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.

Construction of efficient blue AIE emitters with triphenylamine and TPE moieties for non-doped OLEDs

Abstract: In this paper, by merging the hole-dominated triphenylamine (TPA) and tetraphenylethene (TPE) moieties together with different linkage positions, four derivatives of 1,2-bis[4′-(diphenylamino)biphenyl-4-yl]-1,2-diphenylethene (2TPATPE) were successfully synthesized with confirmed structures, and their thermal, optical and electronic properties were fully investigated. Thanks to the introduction of the meta-linkage mode on the TPE core, their π-conjugation length could be effectively restricted to ensure blue emission. The non-doped OLEDs based on these four emitters exhibit blue emissions from 443–466 nm, largely blue-shifted with respect to the green emission of 2TPATPE (514 nm). Meanwhile, good electroluminescence efficiencies with Lmax, ηC,max, and ηP,max of up to 8160 cd m−2, 3.79 cd A−1, and 2.94 Im W−1 respectively, have also been obtained, further validating our rational design of blue AIE fluorophores.

Untethered Recyclable Tubular Actuators with Versatile Locomotion for Soft Continuum Robots.

Abstract: Stimuli-responsive materials offer a distinguished platform to build tether-free compact soft robots, which can combine sensing and actuation without a linked power supply. In the past, tubular soft robots have to be made by multiple components with various internal channels or complex cavities assembled together. Moreover, robust processing, complex locomotion, simple structure, and easy recyclability represent major challenges in this area. Here, it is shown that those challenges can be tackled by liquid crystalline elastomers with allyl sulfide functional groups. The light-controlled exchange reaction between allyl sulfide groups allows flexible processing of tubular soft robots/actuators, which does not need any assisting materials. Complex locomotion demonstrated here includes reversible simultaneous bending and elongation; reversible diameter expansion; and omnidirectional bending via remote infrared light control. Different modes of actuation can be programmed into the same tube without the routine assembly of multiple tubes as used in the past. In addition, the exchange reaction also makes it possible to use the same single tube repeatedly to perform different functions by erasing and reprogramming.

Insights into operational stability and processing of halide perovskite active layers

Abstract: Rapid improvement of the stability of metal halide perovskite materials is required to enable their adoption for energy production at terawatt scale. To understand the role of the active layer stability in these devices we use in situ X-ray diffraction to observe the evolution in structural stability across mixed A-site APbI3 materials where the A-site is a combination of formamidinium, Cs, and/or methylammonium. During device operation we observe spatial de-mixing and phase segregation into more pure constituent phases. Using complementary first-principles calculations of mixed A-site halide perovskites, a hypothesized framework explaining the experimentally observed mixing and de-mixing in these systems is presented and then validated using in situ X-ray diffraction and spatially resolved time of flight secondary ion mass spectrometry. Taken together, these results indicate that stability is not only a function of device architecture or chemical formulation, but that the processing strategy is critically important in synthesizing the most energetically favorable state and therefore the most stable device systems. This study reconciles disparate reports within the literature and also highlights the limitations of shelf life studies to ascertain stability as well as the importance of testing devices under operational conditions.

A flame-retardant polymer electrolyte for high performance lithium metal batteries with an expanded operation temperature

Abstract: Polymer electrolytes with high ionic conductivity, good interfacial stability and safety are in urgent demand for practical rechargeable lithium metal batteries (LMBs). Herein we propose a novel flame-retardant polymerized 1,3-dioxolane electrolyte (PDE), which is in situ formed via a multifunctional tris(pentafluorophenyl)borane (TB) additive. The in situ formed PDE not only affords an integrated battery structure with stabilized electrode–electrolyte interface, but also achieves good flame retardancy, significantly expanded operating temperature limit and improved oxidative stability. Moreover, TB also contributes to a highly stable LiF-rich solid electrolyte interphase (SEI). In addition, the PDE has good compatibility with electrodes and polypropylene (PP) separator, hardly increasing the thickness of the battery, and the amount of additive TB is small, so there is no loss of gravimetric or volumetric energy density due to the polymerization. Based on the in situ formed PDE, Li–S batteries without the addition of LiNO3 demonstrate excellent cycle stability (>500 cycles) and a wide operating temperature (−20 to 50 °C); the high voltage Li–LiNi0.6Co0.2Mn0.2O2 and Li–LiFePO4 batteries both exhibit excellent electrochemical performance (>1200 cycles). In addition, the ultrasonic imaging technique developed by our group also demonstrates no gas generation inside pouch cells using PDE. This work provides a facile and practical approach to design a highly stable polymer electrolyte for high performance LMBs.

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.