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.

The influence of pentafluorophenyl groups on the nonlinear optical (NLO) performance of high generation dendrons and dendrimers

Abstract: With the aim to make the influence of pentafluorophenyl groups in the periphery of high generation dendrons and dendrimers on their NLO performance clearly, some NLO dendrons and dendrimers with different chromophore moieties or different end-capped groups were carefully designed and investigated in detail. The results demonstrated that some strong Ar-ArF interactions between the pentafluorophenyl groups in the periphery and the normal phenyl rings of the donors, could influence the topological structures of dendrons or dendrimers, and then affect their NLO performance. Furthermore, the optical transparency and the stability of the dendrons and dendrimers with pentafluorophenyl groups as end-capped moieties were all improved, in comparison with normal dendrons and dendrimers containing phenyl ones as the end-capped groups.

Dual-Network Structured Hydrogel Electrolytes Engaged Solid-State Rechargeable Zn-Air/Iodide Hybrid Batteries.

Abstract: As a key component of batteries, the electrolyte determines the ion transport and interface chemistry of the cathode and anode. In this work, we develop a dual-network structured hydrogel electrolyte composed of polyacrylamide (PAM), sodium alginate (SA) and potassium iodide (KI) for solid-state zinc-air/iodide hybrid batteries. The assembled hybrid battery shows excellent renewability and a long cycling life of 110 h with a high energy efficiency of 80%. The ion-crosslinked dual-network structure endows the material with improved mechanical strength and increased ionic conductivity. More importantly, the introduction of iodine species not only offers more favorable cathodic kinetics of iodide/iodate redox than oxygen electrocatalysis but also regulates the solvation structure of zinc ions to ensure better interface stability. This work provides significant concepts for developing novel solid-state electrolytes to realize high-performance energy devices and technologies.

Changes in soil aggregate‐associated enzyme activities and nutrients under long‐term chemical fertilizer applications in a phosphorus‐limited paddy soil

Abstract: Paddy soils in subtropical China are usually deficient in phosphorus (P) and require regular application of chemical fertilizers. This study evaluated the effects of chemical fertilizers on the distribution of soil organic carbon (SOC), total nitrogen (N) and available P, and on the activity of the associated enzymes in bulk soil and aggregates. Surface soils (0–20 cm) were collected from a 24-yr-old field experiment with five treatments: unfertilized control (CK), N only (N), N and potassium (NK), N and P (NP), and N, P and K (NPK). Undisturbed bulk soils were separated into >2, 1–2, 0.25–1, 0.053–0.25 and 0.25 mm), which accounted for 64–81% of SOC and 54–82% of total N in bulk soil. The activities of invertase and acid phosphatase in the 1–2 mm fraction were the highest under NPK treatment. The highest activity of urease was observed in the <0.053 mm fraction under NP treatment. Soil organic carbon and available P were major contributors to variation of enzyme activities at the aggregate scale. In conclusion, application of NP or NPK fertilizers promoted the formation of soil aggregates, nutrient contents and activities of associated enzymes in P-limited paddy soils, and thus enhanced soil quality.

The crucial roles of the configurations and electronic properties of organic hole-transporting molecules to the photovoltaic performance of perovskite solar cells

Abstract: Perovskite solar cells have become one of the most promising technologies to make use of solar energy; to date, the power conversion efficiencies (PCEs) have been improved from 3.8% to 25.6%. Hole-transporting materials (HTMs) play an important role in the photovoltaic conversion process by extracting photogenerated holes and transporting charges. However, the relationship among the molecular structure of HTMs, molecular packing in hole-transporting layers (HTLs) and the device performance of perovskite solar cells (PSCs) has not been explained systematically. In this review, the structure–property relationship of HTMs is discussed from the aspects of molecular configuration, electron properties, and their synergetic effects, to provide useful guidance for the HTM design and PSC development.

A rigid ringlike molecule: large second-order nonlinear optical performance, good temporal and thermal stability, and ideal spherical structure conforming to the “site isolation” principle

Abstract: In this paper, according to the principle of site isolation, a ringlike molecule of R1, based on the second-order nonlinear optical chromophore of FTC (Fang's Thermally-Stable Chromophore), was designed and synthesized, in which, the heads and waists of two FTC moieties were locked together through chemical bonds, respectively. Thanks to the ideal spherical structure and good alignment of the two pieces of FTC moieties, R1 exhibited an ultra high nonlinear optical effect with a d33 value of 562 pm V−1 at a wavelength of 1950 nm, which could still retain 80% at a very high temperature of 145 °C.

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.