Showing papers by "China University of Petroleum published in 2017"

Metal-Organic Frameworks Derived Nanotube of Nickel–Cobalt Bimetal Phosphides as Highly Efficient Electrocatalysts for Overall Water Splitting

Abstract: The design of highly efficient, stable, and noble-metal-free bifunctional electrocatalysts for overall water splitting is critical but challenging. Herein, a facile and controllable synthesis strategy for nickel–cobalt bimetal phosphide nanotubes as highly efficient electrocatalysts for overall water splitting via low-temperature phosphorization from a bimetallic metal-organic framework (MOF-74) precursor is reported. By optimizing the molar ratio of Co/Ni atoms in MOF-74, a series of CoxNiyP catalysts are synthesized, and the obtained Co4Ni1P has a rare form of nanotubes that possess similar morphology to the MOF precursor and exhibit perfect dispersal of the active sites. The nanotubes show remarkable hydrogen evolution reaction (HER) and oxygen evolution reaction (OER) catalytic performance in an alkaline electrolyte, affording a current density of 10 mA cm−2 at overpotentials of 129 mV for HER and 245 mV for OER, respectively. An electrolyzer with Co4Ni1P nanotubes as both the cathode and anode catalyst in alkaline solutions achieves a current density of 10 mA cm−2 at a voltage of 1.59 V, which is comparable to the integrated Pt/C and RuO2 counterparts and ranks among the best of the metal-phosphide electrocatalysts reported to date.

Learning to Monitor Machine Health with Convolutional Bi-Directional LSTM Networks.

Abstract: 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.

Observation of Internal Photoinduced Electron and Hole Separation in Hybrid Two-Dimentional Perovskite Films.

Abstract: Two-dimensional (2D) organolead halide perovskites are promising for various optoelectronic applications. Here we report a unique spontaneous charge (electron/hole) separation property in multilayered (BA)2(MA)n−1PbnI3n+1 (BA = CH3(CH2)3NH3+, MA = CH3NH3+) 2D perovskite films by studying the charge carrier dynamics using ultrafast transient absorption and photoluminescence spectroscopy. Surprisingly, the 2D perovskite films, although nominally prepared as “n = 4”, are found to be mixture of multiple perovskite phases, with n = 2, 3, 4 and ≈ ∞, that naturally align in the order of n along the direction perpendicular to the substrate. Driven by the band alignment between 2D perovskites phases, we observe consecutive photoinduced electron transfer from small-n to large-n phases and hole transfer in the opposite direction on hundreds of picoseconds inside the 2D film of ∼358 nm thickness. This internal charge transfer efficiently separates electrons and holes to the upper and bottom surfaces of the films, whi...

Evolution of the global phosphorus cycle

Abstract: The macronutrient phosphorus is thought to limit primary productivity in the oceans on geological timescales. Although there has been a sustained effort to reconstruct the dynamics of the phosphorus cycle over the past 3.5 billion years, it remains uncertain whether phosphorus limitation persisted throughout Earth’s history and therefore whether the phosphorus cycle has consistently modulated biospheric productivity and ocean–atmosphere oxygen levels over time. Here we present a compilation of phosphorus abundances in marine sedimentary rocks spanning the past 3.5 billion years. We find evidence for relatively low authigenic phosphorus burial in shallow marine environments until about 800 to 700 million years ago. Our interpretation of the database leads us to propose that limited marginal phosphorus burial before that time was linked to phosphorus biolimitation, resulting in elemental stoichiometries in primary producers that diverged strongly from the Redfield ratio (the atomic ratio of carbon, nitrogen and phosphorus found in phytoplankton). We place our phosphorus record in a quantitative biogeochemical model framework and find that a combination of enhanced phosphorus scavenging in anoxic, iron-rich oceans and a nutrient-based bistability in atmospheric oxygen levels could have resulted in a stable low-oxygen world. The combination of these factors may explain the protracted oxygenation of Earth’s surface over the last 3.5 billion years of Earth history. However, our analysis also suggests that a fundamental shift in the phosphorus cycle may have occurred during the late Proterozoic eon (between 800 and 635 million years ago), coincident with a previously inferred shift in marine redox states, severe perturbations to Earth’s climate system, and the emergence of animals.

Wettability effect on nanoconfined water flow

Abstract: Understanding and controlling the flow of water confined in nanopores has tremendous implications in theoretical studies and industrial applications. Here, we propose a simple model for the confined water flow based on the concept of effective slip, which is a linear sum of true slip, depending on a contact angle, and apparent slip, caused by a spatial variation of the confined water viscosity as a function of wettability as well as the nanopore dimension. Results from this model show that the flow capacity of confined water is 10 −1 ∼10 7 times that calculated by the no-slip Hagen–Poiseuille equation for nanopores with various contact angles and dimensions, in agreement with the majority of 53 different study cases from the literature. This work further sheds light on a controversy over an increase or decrease in flow capacity from molecular dynamics simulations and experiments.

Adaptable Blockchain-Based Systems: A Case Study for Product Traceability

Abstract: Traceability allows tracking products through all stages of a supply chain, which is crucial for product quality control. To provide accountability and forensic information, traceability information must be secured. This is challenging because traceability systems often must adapt to changes in regulations and to customized traceability inspection processes. OriginChain is a real-world traceability system using a blockchain. Blockchains are an emerging data storage technology that enables new forms of decentralized architectures. Components can agree on their shared states without trusting a central integration point. OriginChain’s architecture provides transparent tamper-proof traceability information, automates regulatory compliance checking, and enables system adaptability.

Polymer Composite with Improved Thermal Conductivity by Constructing a Hierarchically Ordered Three-Dimensional Interconnected Network of BN.

Abstract: In this work, we report a fabrication of epoxy resin/ordered three-dimensional boron nitride (3D-BN) network composites through combination of ice-templating self-assembly and infiltration methods. The polymer composites possess much higher thermal conductivity up to 4.42 W m–1 K–1 at relatively low loading 34 vol % than that of random distribution composites (1.81 W m–1 K–1 for epoxy/random 3D-BN composites, 1.16 W m–1 K–1 for epoxy/random BN composites) and exhibit a high glass transition temperature (178.9–229.2 °C) and dimensional stability (22.7 ppm/K). We attribute the increased thermal conductivity to the unique oriented 3D-BN thermally conducive network, in which the much higher thermal conductivity along the in-plane direction of BN microplatelets is most useful. This study paves the way for thermally conductive polymer composites used as thermal interface materials for next-generation electronic packaging and 3D integration circuits.

Bayesian Networks in Fault Diagnosis

Abstract: Fault diagnosis is useful in helping technicians detect, isolate, and identify faults, and troubleshoot. Bayesian network (BN) is a probabilistic graphical model that effectively deals with various uncertainty problems. This model is increasingly utilized in fault diagnosis. This paper presents bibliographical review on use of BNs in fault diagnosis in the last decades with focus on engineering systems. This work also presents general procedure of fault diagnosis modeling with BNs; processes include BN structure modeling, BN parameter modeling, BN inference, fault identification, validation, and verification. The paper provides series of classification schemes for BNs for fault diagnosis, BNs combined with other techniques, and domain of fault diagnosis with BN. This study finally explores current gaps and challenges and several directions for future research.

A Data-Driven Fault Diagnosis Methodology in Three-Phase Inverters for PMSM Drive Systems

Abstract: Permanent magnet synchronous motor and power electronics-based three-phase inverter are the major components in the modern industrial electric drive system, such as electrical actuators in an all-electric subsea Christmas tree. Inverters are the weakest components in the drive system, and power switches are the most vulnerable components in inverters. Fault detection and diagnosis of inverters are extremely necessary for improving drive system reliability. Motivated by solving the uncertainty problem in fault diagnosis of inverters, which is caused by various reasons, such as bias and noise of sensors, this paper proposes a Bayesian network-based data-driven fault diagnosis methodology of three-phase inverters. Two output line-to-line voltages for different fault modes are measured, the signal features are extracted using fast Fourier transform, the dimensions of samples are reduced using principal component analysis, and the faults are detected and diagnosed using Bayesian networks. Simulated and experimental data are used to train the fault diagnosis model, as well as validate the proposed fault diagnosis methodology.

Recent advances and challenges of metal–organic framework membranes for gas separation

Abstract: Gas separation is one of the most critical and challenging steps for industrial processes, and metal–organic framework (MOF) membranes are potential candidates for this application. This review mainly focuses on the recent advances in improving the performance of MOF membranes, involving the issues faced with MOF designation and growth for practical applications. First, we discussed three strategies for permeability and selectivity enhancement of MOF membranes, in terms of obtaining ultra-thin two-dimensional (2D) MOF nanosheets, fine-tuning the pore size of the MOF framework and integrating with other species. Second, we reviewed the recent potential resolutions to the problems of MOF membranes for future practical applications including scale-up preparation and stability improvement. Finally, we summarized our work by providing some general conclusions on the state-of-the-art and an outlook on some development directions of molecule-sieving membranes.

Room-temperature SO2 gas-sensing properties based on a metal-doped MoS2 nanoflower: an experimental and density functional theory investigation

Abstract: This paper demonstrates a sulfur dioxide (SO2) gas sensor based on a transition-metal-doped molybdenum disulfide (MoS2) nanocomposite synthesized via a facile single-step hydrothermal route. The Ni-doped, Fe-doped, Co-doped, and pristine MoS2 film sensors were fabricated on a FR4 epoxy substrate with interdigital electrodes. The morphologies, microstructures, and compositions of as-prepared samples were fully examined using X-ray diffraction, energy dispersive spectroscopy, scanning electron microscopy, transmission electron microscope, and X-ray photoelectron spectroscopy. The gas-sensing properties of the four samples were systematically investigated at room temperature, and the Ni-doped MoS2 film sensor was screened out as the optimal SO2 sensor among the four sensors, exhibiting a relatively high response value, quick response/recovery time, and excellent stability toward SO2 gas. Furthermore, in order to explain the experimental results, we used Materials Studio software to construct molecular models of adsorption systems and calculate the geometry, energy, and charge parameters via density functional theory (DFT) based on first principles. The sensing mechanism is also discussed in depth. Through a comprehensive research approach of combining experimentation with DFT simulation, this work suggests that an Ni-doped MoS2 film sensor is able to detect SO2 gas at room temperature.

BAS: Beetle Antennae Search Algorithm for Optimization Problems

Abstract: Meta-heuristic algorithms have become very popular because of powerful performance on the optimization problem. A new algorithm called beetle antennae search algorithm (BAS) is proposed in the paper inspired by the searching behavior of longhorn beetles. The BAS algorithm imitates the function of antennae and the random walking mechanism of beetles in nature, and then two main steps of detecting and searching are implemented. Finally, the algorithm is benchmarked on 2 well-known test functions, in which the numerical results validate the efficacy of the proposed BAS algorithm.

Layered double hydroxides toward high-performance supercapacitors

Abstract: The urgent demand for clean energies and rapid development of modern electronic technologies have led to enthusiastic research on novel energy storage technologies, especially for supercapacitors. The most important part is designing electrode materials with excellent capacitive performance. Layered double hydroxides (LDHs) have sparked intense interest among researchers in the past decade due to the facile tunability of their composition, structure and morphology. Various and fruitful accomplishments have been achieved toward developing LDH-based materials for supercapacitor electrodes. This review outlines the recent advances in the designing of LDH-based electrode materials for supercapacitors. Feasible and practical strategies for improving the capacitive performance of LDH-based materials have been discussed and highlighted in terms of tuning the composition of LDHs, designing the electrode structure and assembling applicable supercapacitor devices. Through the ceaseless efforts of scientists, the capacitive performance and practicability of LDH-based materials have been greatly ameliorated, making them more competitive for modern energy storage applications.

Quantitative detection of formaldehyde and ammonia gas via metal oxide-modified graphene-based sensor array combining with neural network model

Abstract: This paper reports metal oxide (MOx)-decorated graphene-based sensor array combining with back-propagation (BP) neural network toward the detection of indoor air pollutant exposure. Tin dioxide (SnO 2 ) nanospheres and copper oxide (CuO) nanoflowers-decorated graphene were used as candidates for formaldehyde and ammonia gas sensing, respectively. The as-synthesized sensing materials were characterized in terms of their nanostructural, morphological and compositional features by SEM, Raman spectra, and XRD. The sensor array was fabricated via one-step hydrothermal route and layer-by-layer (LbL) self-assembly technique on the substrate with interdigital microelectrodes. The sensing properties of MOx/graphene composite toward the mixture gas of ammonia and formaldehyde, such as dynamic response, sensitivity, response/recovery time, and stability, were investigated at room temperature. And furthermore, this work successfully achieved the recognition and quantitative prediction of components in the gas mixture of formaldehyde and ammonia through the combination of MOx/graphene-based sensor array and neural network-based signal processing technologies.

Topological liquid diode

Abstract: The last two decades have witnessed an explosion of interest in the field of droplet-based microfluidics for their multifarious applications. Despite rapid innovations in strategies to generate small-scale liquid transport on these devices, the speed of motion is usually slow, the transport distance is limited, and the flow direction is not well controlled because of unwanted pinning of contact lines by defects on the surface. We report a new method of microscopic liquid transport based on a unique topological structure. This method breaks the contact line pinning through efficient conversion of excess surface energy to kinetic energy at the advancing edge of the droplet while simultaneously arresting the reverse motion of the droplet via strong pinning. This results in a novel topological fluid diode that allows for a rapid, directional, and long-distance transport of virtually any kind of liquid without the need for an external energy input.

Enhancement of full-spectrum photocatalytic activity over BiPO4/Bi2WO6 composites

Abstract: The full-spectrum photocatalytst is of important value for the practical use, which could absorb natural sunlight for photoctalytic degrading organic pollutants. BiPO4/Bi2WO6 composite photocatalysts were prepared via ultrasonic-calcination method and had superior photocatalytic performance for degrading different kinds of organic pollutants under simulant sunlight irradiation. The apparent rate constant of 5.0%BiPO4/Bi2WO6 on the degradation of methylene blue (MB) is 0.0305 min−1, which is about 25.4 and 3.2 times of pure BiPO4 and Bi2WO6 respectively. In the BiPO4/Bi2WO6 composite photocatalysts, the core-hole structure of BiPO4 as core and Bi2WO6 as hole was formed. During the photocatalytic process of BiPO4/Bi2WO6 composites under simulant sunlight irradiation, the photo-generated electrons of BiPO4 would inject to the conduction band of Bi2WO6, and the photo-generated holes on Bi2WO6 could transfer to the valance band of BiPO4, and then an effective charges separation was achieved. The interaction of BiPO4 and Bi2WO6 not only expanded the range of absorption spectrum but also enhanced the separation efficiency of photo-generated charges, and further improved the photocatalytic performance.

Layer-by-Layer Self-assembly of Co3O4 Nanorod-Decorated MoS2 Nanosheet-Based Nanocomposite toward High-Performance Ammonia Detection.

Abstract: This article is the first demonstration of a molybdenum disulfide (MoS2)/tricobalt tetraoxide (Co3O4) nanocomposite film sensor toward NH3 detection. The MoS2/Co3O4 film sensor was fabricated on a substrate with interdigital electrodes via layer-by-layer self-assembly route. The surface morphology, nanostructure, and elemental composition of the MoS2/Co3O4 samples were examined by scanning electron microscopy, transmission electron microscopy, X-ray diffraction, energy-dispersive spectrometry, and X-ray photoelectron spectroscopy. The characterization results confirmed its successful preparation and rationality. The NH3 sensing properties of the sensor for ultra-low-concentration detection were investigated at room temperature. The experimental results revealed that high sensitivity, good repeatability, stability, and selectivity and fast response/recovery characteristics were achieved by the sensor toward NH3. Moreover, the MoS2/Co3O4 nanocomposite film sensor exhibited significant enhancement in ammonia...

Text feature extraction based on deep learning: a review

Abstract: Selection of text feature item is a basic and important matter for text mining and information retrieval. Traditional methods of feature extraction require handcrafted features. To hand-design, an effective feature is a lengthy process, but aiming at new applications, deep learning enables to acquire new effective feature representation from training data. As a new feature extraction method, deep learning has made achievements in text mining. The major difference between deep learning and conventional methods is that deep learning automatically learns features from big data, instead of adopting handcrafted features, which mainly depends on priori knowledge of designers and is highly impossible to take the advantage of big data. Deep learning can automatically learn feature representation from big data, including millions of parameters. This thesis outlines the common methods used in text feature extraction first, and then expands frequently used deep learning methods in text feature extraction and its applications, and forecasts the application of deep learning in feature extraction.

Polymer Gel Systems for Water Management in High-Temperature Petroleum Reservoirs: A Chemical Review

Abstract: Polymer gel systems as water management materials have been widely used in recent years for enhanced oil recovery applications. However, most polymer gel systems are limited in their ability to withstand the harsh environments of high temperature and high salinity. Those polymer gel systems that can handle high-temperature excessive water treatments are reviewed in this paper and categorized into three major types: in situ cross-linked polymer gels, preformed gels, and foamed gels. Future directions for the development of polymer gel systems for high-temperature conditions are recommended. For excessive water management with temperatures from 80 to 120 °C, current polymer systems are substantially adequate. Polymer gel systems composed of partially hydrolyzed polyacrylamide (HPAM)/chromium can be combined with nanoparticle technology to elongate their gelation time and reduce the adsorption of chromium ions in the formation. Phenolic resin cross-linker systems have reasonable gelation times and gel streng...

Molecular Insights into the Transformation of Dissolved Organic Matter in Landfill Leachate Concentrate during Biodegradation and Coagulation Processes Using ESI FT-ICR MS.

Abstract: Landfill leachate concentrate is a type of refractory organic wastewater with high environmental risk. Identification of refractory components and insights into the molecular transformations of the organics are essential for the development of efficient treatment process. In this report, molecular compositions of dissolved organic matter (DOM) in leachate concentrate, as well as changes after anaerobic/aerobic biodegradation and coagulation with salts, were characterized using electrospray ionization (ESI) coupled with Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR MS). DOM in leachate concentrate were more saturated and less oxidized with more nitrogen and sulfur-containing substances (accounting for 50.0%), comparing with natural organic matter in Suwannee River. Selectivity for different classes of organics during biodegradation and coagulation processes was observed. Substances with low oxidation degree (O/C < 0.3) were more reactive during biodegradation process, leading to the f...

A Dynamic-Bayesian-Network-Based Fault Diagnosis Methodology Considering Transient and Intermittent Faults

Abstract: Transient fault (TF) and intermittent fault (IF) of complex electronic systems are difficult to diagnose. As the performance of electronic products degrades over time, the results of fault diagnosis could be different at different times for the given identical fault symptoms. A dynamic Bayesian network (DBN)-based fault diagnosis methodology in the presence of TF and IF for electronic systems is proposed. DBNs are used to model the dynamic degradation process of electronic products, and Markov chains are used to model the transition relationships of four states, i.e., no fault, TF, IF, and permanent fault. Our fault diagnosis methodology can identify the faulty components and distinguish the fault types. Four fault diagnosis cases of the Genius modular redundancy control system are investigated to demonstrate the application of this methodology.