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.

Nitrogen and fluorine dual-doped mesoporous graphene: a high-performance metal-free ORR electrocatalyst with a super-low HO2− yield

Abstract: In this study, we successfully, for the first time, prepared nitrogen and fluorine dual-doped mesoporous graphene (NF–MG) via the thermal treatment of graphene oxide/polyaniline composites (GO/PANI) and NH4F. Benefiting from the synergistic effect of N and F co-doping into the G framework, the oxygen reduction reaction performance of the optimal catalyst (NF–MG3) is comparable with the-state-of-the-art Pt/C catalyst in an alkaline medium, which makes it an ideal candidate as an efficient metal-free ORR electrocatalyst in fuel cells.

Nitrogen-doped activated carbons derived from a co-polymer for high supercapacitor performance

Abstract: Nitrogen-doped activated carbons (N-ACs) with controlled nitrogen doping and analogous microporous structures were prepared by pyrolysis of poly[(pyrrole-2,5-diyl)-co-(benzylidene)] (PPCB). The obtained N-ACs were thoroughly characterized using HRTEM, FESEM, BET, FTIR and XPS for their morphology, surface area and chemical composition. The N-ACs were further used to fabricate supercapacitors, and their comprehensive electrochemical properties, such as cyclic voltammograms, galvanostatic charge–discharge, electrochemical impedance spectrum, electrochemical capacitive performance, power density and long cyclic stability, were studied. The galvanostatic charge–discharge (GC) measurements on N-ACs produced at 700 °C and 800 °C show a high specific capacitance (up to 525.5 F g−1 and an energy density of ca. 262.7 W h kg−1 at 0.26 A g−1) in alkaline media (2 M KOH). More importantly, the capacitance remains practically identical when the scan rate was increased from 0.26 to 26.31 A g−1. The observed capacitance retention (∼99.5%) of N-ACs is remarkably stable for electrodes even after 4000 cycles, due to the presence of nitrogen at the surface and in the graphitic edge planes. The nitrogen content plays a significant role in producing micropore dominated ACs and in facilitating the transfer of ions through pores on the surface. The precursor (PPCB) used is cheap and can easily be prepared, making it promising for the large-scale production of N-ACs as excellent electrode materials for supercapacitors.

Highly twisted double-helix carbon nanotube yarns.

Abstract: The strength and flexibility of carbon nanotubes (CNTs) allow them to be constructed into a variety of innovated architectures with fascinating properties. Here, we show that CNTs can be made into a highly twisted yarn-derived double-helix structure by a conventional twist-spinning process. The double-helix is a stable and hierarchical configuration consisting of two single-helical yarn segments, with controlled pitch and unique mechanical properties. While one of the yarn components breaks early under tension due to the highly twisted state, the second yarn produces much larger tensile strain and significantly prolongs the process until ultimate fracture. In addition, these elastic and conductive double-helix yarns show simultaneous and reversible resistance change in response to a wide range of input sources (mechanical, photo, and thermal) such as applied strains or stresses, light illumination, and environmental temperature. Our results indicate that it is possible to create higher-level, more complex...

Controlled synthesis of copper telluride nanostructures for long-cycling anodes in lithium ion batteries

Abstract: Copper telluride nanocubes, nanosheets, and nanoparticles were prepared by a solvothermal method under the protection of an inert atmosphere. The influence of reaction parameters, including precursor concentration, precursor ratio, precursor type, reaction time, reaction temperature, solvent, and organic ligands, on the size, morphology, crystalline structure, and composition of the resultant copper telluride nanostructures was comprehensively investigated. The results showed that the crystal structure and composition of the resultant nanostructures varied case by case, demonstrating the complexity of copper tellurides, despite their simple molecular formula. The obtained copper telluride nanostructures were tested as anodes in lithium ion batteries. The assembled Li/LiPF6/CuxTe cells exhibit extremely high cycling stability (up to 5000 cycles) and their highest specific capacity is 280 mA h g−1. The results also showed better performance of the Cu2−xTe nanosheet electrodes than those of electrodes made from nanoparticles and nanocubes, demonstrating the importance of controlling the morphology of copper telluride during preparation.

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.