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.

Mesoporous Carbon@Titanium Nitride Hollow Spheres as an Efficient SeS2 Host for Advanced Li-SeS2 Batteries.

Abstract: The introduction of a certain proportion of selenium into sulfur-based cathodes is an effective strategy for enhancing the integrated battery performance. However, similar to sulfur, selenium sulfide cathodes suffer from poor cycling stability owing to the dissolution of reaction intermediate products. In this study, to exploit the advantages of SeS2 to the full and avoid its shortcomings, we designed and synthesized a hollow mesoporous carbon@titanium nitride (HMC@TiN) host for loading 70 wt % of SeS2 as a cathode material for Li–SeS2 batteries. Benefiting from both physical and chemical entrapment by hollow mesoporous carbon and TiN, the HMC@TiN/SeS2 cathode manifests high utilization of the active material and excellent cycling stability. Moreover, it exhibits promising areal capacity (up to 4 mAh cm−2) with stable cell performance in the high-mass-loading electrode.

New Azo-Chromophore-Containing Hyperbranched Polytriazoles Derived from AB2 Monomers via Click Chemistry under Copper(I) Catalysis

Abstract: By modifying the synthetic procedure, the previous reported impossible approach was successfully utilized to construct new azo-chromophore-containing hyperbranched polymers (HP1 and HP2) from AB2 m...

Boosting the Radiosensitizing and Photothermal Performance of Cu2–xSe Nanocrystals for Synergetic Radiophotothermal Therapy of Orthotopic Breast Cancer

Abstract: The small difference between tumor and normal tissues in their responses to ionizing radiation has been a significant issue for radiotherapy of tumors. Herein, we report that dumbbell-shaped heterogeneous copper selenide-gold nanocrystals can serve as an efficient radiosensitizer for enhanced radiotherapy. The mean lethal dose of X-rays to 4T1 tumor cells can be drastically decreased about 40%, that is, decreasing from 1.81 to 1.10 Gy after culture with heterostructures. Due to the synergetic effect of heterostructures, the dose of X-rays is also much lower than those obtained from mixture of Cu2- xSe + Au nanoparticles (1.78 Gy), Cu2- xSe nanoparticles (1.72 Gy) and Au nanoparticles (1.50 Gy), respectively. We demonstrate that the sensitivity enhancement ratio of Cu2- xSe nanoparticles was significantly improved 45% ( i. e., from 1.1 to 1.6) after the formation of heterostructures with gold. We also show that the heteronanocrystals exhibit an enhanced photothermal conversion efficiency, due to the synergetic interactions of localized surface plasmon resonance. These properties highly feature them as a multimodal imaging contrast agent (particularly for photoacoustic imaging, computed tomography imaging, and single photon emission computed tomography after labeled with radioisotopes) and as a radiosensitizer for imaging guided synergetic radiophotothermal treatment of cancer. The research provides insights for engineering low- Z nanomaterials with high- Z elements to form heteronanostructures with enhanced synergetic performance for tumor theranostics.

Recent rapid warming trend revealed from the isotopic record in Muztagata ice core, eastern Pamirs

Abstract: [1] Many have made efforts to clarify the climatic significance of stable isotopic variations in ice cores around central Asia through the study of stable isotopes in present-day precipitation. A new shallow ice core from Muztagata, in the eastern Pamirs, allows for a detailed comparison of annual δ18O variation with local meteorological data as well as with global air temperature variations. On the basis of a comparison of seasonal fluctuations of δ18O in the local precipitation, the 41.6-m ice core drilled at 7010 m provides a record of about one-half century. The annual fluctuations of δ18O in this ice core are in good agreement (correlation coefficient of 0.67) with the annual air temperature changes at the nearby meteorological station Taxkorgen, indicating that the isotopic record from this ice core is a reliable temperature trend indicator. The most important discovery from the δ18O variation of this ice core is a rapid warming trend in the 1990s, which is consistent with a general global warming trend over this time period. This recent rapid warming at higher elevations in this area has led to the quick retreat of alpine glaciers.

Dopant-Free Squaraine-Based Polymeric Hole-Transporting Materials with Comprehensive Passivation Effects for Efficient All-Inorganic Perovskite Solar Cells.

Abstract: Development of high-performance dopant-free hole-transporting materials (HTMs) with comprehensive passivation effects is highly desirable for all-inorganic perovskite solar cells (PVSCs). Squaraines (SQs) could be a candidate for dopant-free HTMs as they are natural passivators for perovskites. One major limitation of SQs is their relatively low hole mobility. Herein we demonstrate that polymerizing SQs into pseudo two dimensional (2D) p-π conjugated polymers could overcome this problem. By rationally using N,N-diarylanilinosquaraines as the comonomers, the resulting polysquaraine HTMs not only exhibit suitable energy levels and efficient passivation effects, but also achieve very high hole mobility close to 0.01 cm-2 V-1 s-1 . Thus as dopant-free HTMs for α-CsPbI2 Br-based all-inorganic PVSCs, the best PCE reached is 15.5 %, outperforming those of the doped-Spiro-OMeTAD (14.4 %) based control devices and among the best for all-inorganic PVSCs.

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.