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.

Direct growth of pod-like Cu2O nanowire arrays on copper foam: Highly sensitive and efficient nonenzymatic glucose and H2O2 biosensor

Abstract: The pod-like Cu 2 O nanowire arrays (denoted as Cu 2 O PLNWs/Cu foam) grown on the three-dimensional copper foam is synthesized by a low-cost and convenient method. The morphology and composition of the Cu 2 O PLNWs/Cu foam are characterized by scanning electron microscopy, transmission electron microscopy and X-ray diffraction, respectively. The high conductivity of Cu foam as current collector can facilitate the charge and mass transfer, and the Cu foam with opened framework provides large amounts of anchoring sites for the deposition of Cu 2 O NWs during the synthesis of the Cu 2 O PLNWs/Cu foam. Accordingly, The Cu 2 O PLNWs/Cu foam is used as electrocatalysts for the detection of glucose and H 2 O 2 . The Cu 2 O PLNWs/Cu foam electrode shows extremely high sensitivity of 6.6807 mA mM −1 cm −2 and a low detection limit of 0.67 μM for the electrocatalytic oxidation for glucose. The nonenzymatic sensor also demonstrates good response toward hydrogen peroxide with high sensitivity of 1.4773 mA mM −1 cm −2 and the detection limit of 1.05 μM. Due to the excellently high sensitivity, stability and anti-interference ability, the Cu 2 O PLNWs/Cu foam will be the potential candidate for constructing practical non-enzymatic glucose and hydrogen peroxide sensors.

Uniformly Sized (112) Facet Co2P on Graphene for Highly Effective Photocatalytic Hydrogen Evolution

Abstract: We show that photocatalytic hydrogen evolution reaction (HER) rate is highly dependent on Co surface state, indicated by binding energy data. The key process of hydrogen generation, Co2P–H species formation, follows lower hydrogen adsorption free energy (ΔGH) route. Such low surface energy species can dramatically decrease the overpotential for HER (about 35 mV for HER in basic electrolyte at pH 11, and 150 and 196 mV overpotentials at current density 5 and 15 mA/cm2, respectively). This could explain why Co2P loaded on reduced graphene oxide (RGO) reached high hydrogen generation rate, 1068 μmol·h–1, much higher than that of Pt/RGO catalyst (822 μmol·h–1) under the same reaction condition, while a high apparent quantum efficiency (AQE) (33.3%) was achieved at 520 nm. Moreover, it opens a design strategy for development of cocatalyst with enhanced efficiencies through change of surface H species formation.

A carbon-oxygen-bridged ladder-type building block for efficient donor and acceptor materials used in organic solar cells

Abstract: A carbon-oxygen-bridged ladder-type donor unit (CO5) was invented and prepared via an intramolecular demethanolization cyclization approach. Its single crystal structure indicates enhanced planarity compared with the carbon-bridged analogue indacenodithiophene (IDT). Owing to the stronger electron-donating capability of CO5 than IDT, CO5-based donor and acceptor materials show narrower bandgaps. A donor-acceptor (D-A) copolymer donor (PCO5TPD) and an A-D-A nonfullerene acceptor (CO5IC) demonstrated higher performance than IDT-based counterparts, PIDTTPD and IDTIC, respectively. The better performance of CO5-based materials results from their stronger light-harvesting capability and higher charge-carrier mobilities.

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.