China University of Petroleum, Beijing

Abstract Hierarchical flowerlike nickel hydroxide decorated on graphene sheets has been prepared by a facile and cost‐effective microwave‐assisted method. In order to achieve high energy and power densities, a high‐voltage asymmetric supercapacitor is successfully fabricated using Ni(OH) 2 /graphene and porous graphene as the positive and negative electrodes, respectively. Because of their unique structure, both of these materials exhibit excellent electrochemical performances. The optimized asymmetric supercapacitor could be cycled reversibly in the high‐voltage region of 0–1.6 V and displays intriguing performances with a maximum specific capacitance of 218.4 F g −1 and high energy density of 77.8 Wh kg −1 . Furthermore, the Ni(OH) 2 /graphene//porous graphene supercapacitor device exhibits an excellent long cycle life along with 94.3% specific capacitance retained after 3000 cycles. These fascinating performances can be attributed to the high capacitance and the positive synergistic effects of the two electrodes. The impressive results presented here may pave the way for promising applications in high energy density storage systems.

Abstract Asymmetric supercapacitor with high energy density has been developed successfully using graphene/MnO 2 composite as positive electrode and activated carbon nanofibers (ACN) as negative electrode in a neutral aqueous Na 2 SO 4 electrolyte. Due to the high capacitances and excellent rate performances of graphene/MnO 2 and ACN, as well as the synergistic effects of the two electrodes, such asymmetric cell exhibits superior electrochemical performances. An optimized asymmetric supercapacitor can be cycled reversibly in the voltage range of 0–1.8 V, and exhibits maximum energy density of 51.1 Wh kg −1 , which is much higher than that of MnO 2 //DWNT cell (29.1 Wh kg −1 ). Additionally, graphene/MnO 2 //ACN asymmetric supercapacitor exhibits excellent cycling durability, with 97% specific capacitance retained even after 1000 cycles. These encouraging results show great potential in developing energy storage devices with high energy and power densities for practical applications.

Deep learning with noisy labels is practically challenging, as the capacity of deep models is so high that they can totally memorize these noisy labels sooner or later during training. Nonetheless, recent studies on the memorization effects of deep neural networks show that they would first memorize training data of clean labels and then those of noisy labels. Therefore in this paper, we propose a new deep learning paradigm called ''Co-teaching'' for combating with noisy labels. Namely, we train two deep neural networks simultaneously, and let them teach each other given every mini-batch: firstly, each network feeds forward all data and selects some data of possibly clean labels; secondly, two networks communicate with each other what data in this mini-batch should be used for training; finally, each network back propagates the data selected by its peer network and updates itself. Empirical results on noisy versions of MNIST, CIFAR-10 and CIFAR-100 demonstrate that Co-teaching is much superior to the state-of-the-art methods in the robustness of trained deep models.

The mixed halide perovskites have emerged as outstanding light absorbers for efficient solar cells. Unfortunately, it reveals inhomogeneity in these polycrystalline films due to composition separation, which leads to local lattice mismatches and emergent residual strains consequently. Thus far, the understanding of these residual strains and their effects on photovoltaic device performance is absent. Herein we study the evolution of residual strain over the films by depth-dependent grazing incident X-ray diffraction measurements. We identify the gradient distribution of in-plane strain component perpendicular to the substrate. Moreover, we reveal its impacts on the carrier dynamics over corresponding solar cells, which is stemmed from the strain induced energy bands bending of the perovskite absorber as indicated by first-principles calculations. Eventually, we modulate the status of residual strains in a controllable manner, which leads to enhanced PCEs up to 20.7% (certified) in devices via rational strain engineering.

Perovskite oxides with formula ABO3 or A2BO4 are a very important class of functional materials that exhibit a range of stoichiometries and crystal structures. Because of the structural features, they could accommodate around 90% of the metallic natural elements of the Periodic Table that stand solely or partially at the A and/or B positions without destroying the matrix structure, offering a way of correlating solid state chemistry to catalytic properties. Moreover, their high thermal and hydrothermal stability enable them suitable catalytic materials either for gas or solid reactions carried out at high temperatures, or liquid reactions carried out at low temperatures. In this review, we addressed the preparation, characterization, and application of perovskite oxides in heterogeneous catalysis. Preparation is an important issue in catalysis by which materials with desired textural structure and physicochemical property could be achieved; characterization is the way to explore and understand the textural structures and physicochemical properties of the material; however, application reflects how and where the material could be used and what it can solve in practice, which is the ultimate goal of catalysis. This review is organized in five sections: (1) a brief introduction to perovskite oxides, (2) preparation of perovskite oxides with different textural structures and surface morphologies, (3) general characterizations applied to perovskite oxides, (4) application of perovskite oxides in heterogeneous catalysis, and (5) conclusions and perspectives. We expected that the overview on these achievements could lead to research on the nature of catalytic performances of perovskite oxides and finally commercialization of them for industrial use.

Water is an essential participant in the stability, structure, dynamics, and function of proteins and other biomolecules. Thermodynamically, changes in the aqueous environment affect the stability of biomolecules. Structurally, water participates chemically in the catalytic function of proteins and nucleic acids and physically in the collapse of the protein chain during folding through hydrophobic collapse and mediates binding through the hydrogen bond in complex formation. Water is a partner that slaves the dynamics of proteins, and water interaction with proteins affect their dynamics. Here we provide a review of the experimental and computational advances over the past decade in understanding the role of water in the dynamics, structure, and function of proteins. We focus on the combination of X-ray and neutron crystallography, NMR, terahertz spectroscopy, mass spectroscopy, thermodynamics, and computer simulations to reveal how water assist proteins in their function. The recent advances in computer simulations and the enhanced sensitivity of experimental tools promise major advances in the understanding of protein dynamics, and water surely will be a protagonist.

In modern industries, machine health monitoring systems (MHMS) have been applied wildly with the goal of realizing predictive maintenance including failures tracking, downtime reduction, and assets preservation. In the era of big machinery data, data-driven MHMS have achieved remarkable results in the detection of faults after the occurrence of certain failures (diagnosis) and prediction of the future working conditions and the remaining useful life (prognosis). The numerical representation for raw sensory data is the key stone for various successful MHMS. Conventional methods are the labor-extensive as they usually depend on handcrafted features, which require expert knowledge. Inspired by the success of deep learning methods that redefine representation learning from raw data, we propose local feature-based gated recurrent unit (LFGRU) networks. It is a hybrid approach that combines handcrafted feature design with automatic feature learning for machine health monitoring. First, features from windows of input time series are extracted. Then, an enhanced bidirectional GRU network is designed and applied on the generated sequence of local features to learn the representation. A supervised learning layer is finally trained to predict machine condition. Experiments on three machine health monitoring tasks: tool wear prediction, gearbox fault diagnosis, and incipient bearing fault detection verify the effectiveness and generalization of the proposed LFGRU.

The review summarizes transition metal-based bimetallic MOFs and their derived materials as electrocatalytic materials for the OER. The mechanisms of the OER as probed by DFT calculation and<italic>in situ</italic>characterization techniques are also discussed.

In modern manufacturing systems and industries, more and more research efforts have been made in developing effective machine health monitoring systems. Among various machine health monitoring approaches, data-driven methods are gaining in popularity due to the development of advanced sensing and data analytic techniques. However, considering the noise, varying length and irregular sampling behind sensory data, this kind of sequential data cannot be fed into classification and regression models directly. Therefore, previous work focuses on feature extraction/fusion methods requiring expensive human labor and high quality expert knowledge. With the development of deep learning methods in the last few years, which redefine representation learning from raw data, a deep neural network structure named Convolutional Bi-directional Long Short-Term Memory networks (CBLSTM) has been designed here to address raw sensory data. CBLSTM firstly uses CNN to extract local features that are robust and informative from the sequential input. Then, bi-directional LSTM is introduced to encode temporal information. Long Short-Term Memory networks(LSTMs) are able to capture long-term dependencies and model sequential data, and the bi-directional structure enables the capture of past and future contexts. Stacked, fully-connected layers and the linear regression layer are built on top of bi-directional LSTMs to predict the target value. Here, a real-life tool wear test is introduced, and our proposed CBLSTM is able to predict the actual tool wear based on raw sensory data. The experimental results have shown that our model is able to outperform several state-of-the-art baseline methods.

Novel CdS quantum dot (QD)-coupled graphitic carbon nitride (g-C3N4) photocatalysts were synthesized via a chemical impregnation method and characterized by X-ray diffraction, transmission electron microscopy, ultraviolet–visible diffuse reflection spectroscopy, X-ray photoelectron spectroscopy, Fourier transform infrared spectroscopy, and photoluminescence spectroscopy. The effect of CdS content on the rate of visible light photocatalytic hydrogen evolution was investigated for different CdS loadings using platinum as a cocatalyst in methanol aqueous solutions. The synergistic effect of g-C3N4 and CdS QDs leads to efficient separation of the photogenerated charge carriers and, consequently, enhances the visible light photocatalytic H2 production activity of the materials. The optimal CdS QD content is determined to be 30 wt %, and the corresponding H2 evolution rate was 17.27 μmol·h–1 under visible light irradiation, ∼9 times that of pure g-C3N4. A possible photocatalytic mechanism of the CdS/g-C3N4 composite is proposed and corroborated by photoluminescence spectroscopy and photoelectrochemical curves.

Research on heterogeneous single-atom catalysts (SACs) has become an emerging frontier in catalysis science because of their advantages in high utilization of noble metals, precisely identified active sites, high selectivity, and tunable activity. Graphene, as a one-atom-thick two-dimensional carbon material with unique structural and electronic properties, has been reported to be a superb support for SACs. Herein, we provide an overview of recent progress in investigations of graphene-based SACs. Among the large number of publications, we will selectively focus on the stability of metal single-atoms (SAs) anchored on different sites of graphene support and the catalytic performances of graphene-based SACs for different chemical reactions, including thermocatalysis and electrocatalysis. We will summarize the fundamental understandings on the electronic structures and their intrinsic connection with catalytic properties of graphene-based SACs, and also provide a brief perspective on the future design of efficient SACs with graphene and graphene-like materials.

Monodispersed nickel phosphide nanocrystals (NCs) with different phases were successfully synthesized. The Ni₅P₄ NCs, with a solid structure, exhibited higher catalytic activity than the Ni₁₂P₅ and Ni₂P NCs.

Sulfur-doped carbon dots were synthesized by a one-step hydrothermal method and exhibited high fluorescence quantum yield (67%) and exceptional emission behavior.

Despite the emergence of experimental methods for simultaneous measurement of multiple omics modalities in single cells, most single-cell datasets include only one modality. A major obstacle in integrating omics data from multiple modalities is that different omics layers typically have distinct feature spaces. Here, we propose a computational framework called GLUE (graph-linked unified embedding), which bridges the gap by modeling regulatory interactions across omics layers explicitly. Systematic benchmarking demonstrated that GLUE is more accurate, robust and scalable than state-of-the-art tools for heterogeneous single-cell multi-omics data. We applied GLUE to various challenging tasks, including triple-omics integration, integrative regulatory inference and multi-omics human cell atlas construction over millions of cells, where GLUE was able to correct previous annotations. GLUE features a modular design that can be flexibly extended and enhanced for new analysis tasks. The full package is available online at https://github.com/gao-lab/GLUE .

Heterogeneous catalytic ozonation (HCO) processes have been widely studied for water purification. The reaction mechanisms of these processes are very complicated because of the simultaneous involvement of gas, solid, and liquid phases. Although typical reaction mechanisms have been established for HCO, some of them are only appropriate for specific systems. The divergence and deficiency in mechanisms hinders the development of novel active catalysts. This critical review compares the various existing mechanisms and categorizes the catalytic oxidation of HCO into radical-based oxidation and nonradical oxidation processes with an in-depth discussion. The catalytic active sites and adsorption behaviors of O3 molecules on the catalyst surface are regarded as the key clues for further elucidating the O3 activation processes, evolution of reactive oxygen species (ROS) or organic oxidation pathways. Moreover, the detection methods of the ROS produced in both types of oxidations and their roles in the destruction of organics are reviewed with discussion of some specific problems among them, including the scavengers selection, experiment results analysis as well as some questionable conclusions. Finally, alternative strategies for the systematic investigation of the HCO mechanism and the prospects for future studies are envisaged.

Abstract This article reviews the mechanisms of shale gas storage and discusses the major risks or uncertainties for shale gas exploration in China. At a given temperature and pressure, the gas sorption capacities of organic-rich shales are primarily controlled by the organic matter richness but may be significantly influenced by the type and maturity of the organic matter, mineral composition (especially clay content), moisture content, pore volume and structure, resulting in different ratios of gas sorption capacity (GSC) to total organic carbon content for different shales. In laboratory experiments, the GSC of organic-rich shales increases with increasing pressure and decreases with increasing temperature. Under geologic conditions (assuming hydrostatic pressure gradient and constant thermal gradient), the GSC increases initially with depth due to the predominating effect of pressure, passes through a maximum, and then decreases because of the influence of increasing temperature at greater depth. This pattern of variation is quite similar to that observed for coals and is of great significance for understanding the changes in GSC of organic-rich shales over geologic time as a function of burial history. At an elevated temperature and pressure and with the presence of moisture, the gas sorption capacities of organic-rich shales are quite low. As a result, adsorption alone cannot protect sufficient gas for high-maturity organic-rich shales to be commercial gas reservoirs. Two models are proposed to predict the variation of GSC and total gas content over geologic time as a function of burial history. High contents of free gas in organic-rich shales can be preserved in relatively closed systems. Loss of free gas during postgeneration uplift and erosion may result in undersaturation (the total gas contents lower than the sorption capacity) and is the major risk for gas exploration in marine organic-rich shales in China.

Significance The flow of water confined in nanopores is significantly different from that of bulk water. Moreover, understanding and controlling the flow of the confined water remains an open question, especially concerning whether the flow capacity of the confined water increases or not compared with that of bulk water. Here, combining a theoretical analysis and data from molecular dynamics simulations and experiments in the literature we develop a simple model for the flow of water confined in nanopores. We find that a contact angle and a nanopore dimension may substantially affect the confined water flow. We also quantitatively explain a controversy over an increase or decrease in flow capacity.

Heteroatom-doped carbon dots (CDs), due to their excellent photoluminescence (PL) properties, attracted widespread attention recently and demonstrated immense promise for diverse applications, particularly for biological applications. The objective of this feature article is to provide a comprehensive overview of the recent progress in the research and development of heteroatom-doped CDs and a detailed description of the influence of single or co-doping heteroatoms on their PL behavior. The most recent understanding and critical insights into the PL mechanism of heteroatom-doped CDs are also highlighted. Moreover, potential bio-related applications of heteroatom-doped CDs in biosensing, bioimaging, and theranostics are also reviewed. This state-of-the-art review will provide a platform for understanding the intricate details of heteroatom-doped CDs, a summary of the latest progress in the field, and related applications in biology and is expected to inspire further developments in this exciting class of materials.

With significant advancement in information technologies, Digital Twin has gained increasing attention as it offers an enabling tool to realise digitally-driven, cloud-enabled manufacturing. Given the nonlinear dynamics and uncertainty involved during the process of machinery degradation, proper design and adaptability of a Digital Twin model remain a challenge. This paper presents a Digital Twin reference model for rotating machinery fault diagnosis. The requirements for constructing the Digital Twin model are discussed, and a model updating scheme based on parameter sensitivity analysis is proposed to enhance the model adaptability. Experimental data are collected from a rotor system that emulates an unbalance fault and its progression. The data are then input to a Digital Twin model of the rotor system to investigate its ability of unbalance quantification and localisation for fault diagnosis. The results show that the constructed Digital Twin rotor model enables accurate diagnosis and adaptive degradation analysis.

Since the start of last century, methanol synthesis has attracted great interests because of its importance in chemical industries and its potential as an environmentally friendly energy carrier. The catalyst for the methanol synthesis has been a key area of research in order to optimize the reaction process. In the literature, the nature of the active site and the effects of the promoter and support have been extensively investigated. In this updated review, the recent progresses in the catalyst innovation, optimization of the reaction conditions, reaction mechanism, and catalyst performance in methanol synthesis are comprehensively discussed. Key issues of catalyst improvement are highlighted, and areas of priority in R&D are identified in the conclusions.