained deep models.
866 citations
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TL;DR: Experiments and density functional theory calculations demonstrate single-atom Co-N5 site is the dominating active center simultaneously for CO2 activation, the rapid formation of key intermediate COOH* as well as the desorption of CO.
Abstract: We develop an N-coordination strategy to design a robust CO2 reduction reaction (CO2RR) electrocatalyst with atomically dispersed Co–N5 site anchored on polymer-derived hollow N-doped porous carbon spheres. Our catalyst exhibits high selectivity for CO2RR with CO Faradaic efficiency (FECO) above 90% over a wide potential range from −0.57 to −0.88 V (the FECO exceeded 99% at −0.73 and −0.79 V). The CO current density and FECO remained nearly unchanged after electrolyzing 10 h, revealing remarkable stability. Experiments and density functional theory calculations demonstrate single-atom Co–N5 site is the dominating active center simultaneously for CO2 activation, the rapid formation of key intermediate COOH* as well as the desorption of CO.
835 citations
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TL;DR: The fundamental engineering principles used to design RCA nanotechnologies are introduced, the recently developed RCA-based diagnostics and bioanalytical tools are discussed, and the use of RCA to construct multivalent molecular scaffolds and nanostructures for applications in biology, diagnostic and therapeutics is summarized.
Abstract: Rolling circle amplification (RCA) is an isothermal enzymatic process where a short DNA or RNA primer is amplified to form a long single stranded DNA or RNA using a circular DNA template and special DNA or RNA polymerases. The RCA product is a concatemer containing tens to hundreds of tandem repeats that are complementary to the circular template. The power, simplicity, and versatility of the DNA amplification technique have made it an attractive tool for biomedical research and nanobiotechnology. Traditionally, RCA has been used to develop sensitive diagnostic methods for a variety of targets including nucleic acids (DNA, RNA), small molecules, proteins, and cells. RCA has also attracted significant attention in the field of nanotechnology and nanobiotechnology. The RCA-produced long, single-stranded DNA with repeating units has been used as template for the periodic assembly of nanospecies. Moreover, since RCA products can be tailor-designed by manipulating the circular template, RCA has been employed to generate complex DNA nanostructures such as DNA origami, nanotubes, nanoribbons and DNA based metamaterials. These functional RCA based nanotechnologies have been utilized for biodetection, drug delivery, designing bioelectronic circuits and bioseparation. In this review, we introduce the fundamental engineering principles used to design RCA nanotechnologies, discuss recently developed RCA-based diagnostics and bioanalytical tools, and summarize the use of RCA to construct multivalent molecular scaffolds and nanostructures for applications in biology, diagnostics and therapeutics.
788 citations
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TL;DR: In this article, the principles of electrical plasma with liquids for pollutant removal and the reactors of various electrical discharge types are outlined in this review, and detailed discussions are given of the effects of various factors on the performance of pulsed electrical plasma technology in water treatment processes.
723 citations
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TL;DR: In this article, a critical review of research on heat transfer applications of nanofluids with the aim of identifying the limiting factors so as to push forward their further development is presented.
697 citations
Authors
Showing all 40138 results
Name | H-index | Papers | Citations |
---|---|---|---|
Lei Jiang | 170 | 2244 | 135205 |
Shi-Zhang Qiao | 142 | 523 | 80888 |
Jian Zhou | 128 | 3007 | 91402 |
Tao Zhang | 123 | 2772 | 83866 |
Jian Liu | 117 | 2090 | 73156 |
Qiang Yang | 112 | 1117 | 71540 |
Jianjun Liu | 112 | 1040 | 71032 |
Ju Li | 109 | 623 | 46004 |
Peng Wang | 108 | 1672 | 54529 |
Alan R. Fersht | 108 | 400 | 33895 |
Jian Zhang | 107 | 3064 | 69715 |
Wei Liu | 102 | 2927 | 65228 |
Xiaoming Sun | 96 | 382 | 47153 |
Haibo Zeng | 94 | 604 | 39226 |
Chao Wang | 91 | 561 | 32854 |