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.

Third-order nonlinear optical properties of methylammonium lead halide perovskite films

Abstract: We report third-order nonlinear coefficient values and decay time kinetics vs. halide composition (CH3NH3PbBr3 and CH3NH3PbBr2I), temperature, and excitation wavelength. The maximum values of the third-order nonlinear susceptibility χ(3) (∼1.6 × 10−6 esu) are similar to or larger than many common third-order materials. The source of the nonlinearity is shown to be primarily excitonic in the tribromide film by virtue of its strong enhancement near the exciton resonance. Nonresonant excitation reduces the nonlinearity significantly, as does increasing the temperature. Substitution of one I for one Br also reduces the nonlinearity by at least one order of magnitude, presumably due to the lack of strong exciton resonance in the substituted form. The thin films are stable, highly homogenous (lacking significant light scattering), and simple and inexpensive to fabricate, making them potentially useful in a variety of optoelectronic applications in which wavelength selectivity is important.

A relay strategy for the mercury (II) chemodosimeter with ultra-sensitivity as test strips

Abstract: A relay strategy has been proposed to design a new Hg2+ chemodosimeter (TPE-S), by coupling Hg2+-promoted deprotection reaction with ketone-enol isomerization, realizing the multistage amplifying effect. Changes in both of color and fluorescence could occur immediately, and TPE-S displayed high selectivity for Hg2+, other metal ions (Ag+, Fe3+, Cu2+, Pb2+, Co2+, Cr3+, Al3+, Cd2+, Mg2+, Mn2+, Ba2+, Fe2+, Ca2+, Ni2+, Zn2+, Li+, K+ and Na+) gave nearly no disturbance to the sensing process. When fabricated as test strips similar to pH-indicator papers, immediate color change from colorless to purple could be visually observed by naked-eyes without the aid of any additional equipment, with the detection limit as low as 1 x 10(-7) M (Hg2+ in aqueous solution). Due to its easy synthesis, high selectivity and sensitivity, combined with the portable test strips, TPE-S could be developed as a convenient and cost-effective tool for the detection of Hg2+ in on-site inspections.

How the linkage positions affect the performance of bulk-heterojunction polymer solar cells

Abstract: Two new alternating copolymers. PCT-10,13-BPz (P1) and PCT-2,7-BPz (P2), which were constructed from the same monomers and only different in the connecting points, were synthesized via Suzuki coupling reaction. The UV-vis absorptions, thermal stability, energy levels, field-effect carrier mobility and photovoltaic characteristics of the two copolymers were systematically evaluated to understand the relationships between the polymer structure at the molecular level and the photovoltaic performances. Photovoltaic cells based on the two copolymers with a structure of ITO/PEDOT : PSS/Polymer : PC71BM/PFN/Al exhibited PCEs of 4.31% and 0.64%, respectively, under one sun of AM 1.5 solar simulator illumination (100 mW cm−2).

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.