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.

Novel global-like second-order nonlinear optical dendrimers: convenient synthesis through powerful click chemistry and large NLO effects achieved by using simple azo chromophore

Abstract: In this paper, global-like dendrimers, G1-TPA, G2-TPA and G3-TPA, bearing 9, 21 and 45 azobenzene chromophore moieties, respectively, were conveniently prepared with high purity and satisfactory yields, through the combination of divergent and convergent approaches, coupled with the utilization of the powerful Sharpless “click” reaction. Due to their perfect 3D structure and the isolation effect of the exterior benzene moieties and the interior triazole rings, these global-like dendrimers exhibited large d33 (a second harmonic generation coefficient) values, e.g., 246.0 pm V−1 for G3-TPA, which, to the best of our knowledge, is a new record reported so far for simple azo chromophore moieties.

A new approach to prepare efficient blue AIE emitters for undoped OLEDs.

Abstract: Two aggregation-induced emission active luminogens (TPE-pTPA and TPE-mTPA) were successfully synthesized. For comparison, another six similar compounds were prepared. Because of the introduced hole-dominated triphenylamine (TPA), fluorene groups with high luminous efficiency, and unconjugated linkages, the π conjugation length of the obtained luminogens is effectively restricted to ensure their blue emission. The undoped organic light-emitting diodes based on TPE-pTPA and TPE-mTPA exhibited blue or deep-blue emissions, low turn-on voltages (3 V), and high electroluminescence efficiencies with Lmax, ηC,max, and ηP,max values of up to 26,697 cd m(-2), 3.37 cd A(-1), and 2.40 Lm W(-1).

Colorimetric cyanide detection using an azobenzene acid in aqueous solutions

Abstract: An azo-based dye (I) was designed for the detection of cyanide by utilizing a new indirect method. In the presence of Cu(II), compound I could give rise to visible red-to-yellow color change. The resultant yellow solution could change to red immediately upon the addition of trace cyanide, with the detection limit of 0.15 ppm, but no changes were observed in the presence of other anions, including Cl−, I−, IO3−, SO42−, NO2−, Br−, H2PO4−, F−, SCN−, HSO4−, ClO4− and CN−, making compound I a selective and sensitive cyanide chemosensor.

Graphene-CdSe nanobelt solar cells with tunable configurations

Abstract: We have combined two planar nanostructures, graphene and CdSe nanobelts, to construct Schottky junction solar cells with open-circuit voltages of about 0.5 V and cell efficiencies on the order of 0.1%. By covering transparent graphene or carbon nanotube (CNT) films on selected positions along macroscopically long CdSe nanobelts, we have demonstrated the fabrication of active solar cells with many different configurations and parallel connections from individual or multiple assembled nanobelts. The graphene-CdSe nanobelt solar cells reported here show a great flexibility in creating diverse device architectures, and might be scaled up for cell integration based on assembled nanobelt arrays and patterned graphene (or CNT) films.

I and i

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 …

Fast parallel algorithms for short-range molecular dynamics

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.

Highly accurate protein structure prediction with AlphaFold

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.