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.

Synthesis, characterization and photovoltaic performances of D–A copolymers based on BDT and DBPz: the largely improved performance caused by additional thiophene blocks

Abstract: Two new low bandgap copolymers, PBDT-DBPz and PBDT-DTDBPz, were prepared, in which the only difference was the additional added thiophene bridge between the BDT donor unit and DBPz acceptor unit for PBDT-DTDBPz. In addition to the improvements of UV-vis absorption and hole mobility, the PSC based on PBDT-DTDBPz:PC71BM demonstrated a power conversion efficiency of 4.75%, much higher than that of the device based on PBDT-DBPz:PC71BM (0.46%), under the same experimental conditions.

New Hyperbranched Conjugated Polymers Containing Hexaphenylbenzene and Oxadiazole Units: Convenient Synthesis and Efficient Deep Blue Emitters for PLEDs Application

Abstract: In this article, four “A3+B2+C2”-type hyperbranched conjugated polymers (P1−P4) containing hexaphenylbenzene as the core were synthesized successfully for the first time with high yields through one-pot Suzuki polymerization reaction. The copolymerization percent of 1,3,4-oxadiazole units was adjusted to investigate the effect of polymer composition on the physical, optical, and EL properties. All polymers were well-characterized and exhibited good solubility, film-forming ability, and thermal stability. Both the solution and the films of these hyperbranched polymers emitted pure and stable deep-blue light emission, and their PL spectra did not change after annealing at 150 °C for 0.5 h in air, indicating that the hyperbranched structure, coupled to the introduced hexaphenylbenzene moieties, effectively suppressed the formation of aggregation excimer and keto defects. Two-layer PLED devices were fabricated to investigate the electroluminescence properties of these hyperbranched polymers, and P3 demonstrat...

Toward the development of versatile functionalized carbon nanotubes

Abstract: Surface modification of carbon nanotubes (CNT) with polymers is a general and effective strategy to improve the performance of CNTs for applications. In this work, a facile strategy to synthesize hydrophobic and hydrophilic CNT by mussel inspired chemistry and Single-Electron Transfer Living Radical Polymerization (SET-LRP) was developed for the first time. The successful synthesis of these CNT–polymer composites was confirmed by a series of characterization techniques including transmission electron microscopy, Fourier transform infrared, thermogravimetric analysis and X-ray photoelectron spectra. These CNT exhibited obviously enhanced dispersibility in water and different organic solvents after they were surface functionalized with hydrophilic and hydrophobic polymers. The synthetic strategy is convenient, versatile and environmentally friendly and can be extended for fabrication of many other polymer nanocomposites. Therefore, the method developed in the present work might open a new route to fabricate functional CNT–polymer composites for different applications.

Polyurethanes Containing Indole‐Based Non‐Linear Optical Chromophores: from Linear Chromophore to H‐Type

Abstract: Two new "H" type of indole-based chromophores were designed and successfully introduced to the polymeric system, the resultant polymers demonstrated enhanced NLO effects, good processability, thermal stability and nearly excellent transparency, indicating the advantages of "H" type chromophore moieties. And they could be promising candidates for the practical applications as new photonic materials.

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.