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.

A black phosphorus nanometer particle with biocompatibility, a preparing method thereof and applications of the black phosphorus nanometer particle

Abstract: The invention discloses a black phosphorus nanometer particle with biocompatibility, a preparing method thereof and applications of the black phosphorus nanometer particle According to the black phosphorus nanometer particle, black phosphorus is subjected to in-situ modification with a biocompatible polymer in a preparing process so that the water dispersibility and biocompatibility are good The preparing method is simple in operation, easy to popularize and high in yield, and avoids a disadvantage that black phosphorus reacts with a large amount of water and oxygen for long time in an open system and other disadvantages The prepared black phosphorus nanometer particle is uniform in particle size, high in degree of crystallinity and high in light-thermal conversion efficiency, and can be used for the field of biomedicine, such as photoacoustic imaging and photothermal therapy

Bright mechanoluminescent luminogens even in daylight through close intermolecular interaction with the characteristic of hybridized local and charge transfer (HLCT)

Abstract: The study of mechanoluminescence (ML) has attracted much attention for its widespread applications. Until now, organic ML luminogens are still very scarce, especially those with bright emissions. Herein, three donor–acceptor (D–A) molecules with triphenylamine, phenothiazine and phenoxazine as electron donors and p-fluorophenylcarbonyl as an electron acceptor are designed and synthesized via a facile approach with the Friedel–Crafts reaction in one step, named FCO-TPA, FCO-CzS and FCO-CzO, which exhibit high emission quantum yields both in solution and solid states. The fluorescence solvatochromic experiments and theoretical calculations prove their unique HLCT state characteristics, which can largely enhance the excited state energy utilization. Accordingly, very bright mechanoluminescence (ML) even in daylight is achieved in FCO-TPA with non-centrosymmetric molecular arrangement and close intermolecular interactions.

Polymorphism of pharmaceutical molecules: perspectives on nucleation

Abstract: Polymorphism is a widespread phenomenon observed in more than half of all drug substances. Various polymorphs frequently possess different physical, chemical, mechanical and thermal properties that can profoundly affect the bioavailability, stability and other performance characteristics of the drug. Accordingly, the elucidation of the relationship between the particular polymorph of a pharmaceutical molecule and its functional properties is crucial to select the most suitable polymorph of the pharmaceutical molecule for development into a drug product. This review briefly introduces recent advances in the discovery and control of the polymorphs of pharmaceutical molecules, in terms of the enhancement of the selective nucleation of a particular polymorph. In the light of this, some cases discussed in the following is to be considered controversial.

Synthesis of magnetic hollow periodic mesoporous organosilica with enhanced cellulose tissue penetration behaviour

Abstract: Commercially available barium ferrite BaFe12O19 (BaFeO) nanoparticles with a size of ∼100 nm have been successfully encapsulated inside the hollow periodic mesoporous organosilica (HPMO) host material, through a 2-step (coating and encapsulation) approach. The resultant magnetic HPMO (MHPMO) nanoparticles possess a relatively high saturated magnetization (25 emu g−1) and a high enzyme loading capacity (1.32 mg/mg). It is further demonstrated that MHPMO materials exhibited enhanced cellulose tissue penetration behaviour under applied external magnetic field, promising for delivery applications to plant cells.

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.