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.

Graphene buffered galvanic synthesis of graphene–metal hybrids

Abstract: We report an in situ synthesis of graphene–metal hybrids using graphene as the buffer layer by a substrate-induced galvanic reaction. Ag nanoplates are obtained with the template effect of graphene, and their morphologies are tailored by light mediation. Our result suggests that defect sites or open edges of graphene favor binding with Ag atoms. The graphene–Ag hybrids have been used as Raman enhanced substrates for dye detection. The facile method for synthesis of graphene–metal hybrids opens up opportunities for the future development of optical, electronic and catalytic materials based on graphene and metals.

Label-free detection of acetylcholinesterase and its inhibitor based on the in situ formation of fluorescent copper nanoparticles

Abstract: The poly(thymine) (poly T) can effectively template the in situ formation of copper nanoparticles (CuNPs) within several minutes under ambient conditions, offering great potential as fluorescence probe for biochemical analysis without complicated modifications. However, the exploration of poly T-templated CuNPs (poly T-CuNPs) for biochemical applications is still at its very early stage. Herein, a novel fluorescent assay has been developed for acetylcholinesterase (AChE) and its inhibitor detection based on poly T-CuNPs. In the absence of AChE, the high affinity between Cu 2+ and thymine leads to the formation of fluorescent CuNPs. In the presence of AChE, the fluorescence of poly T-CuNPs is quenched based on the reaction between Cu 2+ and thiocholine generating from the hydrolysis of ATCh by AChE. This detection assay is simple without the requirement for complex labeling of probe DNA and the multiple preparation procedure of fluorescent compounds. The detection assay is highly sensitive for sensing AChE in the concentration ranging from 0.11 to 2.78 mU mL −1 with a detection limit of 0.05 mU mL −1 and is feasible for screening AChE inhibitor. This method paves a new way for exploring the biosensing applications of the poly T-CuNPs.

Different Effect of the Additional Electron-Withdrawing Cyano Group in Different Conjugation Bridge: The Adjusted Molecular Energy Levels and Largely Improved Photovoltaic Performance.

Abstract: Four organic sensitizers (LI-68–LI-71) bearing various conjugated bridges were designed and synthesized, in which the only difference between LI-68 and LI-69 (or LI-70 and LI-71) was the absence/presence of the CN group as the auxiliary electron acceptor. Interestingly, compared to the reference dye of LI-68, LI-69 bearing the additional CN group exhibited the bad performance with the decreased Jsc and Voc values. However, once one thiophene moiety near the anchor group was replaced by pyrrole with the electron-rich property, the resultant LI-71 exhibited a photoelectric conversion efficiency increase by about 3 folds from 2.75% (LI-69) to 7.95% (LI-71), displaying the synergistic effect of the two moieties (CN and pyrrole). Computational analysis disclosed that pyrrole as the auxiliary electron donor (D′) in the conjugated bridge can compensate for the lower negative charge in the electron acceptor, which was caused by the CN group as the electron trap, leading to the more efficient electron injection an...

Similar or different: the same Spiro-core but different alkyl chains with apparently improved device performance of perovskite solar cells

Abstract: By intelligently utilizing the odd-even effect existing in the melting points of alkanes as presented in the basic textbook of Organic Chemistry, different alkoxy groups were introduced to modify the structure of commercial Spiro-OMeTAD to give new Spiro derivatives of Spiro-OEtTAD, Spiro-OPrTAD, Spiro-OiPrTAD and Spiro-OBuTAD, with the aim to adjust the molecular packing status in perovskite solar cells as hole transporting compounds. Excitedly, with the introduction of ethoxy groups instead of the methoxy ones in Spiro-OMeTAD, Spiro-OEtTAD-based perovskite solar cells demonstrated the highest device performance of 20.16%, higher than that of Spiro-OMeTAD (18.64%).

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.