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 pyrolyzed polyacrylonitrile/selenium disulfide composite cathode with remarkable lithium and sodium storage performances.

Abstract: As a special class of cathode materials for lithium-sulfur batteries, pyrolyzed polyacrylonitrile/sulfur (pPAN/S) can completely solve the polysulfide dissolution problem and deliver reliable performance. However, the applicable S contents of pPAN/S are usually lower than 50 weight % (wt %), and their capacity utilizations are not sufficient, both of which greatly limit their energy densities for commercial applications. We report a pyrolyzed polyacrylonitrile/selenium disulfide (pPAN/SeS 2 ) composite with dramatically enhanced active material content (63 wt %) and superior performances for both lithium and sodium storage. As a result, pPAN/SeS 2 delivers high capacity of >1100 mAh g −1 at 0.2 A g −1 for Li storage with extremely stable cycle life over 2000 cycles at 4.0 A g −1 . Moreover, when applied in a room temperature Na-SeS 2 battery, pPAN/SeS 2 achieves superior capacity of >900 mAh g −1 at 0.1 A g −1 and delivers prolonged cycle life over 400 cycles at 1.0 A g −1 .

A modular strategy for decorating isolated cobalt atoms into multichannel carbon matrix for electrocatalytic oxygen reduction

Abstract: Single-atom catalysts (SACs) with their unique electronic and geometric structures usually exhibit extraordinary catalytic performance for many important chemical reactions. Herein, a modular strategy is used to decorate isolated cobalt sites into a multichannel carbon matrix (Co@MCM) with Co content of about 1.4 wt% for efficient electrochemical reduction of oxygen. As confirmed by X-ray absorption fine structure investigation, the pre-designed CoN4 configuration and geometric structure are well maintained in the newly developed Co@MCM. The decorated CoN4 units together with the multichannel carbon substrate with high conductivity and porosity endow the catalyst with excellent activity for the oxygen reduction reaction (ORR). Our findings not only present some fundamental insights for the accurate modulation of nanostructured catalysts at the atomic scale, but also reveal the structural origin of the enhanced catalytic activity.

Metallic 1T MoS2 nanosheet arrays vertically grown on activated carbon fiber cloth for enhanced Li-ion storage performance

Abstract: We have developed a controllable solvothermal method to grow intrinsically conductive MoS2 nanosheet arrays in a metastable 1T phase on carbon fiber cloth (CFC) as binder-free, high-activity Li-ion battery (LIB) anodes By introducing surface hydroxyl groups on the CFC and tuning the DMF content in the mixed solvent, MoS2 nanosheet arrays were perpendicularly grown to the surface of the carbon fibers with a high coverage Electrochemical measurements reveal that the 1T phase nanosheet arrays have excellent Li-ion storage performances, including high specific capacity, high rate capability and good cycling stability, outperforming 2H phase arrays Because of the metallic 1T phase and the highly oriented array architecture, after subtracting the total capacity of CFC, the 1T arrays also deliver a high reversible specific capacity of 1789 mA h g−1 at 01 A g−1 and a retained capacity of 853 mA h g−1 after 140 cycles at 1 A g−1

Achieving Efficient Incorporation of π-Electrons into Graphitic Carbon Nitride for Markedly Improved Hydrogen Generation

Abstract: A rapid and highly efficient strategy for introducing C into g-C3 N4 involves copolymerizing π-electron-rich barbituric acid with melamine via a facile microwave-assisted heating, thereby eliminating the issues in conventional electric furnace heating, such as the severe volatilization, owing to the mismatch of the sublimation temperatures of barbituric acid and melamine. The g-C3 N4 catalyst after optimizing the C-doping content actively generates increased amounts of H2 under visible light exposure with the highest H2 generation rate of 25.0 μmol h-1 , which is nearly 20 times above that using g-C3 N4 produced by conventional electric furnace heating of two identical monomers (1.3 μmol h-1 ). As such, the microwave-assisted heating strategy may stand out as an extremely simple route to incorporating π-electrons into g-C3 N4 with markedly improved photocatalytic performance.

Blue AIEgens: approaches to control the intramolecular conjugation and the optimized performance of OLED devices

Abstract: As one kind of important emitting material, efficient blue-emitting luminogens are badly needed for perfect commercialization of OLEDs. Limited by their intrinsic wide bandgap and their low efficiency in the solid state, excellent blue luminogens are still scarce. Excitingly, the characteristic of aggregation-induced emission might offer a new opportunity to develop good blue luminogens. This review summarizes the recent progress in the field of blue AIEgens, mainly focusing on design strategies for controlling the intramolecular conjugation effect and to realize blue emission, and also gives some outlooks on the further exploration of this field at the end of this paper.

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.