Beijing Institute of Technology

Twelve zeolitic imidazolate frameworks (ZIFs; termed ZIF-1 to -12) have been synthesized as crystals by copolymerization of either Zn(II) (ZIF-1 to -4, -6 to -8, and -10 to -11) or Co(II) (ZIF-9 and -12) with imidazolate-type links. The ZIF crystal structures are based on the nets of seven distinct aluminosilicate zeolites: tetrahedral Si(Al) and the bridging O are replaced with transition metal ion and imidazolate link, respectively. In addition, one example of mixed-coordination imidazolate of Zn(II) and In(III) (ZIF-5) based on the garnet net is reported. Study of the gas adsorption and thermal and chemical stability of two prototypical members, ZIF-8 and -11, demonstrated their permanent porosity (Langmuir surface area = 1,810 m(2)/g), high thermal stability (up to 550 degrees C), and remarkable chemical resistance to boiling alkaline water and organic solvents.

Nitrogen-doped graphene (N-graphene) was synthesized by chemical vapor deposition of methane in the presence of ammonia. The resultant N-graphene was demonstrated to act as a metal-free electrode with a much better electrocatalytic activity, long-term operation stability, and tolerance to crossover effect than platinum for oxygen reduction via a four-electron pathway in alkaline fuel cells. To the best of our knowledge, this is the first report on the use of graphene and its derivatives as metal-free catalysts for oxygen reduction. The important role of N-doping to oxygen reduction reaction (ORR) can be applied to various carbon materials for the development of other metal-free efficient ORR catalysts for fuel cell applications, even new catalytic materials for applications beyond fuel cells.

Covalent organic frameworks (COFs) are a class of crystalline porous polymers that allow the atomically precise integration of organic units to create predesigned skeletons and nanopores. They have recently emerged as a new molecular platform for designing promising organic materials for gas storage, catalysis, and optoelectronic applications. The reversibility of dynamic covalent reactions, diversity of building blocks, and geometry retention are three key factors involved in the reticular design and synthesis of COFs. This tutorial review describes the basic design concepts, the recent synthetic advancements and structural studies, and the frontiers of functional exploration.

Deep learning methods employ multiple processing layers to learn hierarchical representations of data, and have produced state-of-the-art results in many domains. Recently, a variety of model designs and methods have blossomed in the context of natural language processing (NLP). In this paper, we review significant deep learning related models and methods that have been employed for numerous NLP tasks and provide a walk-through of their evolution. We also summarize, compare and contrast the various models and put forward a detailed understanding of the past, present and future of deep learning in NLP.

Organometal halide perovskites are inexpensive materials with desirable characteristics of color-tunable and narrow-band emissions for lighting and display technology, but they suffer from low photoluminescence quantum yields at low excitation fluencies. Here we developed a ligand-assisted reprecipitation strategy to fabricate brightly luminescent and color-tunable colloidal CH3NH3PbX3 (X = Br, I, Cl) quantum dots with absolute quantum yield up to 70% at room temperature and low excitation fluencies. To illustrate the photoluminescence enhancements in these quantum dots, we conducted comprehensive composition and surface characterizations and determined the time- and temperature-dependent photoluminescence spectra. Comparisons between small-sized CH3NH3PbBr3 quantum dots (average diameter 3.3 nm) and corresponding micrometer-sized bulk particles (2-8 μm) suggest that the intense increased photoluminescence quantum yield originates from the increase of exciton binding energy due to size reduction as well as proper chemical passivations of the Br-rich surface. We further demonstrated wide-color gamut white-light-emitting diodes using green emissive CH3NH3PbBr3 quantum dots and red emissive K2SiF6:Mn(4+) as color converters, providing enhanced color quality for display technology. Moreover, colloidal CH3NH3PbX3 quantum dots are expected to exhibit interesting nanoscale excitonic properties and also have other potential applications in lasers, electroluminescence devices, and optical sensors.

The rising global energy demand and environmental impact of traditional energy resources pose serious challenges to human health, energy security, and environmental protection. One promising solution is fuel cell technology, which provides clean and sustainable power. Besides, the catalytic performance of many non-precious metal catalysts still needs to be further improved to meet the requirement for practical applications. These special physicochemical properties, in turn, allow for controlled structural modifications of fullerenes, leading to the formation of various advanced fullerene derivatives with appropriate properties for many potential applications.

ADVERTISEMENT RETURN TO ISSUEPREVReviewNEXTRecent Advances in Polyoxometalate-Catalyzed ReactionsSa-Sa Wang† and Guo-Yu Yang*†‡View Author Information† State Key Laboratory of Structural Chemistry, Fujian Institute of Research on the Structure of Matter, Chinese Academy of Sciences, Fuzhou, Fujian 350002, China‡ MOE Key Laboratory of Cluster Science, School of Chemistry, Beijing Institute of Technology, Beijing 100081, China*E-mail: [email protected]; [email protected]. Fax: (+)86-591-8371-0051. Phone: (+)86-10-6891-8572.Cite this: Chem. Rev. 2015, 115, 11, 4893–4962Publication Date (Web):May 12, 2015Publication History Received20 July 2014Published online12 May 2015Published inissue 10 June 2015https://pubs.acs.org/doi/10.1021/cr500390vhttps://doi.org/10.1021/cr500390vreview-articleACS PublicationsCopyright © 2015 American Chemical SocietyRequest reuse permissionsArticle Views27041Altmetric-Citations1691LEARN ABOUT THESE METRICSArticle Views are the COUNTER-compliant sum of full text article downloads since November 2008 (both PDF and HTML) across all institutions and individuals. These metrics are regularly updated to reflect usage leading up to the last few days.Citations are the number of other articles citing this article, calculated by Crossref and updated daily. Find more information about Crossref citation counts.The Altmetric Attention Score is a quantitative measure of the attention that a research article has received online. Clicking on the donut icon will load a page at altmetric.com with additional details about the score and the social media presence for the given article. Find more information on the Altmetric Attention Score and how the score is calculated. Share Add toView InAdd Full Text with ReferenceAdd Description ExportRISCitationCitation and abstractCitation and referencesMore Options Share onFacebookTwitterWechatLinked InRedditEmail Other access optionsGet e-Alertsclose SUBJECTS:Anions,Catalysts,Hydrocarbons,Oxidation,Selectivity Get e-Alerts

Person re-identification (Re-ID) aims at retrieving a person of interest across multiple non-overlapping cameras. With the advancement of deep neural networks and increasing demand of intelligent video surveillance, it has gained significantly increased interest in the computer vision community. By dissecting the involved components in developing a person Re-ID system, we categorize it into the closed-world and open-world settings. The widely studied closed-world setting is usually applied under various research-oriented assumptions, and has achieved inspiring success using deep learning techniques on a number of datasets. We first conduct a comprehensive overview with in-depth analysis for closed-world person Re-ID from three different perspectives, including deep feature representation learning, deep metric learning and ranking optimization. With the performance saturation under closed-world setting, the research focus for person Re-ID has recently shifted to the open-world setting, facing more challenging issues. This setting is closer to practical applications under specific scenarios. We summarize the open-world Re-ID in terms of five different aspects. By analyzing the advantages of existing methods, we design a powerful AGW baseline, achieving state-of-the-art or at least comparable performance on twelve datasets for four different Re-ID tasks. Meanwhile, we introduce a new evaluation metric (mINP) for person Re-ID, indicating the cost for finding all the correct matches, which provides an additional criteria to evaluate the Re-ID system for real applications. Finally, some important yet under-investigated open issues are discussed.

Graphene quantum dots (GQDs) represent a new class of quantum dots with unique properties. Doping GQDs with heteroatoms provides an attractive means of effectively tuning their intrinsic properties and exploiting new phenomena for advanced device applications. Herein we report a simple electrochemical approach to luminescent and electrocatalytically active nitrogen-doped GQDs (N-GQDs) with oxygen-rich functional groups. Unlike their N-free counterparts, the newly produced N-GQDs with a N/C atomic ratio of ca. 4.3% emit blue luminescence and possess an electrocatalytic activity comparable to that of a commercially available Pt/C catalyst for the oxygen reduction reaction (ORR) in an alkaline medium. In addition to their use as metal-free ORR catalysts in fuel cells, the superior luminescence characteristic of N-GQDs allows them to be used for biomedical imaging and other optoelectronic applications.

Tremendous efforts are being made to develop electrode materials, electrolytes, and separators for energy storage devices to meet the needs of emerging technologies such as electric vehicles, decarbonized electricity, and electrochemical energy storage. However, the sustainability concerns of lithium-ion batteries (LIBs) and next-generation rechargeable batteries have received little attention. Recycling plays an important role in the overall sustainability of future batteries and is affected by battery attributes including environmental hazards and the value of their constituent resources. Therefore, recycling should be considered when developing battery systems. Herein, we provide a systematic overview of rechargeable battery sustainability. With a particular focus on electric vehicles, we analyze the market competitiveness of batteries in terms of economy, environment, and policy. Considering the large volumes of batteries soon to be retired, we comprehensively evaluate battery utilization and recycling from the perspectives of economic feasibility, environmental impact, technology, and safety. Battery sustainability is discussed with respect to life-cycle assessment and analyzed from the perspectives of strategic resources and economic demand. Finally, we propose a 4H strategy for battery recycling with the aims of high efficiency, high economic return, high environmental benefit, and high safety. New challenges and future prospects for battery sustainability are also highlighted.

<b><i>Objective:</i></b> We explored whether medical health workers had more psychosocial problems than nonmedical health workers during the COVID-19 outbreak. <b><i>Methods:</i></b> An online survey was run from February 19 to March 6, 2020; a total of 2,182 Chinese subjects participated. Mental health variables were assessed via the Insomnia Severity Index (ISI), the Symptom Check List-revised (SCL-90-R), and the Patient Health Questionnaire-4 (PHQ-4), which included a 2-item anxiety scale and a 2-item depression scale (PHQ-2). <b><i>Results:</i></b> Compared with nonmedical health workers (<i>n</i> = 1,255), medical health workers (<i>n</i> = 927) had a higher prevalence of insomnia (38.4 vs. 30.5%, <i>p</i> < 0.01), anxiety (13.0 vs. 8.5%, <i>p</i> < 0.01), depression (12.2 vs. 9.5%; <i>p</i>< 0.04), somatization (1.6 vs. 0.4%; <i>p</i> < 0.01), and obsessive-compulsive symptoms (5.3 vs. 2.2%; <i>p</i> < 0.01). They also had higher total scores of ISI, GAD-2, PHQ-2, and SCL-90-R obsessive-compulsive symptoms (<i>p</i> ≤ 0.01). Among medical health workers, having organic disease was an independent factor for insomnia, anxiety, depression, somatization, and obsessive-compulsive symptoms (<i>p</i> < 0.05 or 0.01). Living in rural areas, being female, and being at risk of contact with COVID-19 patients were the most common risk factors for insomnia, anxiety, obsessive-compulsive symptoms, and depression (<i>p</i> < 0.01 or 0.05). Among nonmedical health workers, having organic disease was a risk factor for insomnia, depression, and obsessive-compulsive symptoms (<i>p</i> < 0.01 or 0.05). <b><i>Conclusions:</i></b> During the COVID-19 outbreak, medical health workers had psychosocial problems and risk factors for developing them. They were in need of attention and recovery programs.

Green-luminescent functional graphene quantum dots (GQDs) are prepared by a facile electrochemical approach. The GQDs are rich in oxygen-containing functional groups and soluble in aqueous or organic media, facilitating further functionalization and various applications. Polymer photovoltaic devices using GQDs as a new type of electron-acceptor material are also demonstrated. Detailed facts of importance to specialist readers are published as ”Supporting Information”. Such documents are peer-reviewed, but not copy-edited or typeset. They are made available as submitted by the authors. Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article.

NPs mimic all three cellular antioxidant enzymes including superoxide dismutase, catalase, and glutathione peroxidase. Taking advantage of the physiochemical properties of nanomaterials, nanozymes have shown a broad range of applications from in vitro detection to replacing specific enzymes in living systems. With the emergence of the new concept of "nanozymology", nanozymes have now become an emerging new field connecting nanotechnology and biology. Since the landmark paper on nanozymes was published in 2007, we have extensively explored their catalytic mechanism, established the corresponding standards to quantitatively determine their catalytic activities, and opened up a broad range of applications from biological detection and environmental monitoring to disease diagnosis and biomedicine development. Here we mainly focus on our progress in the systematic design and construction of functionally specific nanozymes, the standardization of nanozyme research, and the exploration of their applications for replacing natural enzymes in living systems. We also show that, by combining the unique physicochemical properties and enzyme-like catalytic activities, nanozymes can offer a variety of multifunctional platforms with a broad of applications from in vitro detection to in vivo monitoring and therapy. For instance, targeting antibody-conjugated ferromagnetic nanozymes simultaneously provide three functions: target capture, magnetic separation, and nanozyme color development for target detection. We finally will address the prospect of nanozyme research to become "nanozymology". We expect that nanozymes with unique physicochemical properties and intrinsic enzyme-mimicking catalytic properties will attract broad interest in both fundamental research and practical applications and offer new opportunities for traditional enzymology.

Chemical graphitized r-GOs, as the thinnest and lightest material in the carbon family, exhibit high-efficiency electromagnetic interference (EMI) shielding at elevated temperature, attributed to the cooperation of dipole polarization and hopping conductivity. The r-GO composites show different temperature-dependent imaginary permittivities and EMI shielding performances with changing mass ratio.

Hierarchical porous nitrogen-doped carbon (HPNC) nanosheets (NS) have been prepared via simultaneous activation and graphitization of biomass-derived natural silk. The as-obtained HPNC-NS show favorable features for electrochemical energy storage such as high specific surface area (SBET: 2494 m(2)/g), high volume of hierarchical pores (2.28 cm(3)/g), nanosheet structures, rich N-doping (4.7%), and defects. With respect to the multiple synergistic effects of these features, a lithium-ion battery anode and a two-electrode-based supercapacitor have been prepared. A reversible lithium storage capacity of 1865 mA h/g has been reported, which is the highest for N-doped carbon anode materials to the best of our knowledge. The HPNC-NS supercapacitor's electrode in ionic liquid electrolytes exhibit a capacitance of 242 F/g and energy density of 102 W h/kg (48 W h/L), with high cycling life stability (9% loss after 10,000 cycles). Thus, a high-performance Li-ion battery and supercapacitors were successfully assembled for the same electrode material, which was obtained through a one-step and facile large-scale synthesis route. It is promising for next-generation hybrid energy storage and renewable delivery devices.

ABSTRACT RNA interference (RNAi) is an ancient biological mechanism used to defend against external invasion. It theoretically can silence any disease-related genes in a sequence-specific manner, making small interfering RNA (siRNA) a promising therapeutic modality. After a two-decade journey from its discovery, two approvals of siRNA therapeutics, ONPATTRO ® (patisiran) and GIVLAARI™ (givosiran), have been achieved by Alnylam Pharmaceuticals. Reviewing the long-term pharmaceutical history of human beings, siRNA therapy currently has set up an extraordinary milestone, as it has already changed and will continue to change the treatment and management of human diseases. It can be administered quarterly, even twice-yearly, to achieve therapeutic effects, which is not the case for small molecules and antibodies. The drug development process was extremely hard, aiming to surmount complex obstacles, such as how to efficiently and safely deliver siRNAs to desired tissues and cells and how to enhance the performance of siRNAs with respect to their activity, stability, specificity and potential off-target effects. In this review, the evolution of siRNA chemical modifications and their biomedical performance are comprehensively reviewed. All clinically explored and commercialized siRNA delivery platforms, including the GalNAc ( N -acetylgalactosamine)–siRNA conjugate, and their fundamental design principles are thoroughly discussed. The latest progress in siRNA therapeutic development is also summarized. This review provides a comprehensive view and roadmap for general readers working in the field.

Extreme learning machine (ELM) is an emerging learning algorithm for the generalized single hidden layer feedforward neural networks, of which the hidden node parameters are randomly generated and the output weights are analytically computed. However, due to its shallow architecture, feature learning using ELM may not be effective for natural signals (e.g., images/videos), even with a large number of hidden nodes. To address this issue, in this paper, a new ELM-based hierarchical learning framework is proposed for multilayer perceptron. The proposed architecture is divided into two main components: 1) self-taught feature extraction followed by supervised feature classification and 2) they are bridged by random initialized hidden weights. The novelties of this paper are as follows: 1) unsupervised multilayer encoding is conducted for feature extraction, and an ELM-based sparse autoencoder is developed via l1 constraint. By doing so, it achieves more compact and meaningful feature representations than the original ELM; 2) by exploiting the advantages of ELM random feature mapping, the hierarchically encoded outputs are randomly projected before final decision making, which leads to a better generalization with faster learning speed; and 3) unlike the greedy layerwise training of deep learning (DL), the hidden layers of the proposed framework are trained in a forward manner. Once the previous layer is established, the weights of the current layer are fixed without fine-tuning. Therefore, it has much better learning efficiency than the DL. Extensive experiments on various widely used classification data sets show that the proposed algorithm achieves better and faster convergence than the existing state-of-the-art hierarchical learning methods. Furthermore, multiple applications in computer vision further confirm the generality and capability of the proposed learning scheme.

Metal-halide perovskites have rapidly emerged as one of the most promising materials of the 21st century, with many exciting properties and great potential for a broad range of applications, from photovoltaics to optoelectronics and photocatalysis. The ease with which metal-halide perovskites can be synthesized in the form of brightly luminescent colloidal nanocrystals, as well as their tunable and intriguing optical and electronic properties, has attracted researchers from different disciplines of science and technology. In the last few years, there has been a significant progress in the shape-controlled synthesis of perovskite nanocrystals and understanding of their properties and applications. In this comprehensive review, researchers having expertise in different fields (chemistry, physics, and device engineering) of metal-halide perovskite nanocrystals have joined together to provide a state of the art overview and future prospects of metal-halide perovskite nanocrystal research.

Starting from symmetry considerations and the tight-binding method in combination with first-principles calculation, we systematically derive the low-energy effective Hamiltonian involving spin-orbit coupling (SOC) for silicene. This Hamiltonian is very general because it applies not only to silicene itself but also to the low-buckled counterparts of graphene for the other group-IVA elements Ge and Sn, as well as to graphene when the structure returns to the planar geometry. The effective Hamitonian is the analog to the graphene quantum spin Hall effect (QSHE) Hamiltonian. As in the graphene model, the effective SOC in low-buckled geometry opens a gap at the Dirac points and establishes the QSHE. The effective SOC actually contains the first order in the atomic intrinsic SOC strength ${\ensuremath{\xi}}_{0}$, while this leading-order contribution of SOC vanishes in the planar structure. Therefore, silicene, as well as the low-buckled counterparts of graphene for the other group-IVA elements Ge and Sn, has a much larger gap opened by the effective SOC at the Dirac points than graphene, due to the low-buckled geometry and larger atomic intrinsic SOC strength. Further, the more buckled is the structure, the greater is the gap. Therefore, the QSHE can be observed in low-buckled Si, Ge, and Sn systems in an experimentally accessible temperature regime. In addition, the Rashba SOC in silicene is intrinsic due to its own low-buckled geometry, which vanishes at the Dirac point $K$, while it has a nonzero value with deviation of $\stackrel{P\vec}{k}$ from the $K$ point. Therefore, the QSHE in silicene is robust against the intrinsic Rashba SOC.

In the search for evidence of silicene, a two-dimensional honeycomb lattice of silicon, it is important to obtain a complete picture for the evolution of Si structures on Ag(111), which is believed to be the most suitable substrate for growth of silicene so far. In this work we report the finding and evolution of several monolayer superstructures of silicon on Ag(111), depending on the coverage and temperature. Combined with first-principles calculations, the detailed structures of these phases have been illuminated. These structures were found to share common building blocks of silicon rings, and they evolve from a fragment of silicene to a complete monolayer silicene and multilayer silicene. Our results elucidate how silicene forms on Ag(111) surface and provides methods to synthesize high-quality and large-scale silicene.