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.

Janus molecules: large second-order nonlinear optical performance, good temporal stability, excellent thermal stability and spherical structure with optimized dendrimer structure

Abstract: In this paper, Janus molecules J1 and J2, based on the second-order nonlinear optical (NLO) chromophore of FTC (Fang's thermally stable chromophore), were designed and synthesized, in which the arrangement of chromophore moieties was different on the two sides of the core. Especially, in J2, each FTC piece had similar direction and a more similar spherical structure realized. Janus molecule J2 exhibited good thermal stability and excellent nonlinear optical performance. Thanks to the unique molecular topology, an ultrahigh d33 value of 529 pm V−1 at the wavelength of 1950 nm has been achieved, with 80% of its value retained at the high temperature of 161 °C. Coupled with the good thermal stability and very simple structure, the obtained results are valuable for the further development of NLO materials.

Recent Advances in Cathode Materials for Room-Temperature Sodium−Sulfur Batteries

Abstract: Room-temperature sodium-sulfur (RT-Na/S) batteries hold great promise to meet the requirements of large-scale energy storage due to their high theoretical energy density, low material cost, resource abundance, and environmental benignity. However, the poor cycle performance and low utilization of active sulfur greatly hinder their practical application. As the essential part directly related to the battery performance, the S-based cathode has attracted tremendous research interests in recent years. This review highlights recent progress in cathode materials for RT-Na/S batteries. Particularly, basic insights into the Na/S reaction mechanism are presented and representative works on S-based cathode materials are systematically summarized. The remaining challenges and developing trends of RT-Na/S batteries are also discussed. We hope this review can shed light on the field of next-generation metal-sulfur batteries.

Entransy-dissipation-based thermal resistance analysis of heat exchanger networks

Abstract: Heat exchanger network optimization has an important role in high-efficiency energy utilization and energy conservation. The thermal resistance of a heat exchanger network is defined based on its entransy dissipation. In two-stream heat exchanger networks, only heat exchanges between hot and cold fluids are considered. Thermal resistance analysis indicates that the maximum heat transfer rate between two fluids corresponds to the minimum entransy-dissipation-based thermal resistance; i.e. the minimum thermal resistance principle can be exploited in optimizing heat exchanger networks.

Effect of Facet‐Selective Assembly of Cocatalyst on BiVO4 Photoanode for Solar Water Oxidation

Abstract: Rational assembly of cocatalysts on crystal‐based photocatalysts with anisotropic facets, namely the reduction cocatalyst on the reduction facets and oxidation cocatalyst on the oxidation facets, can enhance the photocatalytic activity remarkably. However, this strategy of selective loading of cocatalysts on crystal‐facet engineered photoelectrodes is rarely discussed in photoelectrocatalysis (PEC). Herein, we fabricated BiVO4 microcrystal photoanodes with simultaneous exposure of oxidation {110} and reduction {010} facets to study the effect of facet‐selective assembly of cocatalysts on the activity of PEC water oxidation. By elaborative photodeposition, identical cocatalysts of MnOx were selectively loaded on the reduction, the oxidation and all the facets of BiVO4 photoanodes (respectively designated as R‐BVO, O‐BVO and O/R‐BVO). We found that MnOx facilitates the charge separation in BiVO4 more significantly on the oxidation facets than on the reduction ones, contributing to the net increase in photocurrent of O‐BVO being as 5.2 and 2.2 times that of R‐BVO and O/R‐BVO, respectively. Surface photovoltage analysis reveals that the interaction between the inherent built‐in electric field in BiVO4 crystals and the additive electric filed induced by the MnOx was account for the difference. Our work emphasizes the effect of facet‐selective assembly of cocatalyst in PEC systems, which may guide the rational design of highly efficient photoelectrodes for solar energy conversion.

A Reaction‐Based Colorimetric Fluoride Probe: Rapid “Naked‐Eye” Detection and Large Absorption Shift

Abstract: Taking advantage of both the well-known azobenzene structure and the special fluoride-promoted cleavage reaction of the Si?O bond, a new ratiometric colorimetric probe (F-1) toward the fluoride anion was designed and synthesized by using intramolecular charge transfer (ICT) as a signal mechanism. Upon the addition of F- ions, the probe displayed apparent color changes from colorless to deep blue, with a dramatic shift of the maximum absorption wavelength (similar to 230 nm). With the aid of UV/Vis measurements, the detection limit could be as low as 15 mu M. The probe possessed much higher selectivity for fluoride over other common anions. Excitingly, by virtue of a dipstick approach, F-1 could serve as colorimetric probe for convenient measurements, without the requirement of any additional equipment.

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.