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.

Fabrication of regular ZnO/TiO2 heterojunctions with enhanced photocatalytic properties.

Abstract: Facet-selective growth: Regular ZnO/TiO2 heterojunctions have been successfully synthesized using a facile hydrothermal technique (see figure). Due to the interfacial lattice matching, wurtzite ZnO can only grow on the eight {101} facets of the anatase TiO2 single crystals, while the other two {001} facets are untouched. The as-prepared regular ZnO/TiO2 heterojunctions exhibited enhanced photocatalytic generation of (·)OH radicals and enhanced photodegradation of methyl orange when irradiated with UV light.

Transition from core-shell to janus segregation pattern in AgPd nanoalloy by Ni doping for the formate oxidation

Abstract: Developing the nanoalloys with well-designed chemical ordering and understanding the correlation between the chemical ordering and catalytic property remain a challenge, yet represent an effective strategy to improve the performance of the nanoalloys. Herein, three AgPd-based nanoalloys with different composition-segregation types, namely, alloy (mixed-Ag19Pd72Ni9), core-shell (core-shell-Ag40Pd60) and janus (janus-Ag20Pd60Ni20) atomic arrangements, are developed via a successive co-reduction method and used in the formate oxidation reaction (FOR). The janus-Ag20Pd60Ni20 nanoalloy exhibits an electrocatalytic activity of 1.31 A mgPd−1 and remarkable long-term stability, outperforming the commercial Pd/C catalysts. The FOR activity follows the order of janus-Ag20Pd60Ni20 > core-shell-Ag40Pd60 > mixed-Ag19Pd72Ni9 nanoalloys. After galvanic replacement and acid treatment, the obtained galvanic-Ag20Pd60Ni20 and acid-Ag20Pd60Ni20 nanoalloys demonstrate 2.31 and 1.44 times higher mass activity than the janus-Ag20Pd60Ni20 nanoalloy. The improvements in the activity and stability can be attributed to largely increased Pd active sites on the surface of AgPd-based nanoalloys with optimal chemical ordering.

A Survey on Mobile Landmark Recognition for Information Retrieval

Abstract: The growing usage of mobile devices has led to proliferation of many mobile applications. A growing trend in mobile applications is centered on mobile landmark recognition. It is a new mobile application that recognizes a captured landmark using the mobile device and retrieves related information. This paper will present a survey on mobile landmark recognition for information retrieval. A general overview of existing mobile landmark recognition systems will be summarized. The techniques and algorithms used in the literatures, including content analysis of landmarks and classification methods for recognition, will be described.

Effects of magnetic field strength and particle aggregation on relaxivity of ultra-small dual contrast iron oxide nanoparticles

Abstract: This study aims to compare the relaxivities of ultra-small dual positive and negative contrast iron oxide nanoparticles (DCION) at different magnetic field strengths ranging from 4.7 to 16.4 T at physiological temperatures; and to investigate the effect of particle aggregation on relaxivities. Relaxivities of DCIONs were determined by magnetic resonance imaging scanners at 4.7, 7, 9.4, and 16.4 T. Both longitudinal (T 1) and transverse relaxation times (T 2) were measured by appropriate spin-echo sequences. It has been found that both longitudinal and transverse relaxivities are significantly dependent on the magnetic field strength. Particle aggregation also strongly affects the relaxivities. Awareness of the field strength and particle colloid stability is crucial for the comparison and evaluation of relaxivity values of these ultra-small iron oxide nanoparticles, and also for their medical applications as contrast agents.

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.