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 AIE-active ratiometric fluorescent probes for mercury(II) based on the Hg2+-promoted deprotection of thioketal, and good mechanochromic properties

Abstract: Novel AIE-active ratiometric fluorescent probes for Hg2+ were synthesized, which combined the advantages of the excellent luminescence properties of AIEgens in the solid state and the high selectivity of the Hg2+-promoted deprotection reaction. Upon testing with mercury ions, the luminescent color changed from sky blue to yellow-green almost immediately, while other metal ions (Ag+, Fe3+, Cu2+, Pb2+, Co2+, Cr3+, Al3+, Cd2+, Mg2+, Mn2+, Ba2+, Fe2+, Ca2+, Ni2+, Zn2+, Li+, K+, and Na+) caused no disturbance to the sensing process. Furthermore, the fabricated test strips reported the presence of Hg2+ ions with the detection limit as low as 1 × 10−5 M. Moreover, the synthesized AIEgens showed reversible mechanochromic properties with high color contrast, suggesting that they could have additional promising applications in optoelectronic devices.

“Reactive” probe for hydrogen sulfite: “turn-on” fluorescent sensing and bioimaging application

Abstract: Taking advantage of the special nucleophilic addition reaction with aldehyde, a “switching-on” fluorescent probe (C1) for hydrogen sulfite was synthesized using intramolecular charge transfer (ICT) as a signal mechanism Upon the addition of HSO3− ions, the probe displayed apparent fluorescence changes from non-emission to strong green fluorescence C1 gave response to hydrogen sulfite with high sensitivity and the detection limit was determined to be as low as 30 μM In addition to its high selectivity for hydrogen sulfite rather than other common anions, C1 was successfully applied to the detection of hydrogen sulfite in HeLa cells with turn-on fluorescent methods

Miracles of molecular uniting

Abstract: 有机材料的宏观性质是分子聚集效应的客观体现, 不仅取决于单个分子的结构, 而且与整个分子的聚集形式密切相关. 通过对分子聚集态行为的有效调控, 科学家们发现了一些完全不同于单个分子特性的聚集态发光现象, 包括发光强度、颜色、形式以及激发过程的差异. 本文对这些有趣的聚集态发光材料进行了简要的综述, 系统分析了分子聚集模式和分子间相互作用对材料发光性能的影响, 并介绍了“MUSIC”的理念, 以音乐创作形象化材料设计, 强调分子聚集态行为的重要性.

Electric field controlled CO2 capture and CO2/N2 separation on MoS2 monolayers.

Abstract: Developing new materials and technologies for efficient CO2 capture, particularly for separation of CO2 post-combustion, will significantly reduce the CO2 concentration and its impacts on the environment. A challenge for CO2 capture is to obtain high performance adsorbents with both high selectivity and easy regeneration. Here, CO2 capture/regeneration on MoS2 monolayers controlled by turning on/off external electric fields is comprehensively investigated through a density functional theory calculation. The calculated results indicate that CO2 forms a weak interaction with MoS2 monolayers in the absence of an electric field, but strongly interacts with MoS2 monolayers when an electric field of 0.004 a.u. is applied. Moreover, the adsorbed CO2 can be released from the surface of MoS2 without any energy barrier once the electric field is turned off. Compared with the adsorption of CO2, the interactions between N2 and MoS2 are not affected significantly by the external electric fields, which indicates that MoS2 monolayers can be used as a robust absorbent for controllable capture of CO2 by applying an electric field, especially to separate CO2 from the post-combustion gas mixture where CO2 and N2 are the main components.

Irradiated Graphene Loaded with SnO2 Quantum Dots for Energy Storage

Abstract: Tin dioxide (SnO2) and graphene are unique strategic functional materials with widespread technological applications, particularly in the areas of solar batteries, optoelectronic devices, and solid-state gas sensors owing to advances in optical and electronic properties. Versatile strategies for microstructural evolution and related performance of SnO2 and graphene composites are of fundamental importance in the development of electrode materials. Here we report that a novel composite, SnO2 quantum dots (QDs) supported by graphene nanosheets (GNSs), has been prepared successfully by a simple hydrothermal method and electron-beam irradiation (EBI) strategies. Microstructure analysis indicates that the EBI technique can induce the exfoliation of GNSs and increase their interlayer spacing, resulting in the increase of GNS amorphization, disorder, and defects and the removal of partial oxygen-containing functional groups on the surface of GNSs. The investigation of SnO2 nanoparticles supported by GNSs (SnO2/G...

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.