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.

Molecular conformation and packing: their critical roles in the emission performance of mechanochromic fluorescence materials

Abstract: Mechanochromic fluorescence (MCF) materials are a sort of smart material whose photophysical properties are sensitive to mechanical stimulation, such as photoluminescence color, fluorescence quantum yield and emission lifetime. Recently, an increasing number of studies have shown that these photophysical properties can be affected greatly by the molecular packing and conformation, enabling the rapid development of functional materials with mechanochromic fluorescence properties. In this review, we focus on MCF materials with distinct emission properties and various molecular arrangements, especially the inherent correlation between molecular packing modes and emissive behaviors. Many of the selected representative examples possess polymorphism, offering the possibility of exploring different emissions from the exact molecular packing in single crystals. Correspondingly, some remarks are made on the outlook for the next developments in MCF materials and the required thinking about the structure–packing–performance relationship.

Cutting sp2clusters in graphene sheets into colloidal graphene quantum dots with strong green fluorescence

Abstract: Water-soluble and well-crystallized graphene quantum dots with lateral size about 3.0 nm were fabricated by a hydrothermal cutting method and their photoluminescence (PL) properties as well as the potential for bioimaging were demonstrated.

Facile fabrication of large-grain CH3NH3PbI3-xBrx films for high-efficiency solar cells via CH3NH3Br-selective Ostwald ripening.

Abstract: Organometallic halide perovskite solar cells (PSCs) have shown great promise as a low-cost, high-efficiency photovoltaic technology. Structural and electro-optical properties of the perovskite absorber layer are most critical to device operation characteristics. Here we present a facile fabrication of high-efficiency PSCs based on compact, large-grain, pinhole-free CH3NH3PbI3-xBrx (MAPbI3-xBrx) thin films with high reproducibility. A simple methylammonium bromide (MABr) treatment via spin-coating with a proper MABr concentration converts MAPbI3 thin films with different initial film qualities (for example, grain size and pinholes) to high-quality MAPbI3-xBrx thin films following an Ostwald ripening process, which is strongly affected by MABr concentration and is ineffective when replacing MABr with methylammonium iodide. A higher MABr concentration enhances I-Br anion exchange reaction, yielding poorer device performance. This MABr-selective Ostwald ripening process improves cell efficiency but also enhances device stability and thus represents a simple, promising strategy for further improving PSC performance with higher reproducibility and reliability.

Laminated Carbon Nanotube Networks for Metal Electrode-Free Efficient Perovskite Solar Cells

Abstract: Organic–inorganic metal halide perovskite solar cells were fabricated by laminating films of a carbon nanotube (CNT) network onto a CH3NH3PbI3 substrate as a hole collector, bypassing the energy-consuming vacuum process of metal deposition. In the absence of an organic hole-transporting material and metal contact, CH3NH3PbI3 and CNTs formed a solar cell with an efficiency of up to 6.87%. The CH3NH3PbI3/CNTs solar cells were semitransparent and showed photovoltaic output with dual side illuminations due to the transparency of the CNT electrode. Adding spiro-OMeTAD to the CNT network forms a composite electrode that improved the efficiency to 9.90% due to the enhanced hole extraction and reduced recombination in solar cells. The interfacial charge transfer and transport in solar cells were investigated through photoluminescence and impedance measurements. The flexible and transparent CNT network film shows great potential for realizing flexible and semitransparent perovskite solar cells.

The Strong Light-Emission Materials in the Aggregated State: What Happens from a Single Molecule to the Collective Group.

Abstract: The strong light emission of organic luminogens in the aggregated state is essential to their applications as optoelectronic materials with good performance. In this review, with respect to the aggregation-induced emission and room-temperature phosphorescence luminogens, the important role of molecular packing modes is highlighted. As demonstrated in the selected examples, the molecular packing status in the aggregate state is affected by many factors, including the molecular configurations, the inherent electronic properties, the special functional groups, and so on. With the consideration of all these parameters, the strong fluorescence and phosphorescence in the aggregated state could be achieved in the rationally designed organic luminogens, providing some guidance for the further development.

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.