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.

Ultra-thin anatase TiO2 nanosheets dominated with {001} facets: thickness-controlled synthesis, growth mechanism and water-splitting properties

Abstract: Ultra-thin anatase TiO2 nanosheets with dominant {001} facets (∼82%) and controllable thickness (1.6–2.7 nm) were synthesized by using a modified one-pot hydrothermal route. As a morphology controlling agent, the concentration of hydrofluoric acid has a significant impact on the thickness of the as-synthesized TiO2 nanosheets. In addition, according to the XRD patterns and TEM images of the products on different reaction stages, the growth process of TiO2 nanosheets was clarified for the first time. We further measured the efficiency for H2 evolution of the ultra-thin anatase TiO2 nanosheets loaded with 1 wt% Pt from photochemical reduction of water in the presence of methanol as a scavenger. The TiO2 nanosheets exhibited a H2 evolution rate as high as 7381 μmol h−1 g−1 under UV-vis light irradiation, attributing to their exposed reactive {001} facets and high crystallinity.

Graphene sheets from worm-like exfoliated graphite

Abstract: High quality graphene sheets have been prepared by a facile liquid phase exfoliation of worm-like graphite (WEG). This approach combining with the advances in large scale industry manufacturing of WEG could potentially lead to the development of new and more effective graphene products.

Unidirectional suppression of hydrogen oxidation on oxidized platinum clusters

Abstract: Solar-driven water splitting to produce hydrogen may be an ideal solution for global energy and environment issues. Among the various photocatalytic systems, platinum has been widely used to co-catalyse the reduction of protons in water for hydrogen evolution. However, the undesirable hydrogen oxidation reaction can also be readily catalysed by metallic platinum, which limits the solar energy conversion efficiency in artificial photosynthesis. Here we report that the unidirectional suppression of hydrogen oxidation in photocatalytic water splitting can be fulfilled by controlling the valence state of platinum; this platinum-based cocatalyst in a higher oxidation state can act as an efficient hydrogen evolution site while suppressing the undesirable hydrogen back-oxidation. The findings in this work may pave the way for developing other high-efficientcy platinum-based catalysts for photocatalysis, photoelectrochemistry, fuel cells and water-gas shift reactions.

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.