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.

Blue pyrene-based AIEgens: inhibited intermolecular π–π stacking through the introduction of substituents with controllable intramolecular conjugation, and high external quantum efficiencies up to 3.46% in non-doped OLEDs

Abstract: Six blue AIE luminogens, Py-2pTPE, Py-2mTPE, Py-2TP, Py-2TF, Py-2NTF and Py-2F, have been successfully synthesized, which exhibit sky blue (484 nm) to deep blue (444 nm) emissions in accordance with the different introduced aromatic substituents on the pyrene core and the different linkage modes. Furthermore, Py-2pTPE and Py-2mTPE show interesting mechanochromism effects, inherited from TPE-pBr and TPE-mBr, respectively. When fabricated as emitters in OLEDs, all of the six AIEgens demonstrate blue emissions and good EL performances, among which, Py-2TP shows the best device performance with an ηEQE,max up to 3.46% at a CIE coordinate of (0.15, 0.09).

Facile Synthesis of Highly Efficient One-Dimensional Plasmonic Photocatalysts through Ag@Cu2O Core–Shell Heteronanowires

Abstract: A novel class of one-dimensional (1D) plasmonic Ag@Cu2O core–shell heteronanowires have been synthesized at room temperature for photocatalysis application. The morphology, size, crystal structure and composition of the products were investigated by XRD, SEM, TEM, XPS, and UV–vis instruments. It was found the reaction time and the amount of Ag nanowires play crucial roles in the formation of well-defined 1D Ag@Cu2O core–shell heteronanowires. The resultant 1D Ag@Cu2O NWs exhibit much higher photocatalytic activity toward degradation of organic contaminants than Ag@Cu2O core–shell nanoparticles or pure Cu2O nanospheres under solar light irradiation. The drastic enhancement in photocatalytic activity could be attributed to the surface plasmon resonance and the electron sink effect of the Ag NW cores, and the unique 1D core–shell nanostructure.

Graphene Nano-“patches” on a Carbon Nanotube Network for Highly Transparent/Conductive Thin Film Applications

Abstract: Freestanding carbon-based composite thin films, consisting of a carbon nanotube network patched with graphene sheets, were assembled by a solid-phase layer-stacking approach with ethanol wetting. The composite films are highly flexible, transparent, and conductive, showing a sheet resistance of 735 Ω/sq at 90% transmittance (at 550 nm). Under AM1.5 illumination, heterojunction solar cells made from the composite films and n-type silicon show a power conversion efficiency of up to 5.2%.

Learning semantic relationships for better action retrieval in images

Abstract: Human actions capture a wide variety of interactions between people and objects. As a result, the set of possible actions is extremely large and it is difficult to obtain sufficient training examples for all actions. However, we could compensate for this sparsity in supervision by leveraging the rich semantic relationship between different actions. A single action is often composed of other smaller actions and is exclusive of certain others. We need a method which can reason about such relationships and extrapolate unobserved actions from known actions. Hence, we propose a novel neural network framework which jointly extracts the relationship between actions and uses them for training better action retrieval models. Our model incorporates linguistic, visual and logical consistency based cues to effectively identify these relationships. We train and test our model on a largescale image dataset of human actions. We show a significant improvement in mean AP compared to different baseline methods including the HEX-graph approach from Deng et al. [8].

Unexpected room-temperature phosphorescence from a non-aromatic, low molecular weight, pure organic molecule through the intermolecular hydrogen bond

Abstract: Efficient pure organic room temperature phosphorescence (RTP) materials have drawn considerable attention So far, most pure organic RTP molecules are aromatic compounds, and nonconjugated molecules are really scarce The only few reported non-aromatic organic phosphorescence materials are polymers without confirmed subtle structures, and there are no reports concerning non-aromatic organic small molecules with persistent RTP Here, we report an example of a pure non-aromatic organic small molecule, cyanoacetic acid, that shows unexpected persistent RTP behavior with RTP lifetime as long as 862 ms According to the CAA crystal and theoretical calculations, the presence of strong intermolecular hydrogen bonds is the key factor for its persistent RTP effect This discovery demonstrates a clear relationship between the molecular structure, packing mode and RTP effect in the non-aromatic system, which will largely extend the current pure organic RTP systems to deeply investigate the origin of light emission

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.