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.

Functional perovskite hybrid of polyacetylene ammonium and lead bromide: Synthesis, light emission, and fluorescence imagining.

Abstract: A highly photoresponsive perovskite hybrid containing an electroactive organic component (H1) was fabricated. A disubstituted polyacetylene (PA) with a hidden amino functionality (P3) was synthesized, hydrolysis and quaternization of which afforded the desired PA ammonium salt (P5). Mixing P5 with lead bromide readily yielded H1, which was stable, soluble, and film-forming. The inorganic framework induced the polymer chains to align in an ordered fashion, which helped to populate the chain segments with long conjugation lengths. The hybrid emitted a blue light (457 nm) in a high quantum yield (62%), thanks to the enhanced electronic conjugation, the weakened interaction between the layer-segregated chains, and the efficient energy transfer from the inorganic sheets to the organic layers. P3 exhibited a half-discharge time as short as ∼0.7 s, representing the first example of an efficient photoconductive disubstituted PA. While stable to normal light illumination, H1 was rapidly bleached upon exposure to h...

Spatial engineering of a Co(OH)x encapsulated p-Cu2S/n-BiVO4 photoanode: simultaneously promoting charge separation and surface reaction kinetics in solar water splitting

Abstract: The photoelectrochemical (PEC) water splitting efficiency of a photoanode is restricted by charge recombination and sluggish reaction kinetics. Here, we demonstrated the spatial engineering of an ultrathin Co(OH)x encapsulated p-Cu2S/n-BiVO4 photoanode for simultaneously enhancing charge separation and surface reaction kinetics in solar water splitting. Specifically, the separation efficiency of photoexcited charge carriers in the bulk was effectively improved due to the formation of a p-Cu2S/n-BiVO4 heterojunction, and the light-driven water oxidation reaction on the surface was further promoted because of the introduction of Co(OH)x as an oxygen evolution catalyst (OEC) layer. As a result, the p-Cu2S/n-BiVO4 heterostructure yielded a largely enhanced charge separation efficiency of up to 79%, and a significant surface charge separation of 70% was achieved, attributed to the deposition of the Co(OH)x cocatalyst. Furthermore, this synergistic effect in the photoanode gave rise to a remarkably enhanced photocurrent density of 3.51 mA cm−2 at 1.23 V vs. the reversible hydrogen electrode. This spatial engineering provides an efficient strategy for the simultaneous improvement of internal and surface charge separation via dual modification, i.e., p–n heterojunction formation and OEC coating.

Integrating a redox flow battery into a Z-scheme water splitting system for enhancing the solar energy conversion efficiency

Abstract: Photocatalytic Z-scheme water splitting is regarded as a promising approach for efficient conversion of solar energy into hydrogen. However, there is a considerable energy loss during the electron transfer process between two photosystems. How to cut down the energy loss becomes a critical issue for improving the solar energy conversion efficiency. Herein, we analyze and evaluate the maximal room for energy storage in photocatalytic water splitting systems and propose a strategy of integrating a redox flow battery (RFB) into a Z-scheme water splitting system to reduce the energy loss. Moreover, we construct a biohybrid photosystem II (PSII)–ZrO2/TaON Z-scheme system with an integrated quinone/ferricyanide RFB. The platform system can generate both electricity and hydrogen utilizing solar energy through photocatalytic charge and discharge processes of the RFB and the water splitting reaction, resulting in the enhancement of the solar energy conversion efficiency. This proof-of-concept work opens a new avenue to save the energy dissipated in Z-scheme water splitting for more efficient solar energy conversion.

Intravital Multiphoton Imaging of the Selective Uptake of Water‐Dispersible Quantum Dots into Sinusoidal Liver Cells

Abstract: Although many studies reporting the organ-level biodistribution of nanoparticles (NPs) in animals, very few have addressed the fate of NPs in organs at the cellular level. The liver appears to be the main organ for accumulation of NPs after intravenous injection. In this study, for the first time, the in vivo spatiotemporal disposition of recently developed mercaptosuccinic acid (MSA)-capped cadmium telluride/cadmium sulfide (CdTe/CdS) quantum dots (QDs) is explored in rat liver using multiphoton microscopy (MPM) coupled with fluorescence lifetime imaging (FLIM), with subcellular resolution (∼1 μm). With high fluorescence efficiency and largely improved stability in the biological environment, these QDs show a distinct distribution pattern in the liver compared to organic dyes, rhodamine 123 and fluorescein. After intravenous injection, fluorescent molecules are taken up by hepatocytes and excreted into the bile, while negatively charged QDs are retained in the sinusoids and selectively taken up by sinusoidal cells (Kupffer cells and liver sinusoidal endothelial cells), but not by hepatocytes within 3 h. The results could help design NPs targeting the specific types of liver cells and choose the fluorescent markers for appropriate cellular imaging.

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.