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.

Insights into the mechanism of the reaction between ttrachloro-p-bnzoquinone and hydrogen peroxide and their implications in the catalytic role of water molecules in producing the hydroxyl radial

Abstract: Detailed mechanisms for the formation of hydroxyl or alkoxyl radicals in the reactions between tetrachloro-p-benzoquinone (TCBQ) and organic hydroperoxides are crucial for better understanding the potential carcinogenicity of polyhalogenated quinones. Herein, the mechanism of the reaction between TCBQ and H2O2 has been systematically investigated at the B3LYP/6-311++G** level of theory in the presence of different numbers of water molecules. We report that the whole reaction can easily take place with the assistance of explicit water molecules. Namely, an initial intermediate is formed first. After that, a nucleophilic attack of H2O2 onto TCBQ occurs, which results in the formation of a second intermediate that contains an OOH group. Subsequently, this second intermediate decomposes homolytically through cleavage of the O-O bond to produce a hydroxyl radical. Energy analyses suggest that the nucleophilic attack is the rate-determining step in the whole reaction. The participation of explicit water molecules promotes the reaction significantly, which can be used to explain the experimental phenomena. In addition, the effects of F, Br, and CH3 substituents on this reaction have also been studied.

One‐Dimensional Heterostructures of Single‐Walled Carbon Nanotubes and CdSe Nanowires

Abstract: A one-dimensional heterostructure comprising single-walled carbon nanotubes (CNTs) and CdSe nanowires is prepared via electrochemical deposition of Bi nanoparticles onto the nanotubes (see image), followed by solution–liquid–solid synthesis of the semiconductor nanowires.

1.42-Fold Enhancement of Blue OLED Device Performance by Simply Changing Alkyl Groups on the Acridine Ring

Abstract: Summary Developing high-efficiency blue thermally activated delayed fluorescence (TADF) emitters is still a formidable challenge. Here, we report five blue TADF emitters designed by simply changing the frequently used methyl group (C1) to a longer one gradually to subtly adjust the energy levels. DAc-C1–DAc-C5 are developed by using diphenylsulfone (DPS) as the acceptor and acridine with different-length alkyl chains on C(9) as the donor groups. These five compounds have high photoluminescence quantum yields (PLQYs) (75%–84%) with blue emission. The DAc-C2-based OLED device exhibits the maximum external quantum efficiency (EQE) of up to 24.1% with CIE (0.15, 0.19), which is 1.42 times higher than that of state-of-the-art TADF OLEDs based on DAc-C1 (EQE: 17.0%) under the same conditions, confirming the power of the molecular design strategy. Furthermore, the crystal analyses partially explain the influence of the alkyl chain on the device property, demonstrating a new approach for achieving TADF luminogens with high performance.

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.