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.

Manipulating solar absorption and electron transport properties of rutile TiO2 photocatalysts via highly n-type F-doping

Abstract: In this work, we report a facile and nontoxic one-pot hydrothermal method for synthesizing F-doped rutile single crystalline TiO2 with tuneable solar absorption. The optical band gap of the catalyst can be easily manipulated from 3.05 to 2.58 eV via altering the initial F : Ti molar ratio of reaction precursors. The photoanodes made of rutile TiO2 single crystals with appropriate F-doping concentration show excellent photoelectrocatalytic activity towards water oxidation under ultraviolet and visible light illumination. The best photoelectrocatalytic performance under UV irradiation can be obtained by F-doped TiO2 with an initial F : Ti molar ratio of 0.1, which is almost 15 times higher than that of un-doped TiO2. Further, the F-doped TiO2 photoanodes also exhibit superior photoelectrocatalytic activity under visible irradiation, and the best performance can be achieved by F-doped TiO2 photoanode with an initial F : Ti molar ratio of 0.05. The superior photoelectrocatalytic activity could be attributed to the highly n-type dopant introduced by fluorine, which significantly tunes the electrical conductivities and band structures of the resulting TiO2 photoanodes, and thus the photoelectrocatalytic activities under both UV and visible irradiation. Different techniques have been employed to characterize the electrical conductivity, charge carrier density and band structures of the F-doped rutile TiO2 films, such as photoelectrochemical method, electrical impedance spectroscopy (EIS) measurements, Mott–Schottky plots and XPS valence band spectra.

Highly sensitive fluorometric determination of oxytetracycline based on carbon dots and Fe3O4 MNPs

Abstract: In this paper, a novel, selective and eco-friendly biosensor has been fabricated and applied for sensitive detection of antibiotic oxytetracycline (OTC). This fluorescent biosensor was based on carbon quantum dots (CDs), H 2 O 2 and Fe 3 O 4 magnetic nanoparticles (Fe 3 O 4 MNPs). Herein, we firstly demonstrated that the fluorescence of CDs decreased apparently in the presence of OTC, H 2 O 2 and Fe 3 O 4 MNPs. The fluorescence quenching of CDs not only is caused by the hydroxyl radical produced from the Fenton reaction and complexation of Fe 3 O 4 MNPs with OTC, but also might be related to the inner filter effect (IFE) between CDs and OTC, which improved the selectivity and sensitivity of the fabricated biosensor. Under the optimal experiment conditions, the relative fluorescence intensity of the CDs decreased linearly with the increasing concentration of OTC from 25 nM to 1.75 μM. The detection limit of the fabricated biosensor on OTC was 9.5 nM at a signal-to-noise ratio of 3. Compared with other current protocols, the method described here displayed a few advantages including simplicity, low cost and rapid detection. The precision and reproducibility of the proposed sensor were also acceptable Significantly, the practicality of this sensitive sensor for OTC detection in drugs was further validated, revealing the advantages of high simplicity, sensitivity and selectivity. Hence, the developed biosensor might provide a useful and practical tool for OTC determination and related food safety analysis and clinical diagnosis.

Enhancing photoactivity for hydrogen generation by electron tunneling via flip-flop hopping over iodinated graphitic carbon nitride

Abstract: In this work, the significant electron tunneling over iodine-decorated graphitic carbon nitride (I-g-C3N4) was achieved by I3− and I5− clusters implanted. The flip-flop electron tunneling takes place via strong Rashba spin-orbit coupling in p orbitals of polyiodides. The electron tunneling and hopping bridged the easier transfer route between far-located carbon atoms of g-C3N4 through the polyiodides p orbitals. By taking the advantage of this tunneling, the conductivity of I-g-C3N4/Ag photocatalyst was remarkably increased and the lifetime of photogenerated charges was largely prolonged, evidenced by I–V characteristics and the photoluminescence (PL) spectra. With the help of these properties, the obtained I-g-C3N4/Ag photocatalyst presented high active for hydrogen generation under visible light irradiation and Ag NPs as active site for hydrogen formation. 104.3 μmol H2 was evolved over I-g-C3N4/Ag photocatalyst in 3 h, about three time higher than that of un-iodinated g-C3N4/Ag, and no remarkable decay of activity was observed in 900 min reaction. The highest AQE value of 7.3% was achieved at 520 nm.

Biodegradable Nanoagents with Short Biological Half-Life for SPECT/PAI/MRI Multimodality Imaging and PTT Therapy of Tumors

Abstract: Rapid clearance of nanoagents is a critical criterion for their clinical translation. Herein, it is reported that biodegradable and renal clearable nanoparticles are potentially useful for image-guided photothermal therapy of tumors. The multifunctional nanoparticles with excellent colloidal stability are synthesized through coordination reactions between Fe3+ ions and gallic acid (GA)/polyvinyl pyrrolidone (PVP) in aqueous solution. Detailed characterization reveals that the resulting Fe3+ /GA/PVP complex nanoparticles (FGPNs) integrate strong near-infrared absorption with paramagnetism well. As a result, the FGPNs present outstanding performance for photoacoustic imaging and magnetic resonance imaging of tumors, and outstanding photothermal ablation effect for tumor therapy owing to their high photothermal conversion efficiency. More importantly, the pharmacokinetic behaviors of the FGPNs determined through 125 I labeling suggest that the FGPNs are readily degraded in vivo showing a short biological half-life, and the decomposition products are excreted through either renal clearance pathway or bowel elimination pathway via stomach, which highlights the characteristics of the current multifunctional theranostic agent and their potential in clinical translation.

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.