Author
Zhen Li
Other affiliations: Tsinghua University, Hong Kong University of Science and Technology, Academia Sinica ...read more
Bio: Zhen Li is an academic researcher from Wuhan University. The author has contributed to research in topics: Medicine & Computer science. The author has an hindex of 127, co-authored 1712 publications receiving 71351 citations. Previous affiliations of Zhen Li include Tsinghua University & Hong Kong University of Science and Technology.
Topics: Medicine, Computer science, Materials science, Chemistry, Biology
Papers published on a yearly basis
Papers
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TL;DR: Experimental measurements of the second-order nonlinear optical response demonstrated that the chromophores exhibit similar or superior optical nonlinearity compared with their analogues with an aniline moiety as the donor group, but the indole-based Chromophores display blue-shifted absorption, even up to 30 nm.
Abstract: Push−pull indole-containing nonlinear optical chromophores with different acceptor and π-conjugated moieties have been synthesized and characterized Experimental measurements of the second-order nonlinear optical response demonstrated that the chromophores exhibit similar or superior optical nonlinearity compared with their analogues with an aniline moiety as the donor group, but the indole-based chromophores display blue-shifted absorption, even up to 30 nm
61 citations
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TL;DR: The research provides a novel ambient approach for preparation of Cu(2-x)Se nanocrystallines on a large scale for various applications.
Abstract: Grams of copper selenides (Cu2–xSe) were prepared from commercial copper and selenium powders in the presence of thiol ligands by a one-pot reaction at room temperature. The resultant copper selenides are a mixture of nanoparticles and their assembled nanosheets, and the thickness of nanosheets assembled is strongly dependent on the ratio of thiol ligand to selenium powder. The resultant Cu2–xSe nanostructures were treated with hydrazine solution to remove the surface ligands and then explored as a potential thermoelectric candidate in comparison with commercial copper selenide powders. The research provides a novel ambient approach for preparation of Cu2–xSe nanocrystallines on a large scale for various applications.
61 citations
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12 Dec 2019TL;DR: In this paper, an attention-guided network is proposed to segment the cataract surgical instrument, which captures global context and encodes semantic dependencies to emphasize key semantic features, boosting the feature representation.
Abstract: Semantic segmentation of surgical instruments plays a crucial role in robot-assisted surgery. However, accurate segmentation of cataract surgical instruments is still a challenge due to specular reflection and class imbalance issues. In this paper, an attention-guided network is proposed to segment the cataract surgical instrument. A new attention module is designed to learn discriminative features and address the specular reflection issue. It captures global context and encodes semantic dependencies to emphasize key semantic features, boosting the feature representation. This attention module has very few parameters, which helps to save memory. Thus, it can be flexibly plugged into other networks. Besides, a hybrid loss is introduced to train our network for addressing the class imbalance issue, which merges cross entropy and logarithms of Dice loss. A new dataset named Cata7 is constructed to evaluate our network. To the best of our knowledge, this is the first cataract surgical instrument dataset for semantic segmentation. Based on this dataset, RAUNet achieves state-of-the-art performance 97.71\(\%\) mean Dice and 95.62\(\%\) mean IOU.
60 citations
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TL;DR: This work fabricated CNT sponge nanocomposites by directly infiltrating epoxy fluid into the CNT framework while maintaining the original network structure and CNT contact, with simultaneous improvement in mechanical and electrical properties.
Abstract: Fabrication of high-performance nanocomposites requires that the nanoscale fillers be dispersed uniformly and form a continuous network throughout the matrix. Direct infiltration of porous CNT sponges consisting of a three-dimensional nanotube scaffold may provide a possible solution to this challenge. Here, we fabricated CNT sponge nanocomposites by directly infiltrating epoxy fluid into the CNT framework while maintaining the original network structure and CNT contact, with simultaneous improvement in mechanical and electrical properties. The resulting composites have an isotropic structure with electrical resistivities of 10 to 30 Ω·cm along arbitrary directions, much higher than traditional composites by mixing random CNTs with epoxy matrix. We observed reversible resistance change in the sponge composites under compression at modest strains, which can be explained by tunneling conduction model, suggesting potential applications in electromechanical sensors.
60 citations
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TL;DR: In this paper, the triarylamine-based organic hole-transport material (HTM) doped with its oxidized salt analogue (EH44/EH44-ox) led to unencapsulated PSCs with high stability in ambient conditions.
Abstract: With metal halide perovskite solar cells (PSCs) now reaching device efficiencies >23%, more emphasis must now shift toward addressing their device stability. Recently, a triarylamine-based organic hole-transport material (HTM) doped with its oxidized salt analogue (EH44/EH44-ox) led to unencapsulated PSCs with high stability in ambient conditions. Here we report criteria for triarylamine-based organic HTMs formulated with stable oxidized salts as hole-transport layer (HTL) for increased PSC thermal stability. The triarylamine-based dopants must contain at least two para-electron-donating groups for radical cation stabilization to prevent impurity formation that leads to reduced PSC performance. The stability of unencapsulated devices prepared using these new HTMs stressed under constant load and illumination far outperforms that of both EH44/EH44-ox and Li+-doped spiro-OMeTAD controls at 50 °C. Furthermore, the ability to mix and match these dopants with a nonidentical small-molecule-based HTL matrix broa...
60 citations
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TL;DR: There is, I think, something ethereal about i —the square root of minus one, which seems an odd beast at that time—an intruder hovering on the edge of reality.
Abstract: There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.
Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …
33,785 citations
01 May 1993
TL;DR: Comparing the results to the fastest reported vectorized Cray Y-MP and C90 algorithm shows that the current generation of parallel machines is competitive with conventional vector supercomputers even for small problems.
Abstract: Three parallel algorithms for classical molecular dynamics are presented. The first assigns each processor a fixed subset of atoms; the second assigns each a fixed subset of inter-atomic forces to compute; the third assigns each a fixed spatial region. The algorithms are suitable for molecular dynamics models which can be difficult to parallelize efficiently—those with short-range forces where the neighbors of each atom change rapidly. They can be implemented on any distributed-memory parallel machine which allows for message-passing of data between independently executing processors. The algorithms are tested on a standard Lennard-Jones benchmark problem for system sizes ranging from 500 to 100,000,000 atoms on several parallel supercomputers--the nCUBE 2, Intel iPSC/860 and Paragon, and Cray T3D. Comparing the results to the fastest reported vectorized Cray Y-MP and C90 algorithm shows that the current generation of parallel machines is competitive with conventional vector supercomputers even for small problems. For large problems, the spatial algorithm achieves parallel efficiencies of 90% and a 1840-node Intel Paragon performs up to 165 faster than a single Cray C9O processor. Trade-offs between the three algorithms and guidelines for adapting them to more complex molecular dynamics simulations are also discussed.
29,323 citations
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28,685 citations
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TL;DR: For example, AlphaFold as mentioned in this paper predicts protein structures with an accuracy competitive with experimental structures in the majority of cases using a novel deep learning architecture. But the accuracy is limited by the fact that no homologous structure is available.
Abstract: Proteins are essential to life, and understanding their structure can facilitate a mechanistic understanding of their function. Through an enormous experimental effort1–4, the structures of around 100,000 unique proteins have been determined5, but this represents a small fraction of the billions of known protein sequences6,7. Structural coverage is bottlenecked by the months to years of painstaking effort required to determine a single protein structure. Accurate computational approaches are needed to address this gap and to enable large-scale structural bioinformatics. Predicting the three-dimensional structure that a protein will adopt based solely on its amino acid sequence—the structure prediction component of the ‘protein folding problem’8—has been an important open research problem for more than 50 years9. Despite recent progress10–14, existing methods fall far short of atomic accuracy, especially when no homologous structure is available. Here we provide the first computational method that can regularly predict protein structures with atomic accuracy even in cases in which no similar structure is known. We validated an entirely redesigned version of our neural network-based model, AlphaFold, in the challenging 14th Critical Assessment of protein Structure Prediction (CASP14)15, demonstrating accuracy competitive with experimental structures in a majority of cases and greatly outperforming other methods. Underpinning the latest version of AlphaFold is a novel machine learning approach that incorporates physical and biological knowledge about protein structure, leveraging multi-sequence alignments, into the design of the deep learning algorithm. AlphaFold predicts protein structures with an accuracy competitive with experimental structures in the majority of cases using a novel deep learning architecture.
10,601 citations