Institution
Nanjing University of Science and Technology
Education•Nanjing, China•
About: Nanjing University of Science and Technology is a education organization based out in Nanjing, China. It is known for research contribution in the topics: Catalysis & Computer science. The organization has 31581 authors who have published 36390 publications receiving 525474 citations. The organization is also known as: Nánjīng Lǐgōng Dàxué & Nánlǐgōng.
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TL;DR: In this article, an innovative new class of heat transfer fluids can be engineered by suspending metallic nanoparticles in conventional heat-transfer fluids, which are expected to exhibit high thermal conductivities compared to those of currently used heat transfer fluid, and they represent the best hope for enhancing heat transfer.
Abstract: Low thermal conductivity is a primary limitation in the development of energy-efficient heat transfer fluids that are required in many industrial applications. In this paper we propose that an innovative new class of heat transfer fluids can be engineered by suspending metallic nanoparticles in conventional heat transfer fluids. The resulting {open_quotes}nanofluids{close_quotes} are expected to exhibit high thermal conductivities compared to those of currently used heat transfer fluids, and they represent the best hope for enhancement of heat transfer. The results of a theoretical study of the thermal conductivity of nanofluids with copper nanophase materials are presented, the potential benefits of the fluids are estimated, and it is shown that one of the benefits of nanofluids will be dramatic reductions in heat exchanger pumping power.
4,634 citations
TL;DR: A new technique coined two-dimensional principal component analysis (2DPCA) is developed for image representation that is based on 2D image matrices rather than 1D vectors so the image matrix does not need to be transformed into a vector prior to feature extraction.
Abstract: In this paper, a new technique coined two-dimensional principal component analysis (2DPCA) is developed for image representation. As opposed to PCA, 2DPCA is based on 2D image matrices rather than 1D vectors so the image matrix does not need to be transformed into a vector prior to feature extraction. Instead, an image covariance matrix is constructed directly using the original image matrices, and its eigenvectors are derived for image feature extraction. To test 2DPCA and evaluate its performance, a series of experiments were performed on three face image databases: ORL, AR, and Yale face databases. The recognition rate across all trials was higher using 2DPCA than PCA. The experimental results also indicated that the extraction of image features is computationally more efficient using 2DPCA than PCA.
3,439 citations
TL;DR: In this article, the authors proposed two different approaches for deriving heat transfer correlation of the nanofluid, and investigated the mechanism of heat transfer enhancement of the nano-fluid.
2,355 citations
TL;DR: In this article, the authors present a procedure for preparing a nanofluid which is a suspension consisting of nanophase powders and a base liquid, and their TEM photographs are given to illustrate the stability and evenness of suspension.
2,341 citations
TL;DR: The demonstration of these novel quantum-dot light-emitting diodes based on all-inorganic perovskite CsPbX3 (X = Cl, Br, I) nanocrystals opens a new avenue toward designing optoelectronic devices, such as displays, photodetectors, solar cells, and lasers.
Abstract: Novel quantum-dot light-emitting diodes based on all-inorganic perovskite CsPbX3 (X = Cl, Br, I) nanocrystals are reported. The well-dispersed, single-crystal quantum dots (QDs) exhibit high quantum yields, and tunable light emission wavelength. The demonstration of these novel perovskite QDs opens a new avenue toward designing optoelectronic devices, such as displays, photodetectors, solar cells, and lasers.
2,311 citations
Authors
Showing all 31818 results
| Name | H-index | Papers | Citations |
|---|---|---|---|
| Xiao Wei Sun | 83 | 880 | 27985 |
| Gang Feng | 82 | 611 | 26012 |
| Feng Xu | 81 | 1075 | 29572 |
| Xiaohui Liu | 81 | 354 | 23301 |
| Dan Li | 80 | 402 | 44693 |
| Qinghua Zhang | 80 | 907 | 31208 |
| Jiquan Chen | 80 | 468 | 27525 |
| Korneel Rabaey | 78 | 314 | 31825 |
| Shengyuan Xu | 78 | 502 | 21593 |
| Bin He | 77 | 786 | 21052 |
| Shiguang Shan | 76 | 475 | 23566 |
| Xuemei Chen | 76 | 281 | 24252 |
| Zhen Wang | 75 | 758 | 28991 |
| Wei Wang | 75 | 1963 | 35183 |
| Feng Liu | 75 | 628 | 21983 |