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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.


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Journal ArticleDOI
TL;DR: This work reports on a novel and simple hydrothermal approach for the cutting of GSs into surface-functionalized GQDs, which were found to exhibit bright blue photoluminescence (PL), which has never been observed in GSs and GNRs owing to their large lateral sizes.
Abstract: 2010 WILEY-VCH Verlag Gm Graphene-based materials are promising building blocks for future nanodevices owing to their superior electronic, thermal, and mechanical properties as well as their chemical stability. However, currently available graphene-based materials produced by typical physical and chemical routes, including micromechanical cleavage, reduction of exfoliated graphene oxide (GO), and solvothermal synthesis, are generally micrometer-sized graphene sheets (GSs), which limits their direct application in nanodevices. In this context, it has become urgent to develop effective routes for cutting large GSs into nanometer-sized pieces with a well-confined shape, such as graphene nanoribbons (GNRs) and graphene quantum dots (GQDs). Theoretical and experimental studies have shown that narrow GNRs (width less than ca. 10 nm) exhibit substantial quantum confinement and edge effects that render GNRs semiconducting. By comparison, GQDs possess strong quantum confinement and edge effects when their sizes are down to 100 nm. If their sizes are reduced to ca. 10 nm, comparable with the widths of semiconducting GNRs, the two effects will become more pronounced and, hence, induce new physical properties. Up to now, nearly all experimental work on GNRs and GQDs has focused on their electron transportation properties. Little work has been done on the optical properties that are directly associated with the quantum confinement and/or edge effects. Most GNRand GQD-based electronic devices have been fabricated by lithography techniques, which can realize widths and diameters down to ca. 20 nm. This physical approach, however, is limited by the need for expensive equipment and especially by difficulties in obtaining smooth edges. Alternative chemical routes can overcome these drawbacks. Moreover, surface functionalization can be realized easily. Li et al. first reported a chemical route to functionalized and ultrasmooth GNRs with widths ranging from 50 nm to sub-10 nm. Very recently, Kosynkin et al. reported a simple solution-based oxidative process for producing GNRs by lengthwise cutting and unraveling of multiwalled carbon nanotube (CNT) side walls. Yet, no chemical routes have been reported so far for preparing functionalized GQDs with sub-10 nm sizes. Here, we report on a novel and simple hydrothermal approach for the cutting of GSs into surface-functionalized GQDs (ca. 9.6-nm average diameter). The functionalized GQDs were found to exhibit bright blue photoluminescence (PL), which has never been observed in GSs and GNRs owing to their large lateral sizes. The blue luminescence and new UV–vis absorption bands are directly induced by the large edge effect shown in the ultrafine GQDs. The starting material was micrometer-sized rippled GSs obtained by thermal reduction of GO sheets. Figure 1a shows a typical transmission electron microscopy (TEM) image of the pristine GSs. Their (002) interlayer spacing is 3.64 A (Fig. 1c), larger than that of bulk graphite (3.34 A). Before the hydrothermal treatment, the GSs were oxidized in concentrated H2SO4 and HNO3. After the oxidization treatment the GSs became slightly smaller (50 nm–2mm) and the (002) spacing slightly increased to 3.85 A (Fig. 1c). During the oxidation, oxygen-containing functional groups, including C1⁄4O/COOH, OH, and C O C, were introduced at the edge and on the basal plane, as shown in the Fourier transform infrared (FTIR) spectrum (Fig. 1d). The presence of these groups makes the GSs soluble in water. A series of more marked changes took place after the hydrothermal treatment of the oxidized GSs at 200 8C. First, the (002) spacing was reduced to 3.43 A (Fig. 1c), very close to that of bulk graphite, indicating that deoxidization occurs during the hydrothermal process. The deoxidization is further confirmed by the changes in the FTIR and C 1s X-ray photoelectron spectroscopy (XPS) spectra. After the hydrothermal treatment, the strongest vibrational absorption band of C1⁄4O/COOH at 1720 cm 1 became very weak and the vibration band of epoxy groups at 1052 cm 1 disappeared (Fig. 1d). In the XPS C 1s spectra of the oxidized and hydrothermally reduced GSs (Fig. 2a), the signal at 289 eV assigned to carboxyl groups became weak after the hydrothermal treatment, whereas the sp carbon peak at 284.4 eV was almost unchanged. Figure 2b shows the Raman spectrum of the reduced GSs. A G band at 1590 cm 1 and a D band at 1325 cm 1 were observed with a large intensity ratio ID/IG of 1.26. Second, the size of the GSs decreased dramatically and ultrafine GQDswere isolated by a dialysis process. Figure 3 shows typical TEM and atomic force microscopy (AFM) images of the GQDs. Their diameters are mainly distributed in the range of 5–13 nm (9.6 nm average diameter). Their topographic heights are mostly between 1 and 2 nm, similar to those observed in functionalized GNRs with 1–3 layers. More than 85% of the GQDs consist of 1–3 layers.

2,484 citations

Journal ArticleDOI
TL;DR: In this article, the effect of alloying FA0.85Cs0.15PbI3 with CsPbIsI3 was investigated, and it was shown that the effective tolerance factor can be tuned and the stability of the photoactive α-phase of the mixed solid-state perovskite alloys FA1-xCsxPbisI3 is enhanced.
Abstract: Goldschmidt tolerance factor (t) is an empirical index for predicting stable crystal structures of perovskite materials. A t value between 0.8 and 1.0 is favorable for cubic perovskite structure, and larger (>1) or smaller (<0.8) values of tolerance factor usually result in nonperovskite structures. CH(NH2)2PbI3 (FAPbI3) can exist in the perovskite α-phase (black phase) with good photovoltaic properties. However, it has a large tolerance factor and is more stable in the hexagonal δH-phase (yellow phase), with δH-to-α phase-transition temperature higher than room temperature. On the other hand, CsPbI3 is stabilized to an orthorhombic structure (δO-phase) at room temperature due to its small tolerance factor. We find that, by alloying FAPbI3 with CsPbI3, the effective tolerance factor can be tuned, and the stability of the photoactive α-phase of the mixed solid-state perovskite alloys FA1–xCsxPbI3 is enhanced, which is in agreement with our first-principles calculations. Thin films of the FA0.85Cs0.15PbI3 p...

1,483 citations

Journal ArticleDOI
TL;DR: Graphene applications are just starting, and current investigations are on a number of areas such as composites, nanoelectronics, and transparent electrodes, where a continuous single-layer graphene fi lm could retain high conductivity at very low (atomic) thickness, and avoid contact resistance that occurs in a carbon nanotubes between interconnected nanotube bundles.
Abstract: www.MaterialsViews.com C O M Graphene-On-Silicon Schottky Junction Solar Cells M U N I By Xinming Li , Hongwei Zhu , * Kunlin Wang , * Anyuan Cao , Jinquan Wei , Chunyan Li , Yi Jia , Zhen Li , Xiao Li , and Dehai Wu C A IO N Graphene, a single atomic layer of carbon hexagons, has stimulated a lot of research interest owing to its unique structure and fascinating properties. [ 1 ] Graphene has been produced in the form of ultrathin sheets consisting of one or a few atomic layers by chemical vapor deposition (CVD) [ 2–4 ] or solution processing [ 5 , 6 ] and can be transferred to various substrates. The two-dimensionality and structural fl atness make graphene sheets ideal candidates for thin-fi lm devices and combination with other semiconductor materials such as silicon. These fi lms typically show sheet resistances on the order of several hundred ohm per square at about 80% optical transparency. [ 7 ] With modifi cation on the electronic properties and improvement of processing techniques, graphene fi lms show potential for use in conductive, fl exible electrodes, as an alternative for indium tin oxide (ITO). Graphene applications are just starting, and current investigations are on a number of areas such as fi llers for composites, nanoelectronics, and transparent electrodes. [ 8 ] For applications related to solar cells, graphene microsheets were dispersed into conjugated polymers to improve exciton dissociation and charge transport. [ 9–11 ] Also, solution-processed thin fi lms were used as conductive and transparent electrodes for organic [ 12 ] and dyesensitized [ 13 ] solar cells, although the cell effi ciency is still lower than those with ITO and fl uorine tin oxide (FTO) electrodes. Compared with carbon nanotube fi lms that have been extensively studied, graphene fi lms may have several advantages. A continuous single-layer graphene fi lm could retain high conductivity at very low (atomic) thickness, and avoid contact resistance that occurs in a carbon nanotube fi lm between interconnected nanotube bundles. In addition, graphene fi lms have minimum porosity and, in small areas, can provide an extremely fl at surface for molecule assembly and device integration. There are many opportunities in utilizing distinct properties of graphene and exploring novel applications. Bulk heterojunction structures based on carbon materials have attracted a great deal of interest for both scientifi c fundamentals and potential applications in various new optoelectronic devices,

1,070 citations

Journal ArticleDOI
TL;DR: A new physical and chemical entrapment strategy is based on a highly efficient sulfur host, namely hollow carbon nanofibers filled with MnO2 nanosheets, which efficiently prevents polysulfide dissolution in Lithium-sulfur batteries.
Abstract: Lithium–sulfur batteries have been investigated as promising electrochemical-energy storage systems owing to their high theoretical energy density. Sulfur-based cathodes must not only be highly conductive to enhance the utilization of sulfur, but also effectively confine polysulfides to mitigate their dissolution. A new physical and chemical entrapment strategy is based on a highly efficient sulfur host, namely hollow carbon nanofibers (HCFs) filled with MnO2 nanosheets. Benefiting from both the HCFs and birnessite-type MnO2 nanosheets, the MnO2@HCF hybrid host not only facilitates electron and ion transfer during the redox reactions, but also efficiently prevents polysulfide dissolution. With a high sulfur content of 71 wt % in the composite and an areal sulfur mass loading of 3.5 mg cm−2 in the electrode, the MnO2@HCF/S electrode delivered a specific capacity of 1161 mAh g−1 (4.1 mAh cm−2) at 0.05 C and maintained a stable cycling performance at 0.5 C over 300 cycles.

787 citations

Journal ArticleDOI
TL;DR: A new deep learning method that predicts contacts by integrating both evolutionary coupling (EC) and sequence conservation information through an ultra-deep neural network formed by two deep residual neural networks that greatly outperforms existing methods and leads to much more accurate contact-assisted folding.
Abstract: Motivation Protein contacts contain key information for the understanding of protein structure and function and thus, contact prediction from sequence is an important problem. Recently exciting progress has been made on this problem, but the predicted contacts for proteins without many sequence homologs is still of low quality and not very useful for de novo structure prediction. Method This paper presents a new deep learning method that predicts contacts by integrating both evolutionary coupling (EC) and sequence conservation information through an ultra-deep neural network formed by two deep residual neural networks. The first residual network conducts a series of 1-dimensional convolutional transformation of sequential features; the second residual network conducts a series of 2-dimensional convolutional transformation of pairwise information including output of the first residual network, EC information and pairwise potential. By using very deep residual networks, we can accurately model contact occurrence patterns and complex sequence-structure relationship and thus, obtain higher-quality contact prediction regardless of how many sequence homologs are available for proteins in question. Results Our method greatly outperforms existing methods and leads to much more accurate contact-assisted folding. Tested on 105 CASP11 targets, 76 past CAMEO hard targets, and 398 membrane proteins, the average top L long-range prediction accuracy obtained by our method, one representative EC method CCMpred and the CASP11 winner MetaPSICOV is 0.47, 0.21 and 0.30, respectively; the average top L/10 long-range accuracy of our method, CCMpred and MetaPSICOV is 0.77, 0.47 and 0.59, respectively. Ab initio folding using our predicted contacts as restraints but without any force fields can yield correct folds (i.e., TMscore>0.6) for 203 of the 579 test proteins, while that using MetaPSICOV- and CCMpred-predicted contacts can do so for only 79 and 62 of them, respectively. Our contact-assisted models also have much better quality than template-based models especially for membrane proteins. The 3D models built from our contact prediction have TMscore>0.5 for 208 of the 398 membrane proteins, while those from homology modeling have TMscore>0.5 for only 10 of them. Further, even if trained mostly by soluble proteins, our deep learning method works very well on membrane proteins. In the recent blind CAMEO benchmark, our fully-automated web server implementing this method successfully folded 6 targets with a new fold and only 0.3L-2.3L effective sequence homologs, including one β protein of 182 residues, one α+β protein of 125 residues, one α protein of 140 residues, one α protein of 217 residues, one α/β of 260 residues and one α protein of 462 residues. Our method also achieved the highest F1 score on free-modeling targets in the latest CASP (Critical Assessment of Structure Prediction), although it was not fully implemented back then. Availability http://raptorx.uchicago.edu/ContactMap/

779 citations


Cited by
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[...]

08 Dec 2001-BMJ
TL;DR: There is, I think, something ethereal about i —the square root of minus one, which seems an odd beast at that time—an intruder hovering on the edge of reality.
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 …

33,785 citations

01 May 1993
TL;DR: 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.
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.

29,323 citations

Journal ArticleDOI
15 Jul 2021-Nature
TL;DR: For example, AlphaFold as mentioned in this paper predicts protein structures with an accuracy competitive with experimental structures in the majority of cases using a novel deep learning architecture. But the accuracy is limited by the fact that no homologous structure is available.
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.

10,601 citations