Theoretical Investigation of Catalytic Methane Cracking and Carbon Nanotube Growth

Methane cracking on transition metal surfaces is a catalytically important reaction. It is a key step to produce hydrogen and carbonaceous nanomaterials, such as carbon nanotubes (CNT) or carbon nanofibers (CNF), which display unique mechanical and electrical properties, and have been widely used as electronic components, polymer additives, gas storage, and catalyst support materials. Although the catalytic methane cracking and CNT/CNF growth have drawn lots of attentions, the understanding of the catalytic methane cracking properties and CNT/CNF growth mechanism is still limited. To develop a better understanding of the catalytic methane cracking and the CNT/CNF growth process, the activation of the C−H bond of methane and the creation C−C bonds on transition metal catalysts, especially Ni, have been studied at atomic level using Density Functional Theory (DFT). Ni is of particular interest because, among the different metals commonly used in the methane cracking and CNT/CNF production, Ni-based catalysts show very good catalytic activity at relatively moderate temperatures. In this research, factors that affect the methane dissociation properties, e.g. effects of the catalyst structure, carbon deposition, oxide support and alloying, were analyzed using DFT calculations. The study of the Ni catalyst surface topology effect on methane dehydrogenation properties was conducted on various Ni catalyst surfaces, i.e., Ni (100), Ni (111) and Ni (553). The transition states for methane sequential dehydrogenations on the three surfaces were identified. The results show that the adsorption of CHx (x=1-3) and H species is favoured on less packed surfaces, e.g., Ni (100) and Ni (553). Moreover, it was found that the Ni (553) and Ni (100) promote the dissociation of CHx species by lowering the activation barriers when compared to Ni (111).The above study was conducted on clean Ni catalyst surfaces. During the reactions, however, there will be carbon atoms deposited on the Ni surface. To provide a more realistic modeling of the reaction, the study of Ni catalytic methane cracking is then further extended by taking into account the effects of carbon atoms depositions. Methane dissociation on clean, surface-carbon, and subsurface-carbon-covered Ni (111) surfaces were investigated. The results show that the existence of surface and subsurface C atoms destabilized the adsorption of the surface hydrocarbon species when compared to the clean Ni (111) surface. Moreover, it was found that the presence of carbon atoms increase the CHx (x=4-1) species activation barriers especially on the surface–carbon-covered (1/4 ML) Ni (111) surface, where CHx (x=4-1) species encounter the highest energy barriers for dissociation due to the electronic deactivation induced by C−Ni bonding. The calculations also show that CHx dissociation barriers are not affected by neighboring C atoms at low surface carbon coverage (1/9 ML). The DFT study of Ni catalytic methane dissociation, so far, only focuses on Ni catalyst surface. However, in the actual process, the Ni catalyst is usually deposited on oxide support; little is known about the effect of the support, especially the metal-support interface, on the dissociation properties of methane. Therefore, the dissociations of methane and hydrogen on Ni cluster supported on γ-Al2O3 support were investigated using DFT calculations. Two systems: Ni4 cluster supported on the spinel model of γ-Al2O3 (100) surface, S(Ni4), and on the non-spinel model of γ-Al2O3 (100) surface, NS(Ni4), have been used to model Ni4/γ-Al2O3. For both models, it was found that CH4 and H2 dissociations are kinetically preferred at the particular Ni atoms located at the nickel-alumina interface when compared with the top of the Ni cluster. Also, the study of CH3 and H adsorption on different sites of the S(Ni4) and NS(Ni4) show that CH3 and H bonded with the Ni atom at Ni4/γ-Al2O3 interface are more stable than at the top site adsorption. Hirshfeld charge analysis showed that the surface Al atom works primarily as a charge donation partner when CH3 and H are bonded with the Ni atom at the interface. This also resulted in an up shift of the d-orbital around the Fermi energy, which finally stabilized the interface adsorption by this Al (donor)–Ni–adsorbates (acceptor) effect. The results obtained in the present analysis indicate that the metal-oxide interface plays an essential role in the dissociation of methane and hydrogen. During the methane cracking process, carbon is deposited on the catalyst. Part of these carbon atoms will exists in the form of CNT, and some of them is deposited as encapsulating carbon in the form of graphene, which causes catalyst deactivation. To understand the role of metal elements in the growth of CNT or graphene, some crucial processes occurs on the (111) surface of different transition metals, i.e., Fe, Co, Ni, and Cu was analyzed using DFT. These processes consist of methane cracking to produce C, C atoms surface diffusion and C nucleation reactions. This study showed that Ni-based catalyst is a suitable substrate for growing CNT: it has a moderate reactivity towards C−H bond activation; low energy barrier for C atom surface diffusion, and a relatively high nucleation barrier for the surface C atoms. Meanwhile, this study also showed that Cu may be a suitable catalyst for synthesis of graphene due to the low diffusion and nucleation barriers of C adatoms on Cu. One key limitation of Cu is the low reactivity of this metal towards methane dissociation, which dominates the growth rate and reaction conditions of the process. Since Fe and Ni were found more reactive towards C−H bond breaking reactions, the results from this study indicate that Cu based alloys, e.g. Cu8Ni, may be a suitable catalyst for the mass production of graphene. To further extend the understanding regarding the behavior of the carbon atoms during the Ni catalyst CNT growth, the structure, nucleation energetics, and mobility of carbon intermediates up to 6 atoms on the Ni (111) surface were investigated. This study showed that carbon clusters were more thermodynamically stable than adsorbed atomic carbon, with linear carbon structures being more stable than branched and ring structures. The results also showed that carbon chains have higher mobility than branched configurations. The transition states and energybarriers for the formation of different carbon clusters were also studied. The results suggest that the formation of the branched carbon configurations is kinetically favored as it presents lower energy barriers than those obtained for carbon chains. Furthermore, based on the above DFT calculations results, a Ni catalytic CNT growth mechanism based on carbon species surface diffusion was developed. A multi-scale modeling approach that integrates DFT calculations and kinetic Monte Carlo (KMC) simulation was developed, in which the energetic results obtained from DFT calculations were used to set-up the kinetic database for the KMC simulation. The KMC simulations explicitly follow the elementary steps involved in the CNT growth that include CH4 dissociation, C surface and bulk diffusion, C nucleation, C3 trimer diffusion and C and C3 incorporation into CNT wall. The KMC simulations show that CNT growth is dominated by the C surface diffusion. Moreover, it was found that the surface diffusion of the small C cluster, e.g., C3 trimer, is also a critical step in the growth mechanism of the CNT. It prevents fast nucleation of the C atoms on the catalyst surface, and therefore inhibits the deactivation of the catalyst. The CNT growth rates predicted by KMC simulations fit well with the experimental data, verifying the proposed CNT growth mechanism. This study will therefore provide insight regarding the mechanism and kinetic properties of Ni catalytic methane cracking and CNT growth process. In summary, a systematic theoretical investigation of the catalytic methane cracking and CNT growth process was performed in this study. It was found the catalyst structure, carbon deposition, and the γ-Al2O3 support has significant effect on the CHx dissociation properties. Moreover, DFT analysis also shows that the reactivity of the catalyst towards C−H bond activation and CNT or graphene growth varies with different transition metals. Finally, based on the DFT study of the carbon cluster nucleation, a CNT growth model that accounts for carbon cluster diffusion and nucleation was proposed. Using the kinetic parameters that obtained by the DFT calculations, a KMC simulation was developed. By comparing the CNT growth rate predicted by the KMC simulations with the experimental data, the proposed CNT growth mechanism is validated

Experimental and computational Fluid Dynamics study of separation gap effect on gas explosion mitigation for methane storage tanks

This paper presented both experimental and numerical assessments of separation gap effect on vented explosion pressure in and around the area of a tank group. A series of vented gas explosion layouts with different separation gaps between tanks were experimentally investigated. In order to qualitatively determine the relationship between the separation gap distance and explosion pressure, intensive computational Fluid Dynamics (CFD) simulations, verified with testing data, were conducted. Good agreement between CFD simulation results and experimental data was achieved. By using CFD simulation, more gas explosion cases were included to consider different gas cloud coverage scenarios. Separation gap effects on internal and external pressures at various locations were investigated

High strength mullite-bond SiC porous ceramics fabricated by digital light processing

Fabricating SiC ceramics via the digital light processing (DLP) technology is of great challenge due to strong light absorption and high refractive index of deep-colored SiC powders, which highly differ from those of resin, and thus significantly affect the curing performance of the photosensitive SiC slurry. In this paper, a thin silicon oxide (SiO2) layer was in-situ formed on the surface of SiC powders by pre-oxidation treatment. This method was proven to effectively improve the curing ability of SiC slurry. The SiC photosensitive slurry was fabricated with solid content of 55 vol% and viscosity of 7.77 Pa s (shear rate of 30 s-1). The curing thickness was 50 μm with exposure time of only 5 s. Then, a well-designed sintering additive was added to completely convert low-strength SiO2 into mullite reinforcement during sintering. Complexshaped mullite-bond SiC ceramics were successfully fabricated. The flexural strength of SiC ceramics sintered at 1550 °C in air reached 97.6 MPa with porosity of 39.2 vol%, as high as those prepared by spark plasma sintering (SPS) techniques.</p

A Multi-scale model for CO2 capture: A Nickel-based oxygen carrier in Chemical-looping Combustion

In this work, we present a multi-scale modelling framework for the Ni-based oxygen carrier (OC) particle that can explicitly account for the complex reaction mechanism taking place on the contacting surface between gas and solid reactants in Chemical Looping Combustion (CLC). This multi-scale framework consists of a gas diffusion model and a surface reaction model. Continuum equations are used to describe the gas diffusion inside OC particles, whereas Mean-field approximation method is adopted to simulate the micro-scale events, such as molecule adsorption and elementary reaction, occurring on the contacting surface. A pure CO stream is employed as the fuel gas whereas the NiO is used as the metal oxide because it is one of the mostly used material in laboratory and pilot-scale plants. Rate constants for the micro-scale events considered in the present work were obtained from a systematic Density Functional Theory (DFT) analysis, which provides a reasonable elementary reaction kinetics and lays a solid foundation for multi-scale calculations. A sensitivity analysis on the size of intra-particle pore and the adsoprtion rate constant was conducted to assess the mass transport effects on the porous particle. The proposed multi-scale model shows reasonable tendencies and responses to changes in key modelling parameters

A New Adversarial Domain Generalization Network Based on Class Boundary Feature Detection for Bearing Fault Diagnosis

In recent years, many researchers have attempted to achieve cross-domain diagnosis of faults through domain adaptation (DA) methods. However, owing to the complex physical environments, applications of DA-based approach are not guaranteed to unknown operating environments. Some existing domain generalization (DG) methods require enough fully labeled source domains to train, which are often unavailable in practical settings. In this study, an adversarial domain generalization network (ADGN) based on class boundary feature detection is proposed. The ADGN can diagnose faults in unknown operating environments, and only one fully labeled domain is used in training. Although ADGN has to access fully unlabeled auxiliary domains, a large number of unlabeled samples exist under actual working conditions. In our method, fuzzy features at a classification boundary are detected by maximizing the classifier differences. Better feature mapping functions and domain-invariant features are obtained by adversarial training. As the training proceeds, the differences in the distribution of features among the source, auxiliary, and unknown domains become smaller so domain-invariant features can be used for fault diagnosis in unknown operating environments. Comprehensive experiments showed that ADGN can achieve higher fault diagnosis accuracies than other methods when only one fully labeled domain is used in an unknown operating environment. The ADGN can even cope comfortably with complex transfer tasks with different speeds and loads

Global Analysis of DNA Methylation by Methyl-Capture Sequencing Reveals Epigenetic Control of Cisplatin Resistance in Ovarian Cancer Cell

Cisplatin resistance is one of the major reasons leading to the high death rate of ovarian cancer. Methyl-Capture sequencing (MethylCap-seq), which combines precipitation of methylated DNA by recombinant methyl-CpG binding domain of MBD2 protein with NGS, global and unbiased analysis of global DNA methylation patterns. We applied MethylCap-seq to analyze genome-wide DNA methylation profile of cisplatin sensitive ovarian cancer cell line A2780 and its isogenic derivative resistant line A2780CP. We obtained 21,763,035 raw reads for the drug resistant cell line A2780CP and 18,821,061reads for the sensitive cell line A2780. We identified 1224 hyper-methylated and 1216 hypomethylated DMRs (differentially methylated region) in A2780CP compared to A2780. Our MethylCap-seq data on this ovarian cancer cisplatin resistant model provided a good resource for the research community. We also found that A2780CP, compared to A2780, has lower observed to expected methylated CpG ratios, suggesting a lower global CpG methylation in A2780CP cells. Methylation specific PCR and bisulfite sequencing confirmed hypermethylation of PTK6, PRKCE and BCL2L1 in A2780 compared with A2780CP. Furthermore, treatment with the demethylation reagent 5-aza-dC in A2780 cells demethylated the promoters and restored the expression of PTK6, PRKCE and BCL2L1

The DNA Methylome of Human Peripheral Blood Mononuclear Cells

Analysis across the genome of patterns of DNA methylation reveals a rich landscape of allele-specific epigenetic modification and consequent effects on allele-specific gene expression

Grid-based risk mapping for gas explosion accidents by using Bayesian network method

Gas explosions at process facilities close to residential areas may lead to catastrophic consequences. It is difficult for traditional quantitative risk analysis (QRA) to consider all the specific local details and conduct risk assessments efficiently. A grid-based risk mapping method is developed to enable a more detailed and reliable explosion risk screening for large areas under complicated circumstance. A target area is divided into a number of grids of an appropriate size and with simplified conditions, and risk analysis is conducted at each grid. A total risk mapping can be depicted based on risk evaluations of all grids. Meanwhile, in order to consider multi-consequences and the complex inter-relationships between consequences and basic factors, a Bayesian network (BN) model is implemented for the proposed method instead of conventional Event Tree and Fault Tree methods. Furthermore, three kinds of data—practical information, computational simulations, and subjective judgments—are involved in the quantification of the proposed BN in order to reduce the uncertainties caused by data shortage and improve the reliability and accuracy of the proposed method. A case study is provided and a mesh convergence of different grid sizes is conducted. Results show that the proposed method is capable of dealing with large and complex situations effectively

Risk Analysis of Vapour Cloud Explosions for Oil and Gas Facilities

This book focuses on describing and applying risk analysis of vapour cloud explosions (VCEs) in various oil and gas facilities, such as petrol stations, processing plants, and offshore platforms.This book focuses on describing and applying risk analysis of vapour cloud explosions (VCEs) in various oil and gas facilities, such as petrol stations, processing plants, and offshore platforms. Discussing most of the complicated features of gas explosion accidents, the book studies in detail the gas explosion risk analysis approaches of different oil and gas facilities in order to develop more accurate, detailed, efficient and reliable risk analysis methods for VCEs under different conditions. Moreover, it introduces an advanced overpressure approach to predict VCEs using computational fluid dynamics (CFD) modelling, and details applications of CFD using a FLame ACceleration Simulator (FLACS). The book is intended for researchers and organisations engaged in risk and safety assessments of VCEs in the oil and gas industry

Multi-level explosion risk analysis (MLERA) for accidental gas explosion events in super-large FLNG facilities

© 2016 Elsevier Ltd When assessing explosion risks of super-large offshore structures such as Floating Liquefied Natural Gas (FLNG) facilities, there are neither design rules nor industry standards available as FLNG is a new technology. Meanwhile, a large amount of Computational Fluid Dynamic (CFD) calculation time is required due to its super-large size and highly complicated topside structures. A multi-level explosion risk analysis method (MLERA) is developed in this paper, which divides the whole structure into subsections and applies detailed CFD calculations only to the areas with the highest level of potential risks so that the computational time can be reduced to a realistic and acceptable level. The MLERA includes three levels, which are qualitative risk screening, semi-quantitative risk classification, and quantitative risk assessment. A CFD tool called FLACS is used as a calculation tool for detailed risk quantification, and an ALARP (as low as reasonably practical) method is selected as a calibration tool and used to determine the acceptance of the explosion risk. Meanwhile, since the current design standards for normal offshore platforms are not sufficient for super-large structures, during the risk screening and risk classification processes, safety barriers are used as extra risk indicators in addition to the traditional ones. A case study is conducted based on a cylindrical FLNG model, and the result of the case study proves that the proposed MLERA method is able to save a large amount of computational time