scispace - formally typeset
Search or ask a question
Author

Zhen Li

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.


Papers
More filters
Journal ArticleDOI
TL;DR: In this article, the corrosion of X70 steel and iron in supercritical CO2/SO2/O2/H2O environment was investigated after a 454 h exposure.
Abstract: The corrosion of X70 steel and iron in supercritical CO2/SO2/O2/H2O environment were investigated after a 454 h exposure. Optical microscopy was applied to observe the morphology of etch pits and synthesise the three-dimensional morphology. X-ray diffraction and X-ray photoelectron spectroscopy were employed to detect the composition of product scales. Experimental results verified that the localised corrosion occurred on the X70 steel sample under corrosion product deposits. Ferrous sulphate, sulphur and iron sulphide were detected as the corrosion products.

20 citations

Journal ArticleDOI
TL;DR: In this paper, an NLO polymer P2, consisting of a star-type chromophore, was first prepared through the Suzuki coupling reaction and the Sonogashira coupling reaction.

20 citations

Journal ArticleDOI
TL;DR: A conjugated hyperbranched polymer (hb-TFO) containing tetraphenylethylene (TPE) units, a famous aggregation-induced emission (AIE) active group, as the core was synthesized successfully with modest yield via one-pot Suzuki polymerization reaction.
Abstract: A new conjugated hyperbranched polymer (hb-TFO) containing tetraphenylethylene (TPE) units, a famous aggregation-induced emission (AIE) active group, as the core, was synthesized successfully with modest yield via one-pot Suzuki polymerization reaction. Thanks to the introduction of TPE moieties, hb-TFO exhibited aggregation-enhanced emission (AEE) property, and could work as explosive chemosensor with high sensitivity. The polymeric light-emitting diode (PLED) device was fabricated to investigate its electroluminescent property, and hb-TFO demonstrated a maximum luminance efficiency of 0.22 cd/A and a maximum brightness of 545 cd/m2 at 15.9 V.

20 citations

Journal ArticleDOI
TL;DR: In this article, a new pyran (1) was synthesized, which is practically non-emissive when molecularly dissolved in acetone and when added to water induces it to cluster into nanoaggregates, which turns its emission "on" and boosts its luminescence efficiency dramatically.
Abstract: A new pyran (1) is synthesized. When molecularly dissolved in acetone, 1 is practically nonemissive. Addition of water induces it to cluster into nanoaggregates, which turns its emission "on" and boosts its luminescence efficiency dramatically. The color of its emission changes from green to red upon aggregation.

20 citations

Journal ArticleDOI
TL;DR: In this paper, a simple solution method was proposed for the synthesis of monodispersed and core-shell structured ZnO-Ag microspheres by coating the Ag nanoparticles onto the surface of ZnOs via a novel solution method.

20 citations


Cited by
More filters
Journal ArticleDOI

[...]

08 Dec 2001-BMJ
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

Journal ArticleDOI
15 Jul 2021-Nature
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