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.

Crystallization mechanisms and properties of α-cordierite glass–ceramics from K2O–MgO–Al2O3–SiO2 glasses

Abstract: Low temperature co-fired cordierite glass–ceramics were fabricated with K2O–MgO–Al2O3–SiO2 glasses. It was confirmed that α-cordierite crystals were precipitated from the glasses directly below 950 °C and at the higher temperature additional leucite crystals were formed. The calculation of crystallization activation energy (Eα) for a onefold α-cordierite was 230.77–279.81 kJ/mol; and the Eα of the concurrent precipitation of α-cordierite and leucite was 348.85–374.33 kJ/mol, by Kissinger and Ozawa methods. Moreover, the Avrami constant (n) was calculated to be about 2 for onefold α-cordierite and to be 2.27–2.91 for the concomitant α-cordierite and leucite by the Augis–Bennett equation. Therefore, a bulk nucleation with one-dimensional crystal growth mechanism for α-cordierite was determined, which was verified by the results of Scanning Electron Microscope (SEM). The mechanisms for the concomitant α-cordierite and leucite changed from one-dimensional crystal growth to two-dimensional crystal growth with the variation of chemical composition. Furthermore, the α-cordierite glass–ceramics from the low-cost natural material exhibited good sintering capability below 950 °C, low dielectric constant (6.0–8.0) and loss (below 0.01), matchable thermal expansion (4.40–5.63 × 10− 6 K− 1) and applicable flexural strength, which were essential for the application as low temperature co-fired ceramic (LTCC) substrates.

Heavy Metal Level in Human Semen with Different Fertility: a Meta-Analysis

Abstract: There are conflicting reports on the heavy metal levels in human semen with different fertilities. The purpose of this analysis is to merge and analyze the differences of heavy metal lead (Pb), cadmium (Cd), zinc (Zn), and copper (Cu) levels in male semen with normal and low fertilities. All documents in both Chinese and English were collected from the PubMed, Web of Science, and Chinese National Knowledge Infrastructure (CNKI) database from inception date to February 19, 2016. We have used RevMan software (version 5.2) for the meta-analysis and Stata software (version 12.0) for the meta-regression and sensitivity analyses. A total of 20 literatures were included in the study. The results of the meta-analysis indicate a significant difference between fertility with three metal ions (Pb, Cd, Zn) while no significant difference with copper, detailed as follows: (i) 10 studies on the lead concentrations with a standardized mean difference (SMD) = 2.07, 95 %CI (0.97, 3.17), P < 0.01; (ii) 13 studies on the cadmium concentrations with an SMD = 0.75, 95 %CI (0.44, 1.07), P < 0.01; (iii) 8 studies on the concentrations of zinc with an SMD = −0.61, 95 %CI (−1.08, −0.14), P < 0.01; and (iv) 9 studies on the copper concentrations with an SMD = 0.42, 95 %CI (−0.29, 1.13), P = 0.247. The results indicate that the men with low fertility have higher semen Pb and Cd levels and lower semen Zn levels; more studies are needed to indicate the association of the semen copper level with fertility.

New indole-based light-emitting oligomers: structural modification, photophysical behavior, and electroluminescent properties.

Abstract: A novel series of indole-based conjugated oligomers were synthesized by Wittig−Horner−Emmons olefination from the aryl-bridged bisindole aldehydes and the corresponding bisphosphonates. The introduction of indole into the light-emitting materials made these oligmers exhibit favorable properties. They were thermally stable, and the UV−vis spectra of the oligomers could be modulated by the arylenevinylene units; their PL and EL spectra also showed similar properties that can be further modulated. The highest luminance achieved in a device with the configuration ITO/PEDOT:PSS/oligomer/Ba/Al was 2536 cd/m2 at 7.5 V for oligomer P6, and the highest external EL quantum efficiency of 0.39% and luminous efficiency of 0.97 cd/A were attained by oligomer P1.

The marriage of AIE and interface engineering: convenient synthesis and enhanced photovoltaic performance.

Abstract: As a promising option out of all of the well-recognized candidates that have been developed to solve the coming energy crisis, polymer solar cells (PSCs) are a kind of competitive clean energy source. However, as a convenient and efficient method to improve the efficiency of PSCs, the inherent mechanism of the interfacial modification was still not so clear, and interfacial materials constructed with new units were limited to a large degree. Here we present a new kind of interfacial material consisting of AIE units for the first time, with an efficiency of 8.94% being achieved by inserting TPE-2 as a cathode interlayer. This is a relatively high PCE for PC71BM:PTB7-based conventional PSCs with a single-junction structure. Different measurements, including TEM, AFM, SEM, GIXRD, UPS, SKPM, and SCLC, were conducted to investigate the properties in detail. All of the obtained experimental results confirmed the advantages of the utilization of new interfacial materials with AIE characteristics in polymer solar cells, thus providing an additional choice to develop new organic cathode interfacial layers with high performances.

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.