Regulatable Orthotropic 3D Hybrid Continuous Carbon Networks for Efficient Bi-Directional Thermal Conduction

Vertically oriented carbon structures constructed from low-dimen-sional carbon materials are ideal frameworks for high-performance thermal inter-face materials(TIMs).However,improving the interfacial heat-transfer efficiency of vertically oriented carbon structures is a challenging task.Herein,an orthotropic three-dimensional(3D)hybrid carbon network(VSCG)is fabricated by depositing vertically aligned carbon nanotubes(VACNTs)on the surface of a horizontally oriented graphene film(HOGF).The interfacial interaction between the VACNTs and HOGF is then optimized through an annealing strategy.After regulating the orientation structure of the VACNTs and filling the VSCG with polydimethylsi-loxane(PDMS),VSCG/PDMS composites with excellent 3D thermal conductive properties are obtained.The highest in-plane and through-plane thermal conduc-tivities of the composites are 113.61 and 24.37 W m^(-1)K^(-1),respectively.The high contact area of HOGF and good compressibility of VACNTs imbue the VSCG/PDMS composite with low thermal resistance.In addition,the interfacial heat-transfer efficiency of VSCG/PDMS composite in the TIM performance was improved by 71.3%compared to that of a state-of-the-art thermal pad.This new structural design can potentially realize high-performance TIMs that meet the need for high thermal conductivity and low contact thermal resistance in interfacial heat-transfer processes.

Biomass-Derived Nitrogen and Sulfur Co-Doped 3D Carbon Networks with Interconnected Meso-Microporous Structure for High-Performance Supercapacitors

Three-dimensional(3D)carbon networks have been explored as promising capacitive materials thanks to their unique structural features such as large ion-accessible surface area and interconnected porous networks,thus enhancing both ions and electrons transport.Here,sustainable bacterial cellulose(BC)is used both precursor and template for facile synthesis of free-standing N,S-codoped 3Dcarbon networks(a-NSC)by the pyrolysis and activation of polyrhodanine coated BC.The synthesized a-NSC shows highly conductive interconnected porous networks(24S·cm^(-1)),large surface area(1 420m^2·g^(-1))with hierarchical meso-microporosity,and high-level heteroatoms codoping(N:3.1%in atom,S:3.2%in atom).Benefitting from these,a-NSC as binder-free electrode exhibits an ultrahigh specific capacitance of 340F·g^(-1)(24μF·cm^(-2))at the current density of 0.5A·g^(-1)in 6MKOH electrolyte,high-rate capability(71%at 20A·g^(-1))and excellent cycle stability.Furthermore,the assembled symmetrical supercapacitor displays a much short time constant of 0.35sin 1MTEABF4/AN electrolyte,obtaining a maximum energy density of 32.1W·h·kg^(-1 )at power density of 637W·kg^(-1).The in situ multi-heteroatoms doping enables biocellulose-derived carbon networks to exploit its full potentials in energy storage applications,which can be extended to other dimensional carbon nanostructures.

Self-assembled three-dimensional carbon networks with accessorial Lewis base sites and variational electron characteristics as efficient oxygen reduction reaction catalysts for alkaline metal-air batteries

Heteroatom-doped carbon has been demonstrated to be one of the most promising non-noble metal catalysts with high catalytic activity and stability through the modification of the electronic and geometric structures.In this study,we develop a novel solvent method to prepare interconnected N,S co-doped three-dimensional（3D）carbon networks with tunable nanopores derived from an asso-ciated complex based on melamine and sodium dodecylbenzene sulfonate（SDBS）.After the intro-duction of silica templates and calcination,the catalyst exhibits 3D networks with interconnected 50-nm pores and partial graphitization.With the increase of the number of Lewis base sites caused by the N doping and change of the carbon charge and spin densities caused by the S doping,the designed N,S co-doped catalyst exhibits a similar electrochemical activity to that of the commercial 20-wt%Pt/C as an oxygen reduction reaction catalyst.In addition,in an aluminum-air battery,the proposed catalyst even outperforms the commercial 5-wt%Pt/C catalyst.Both interconnected porous structures and synergistic effects of N and S contribute to the superior catalytic perfor-mance.This study paves the way for the synthesis of various other N-doped and co-doped carbon materials as efficient catalysts in electrochemical energy applications.

Small but mighty:Empowering sodium/potassium-ion battery performance with S-doped SnO_(2) quantum dots embedded in N,S codoped carbon fiber network

SnO_(2) has been extensively investigated as an anode material for sodium-ion batteries(SIBs)and potassium-ion batteries(PIBs)due to its high Na/K storage capacity,high abundance,and low toxicity.However,the sluggish reaction kinetics,low electronic conductivity,and large volume changes during charge and discharge hinder the practical applications of SnO_(2)-based electrodes for SIBs and PIBs.Engineering rational structures with fast charge/ion transfer and robust stability is important to overcoming these challenges.Herein,S-doped SnO_(2)(S-SnO_(2))quantum dots(QDs)(≈3 nm)encapsulated in an N,S codoped carbon fiber networks(S-SnO_(2)-CFN)are rationally fabricated using a sequential freeze-drying,calcination,and S-doping strategy.Experimental analysis and density functional theory calculations reveal that the integration of S-SnO_(2) QDs with N,S codoped carbon fiber network remarkably decreases the adsorption energies of Na/K atoms in the interlayer of SnO_(2)-CFN,and the S doping can increase the conductivity of SnO_(2),thereby enhancing the ion transfer kinetics.The synergistic interaction between S-SnO_(2) QDs and N,S codoped carbon fiber network results in a composite with fast Na+/K+storage and extraordinary long-term cyclability.Specifically,the S-SnO_(2)-CFN delivers high rate capacities of 141.0 mAh g^(−1) at 20 A g^(−1) in SIBs and 102.8 mAh g^(−1) at 10 A g^(−1) in PIBs.Impressively,it delivers ultra-stable sodium storage up to 10,000 cycles at 5 A g^(−1) and potassium storage up to 5000 cycles at 2 A g^(−1).This study provides insights into constructing metal oxide-based carbon fiber network structures for high-performance electrochemical energy storage and conversion devices.

High N-doped hierarchical porous carbon networks with expanded interlayers for efficient sodium storage

Sodium-ion batteries (SIBs) have been attracting considerable attention as a promising candidate for large-scale energy storage because of the abundance and low-cost of sodium resources. However, lack of appropriate anode materials impedes further applications. Herein, a novel self-template strategy is designed to synthesize uniform flowerlike N-doped hierarchical porous carbon networks (NHPCN) with high content of N (15.31 at.%) assembled by ultrathin nanosheets via a self-synthesized single precursor and subsequent thermal annealing. Relying on the synergetic coordination of benzimidazole and 2-methylimidazole with metal ions to produce a flowerlike network, a self-formed single precursor can be harvested. Due to the structural and compositional advantages, including the high N doping, the expanded interlayer spacing, the ultrathin two-dimensional nano-sized subunits, and the three-dimensional porous network structure, these unique NHPCN flowers deliver ultrahigh reversible capacities of 453.7 mAh·g^−1 at 0.1 A·g^−1 and 242.5 mAh·g^−1 at 1 A·g^−1 for 2,500 cycles with exceptional rate capability of 5 A·g^−1 with reversible capacities of 201.2 mAh·g^−1. The greatly improved sodium storage performance of NHPCN confirms the importance of reasonable engineering and synthesis of hierarchical carbon with unique structures.

Biomass-based aligned carbon networks with double-layer construction for tunable electromagnetic shielding with ultra-low reflectivity

Nowadays,carbon frameworks derived from natural biomaterials have attracted extensive attention for electromagnetic interference(EMI)shielding due to their renewability and affordability.However,it is critical and challenging to achieve effective regulation of shielding effectiveness(SE)as well as weaken the strong EM reflection of highly conductive biomass-based carbon materials.Herein,commercial cotton pads with oriented structure were selected as carbonaceous precursor to fabricate aligned carbon networks by pyrolysis,and the EMI SE of the samples with increased temperature of 800-1000℃ can be accurately controlled in the effective range of~21.7-29.1,~27.7-37.1 and~32.7-43.3 d B with high reflection coefficient of>0.8 by changing the cross-angle between the electric-field direction of incident EM waves and the fiber-orientation direction due to the occurrence of opposite internal electric field.Moreover,the further construction of Salisbury absorber-liked double-layer structure could result in an ultralow reflection coefficient of only~0.06 but enhanced SE variation range up to~38.7-49.3 d B during the adjustment of cross-angle,possibly due to the destructive interference of EM waves in the double-layer carbon networks.This work would provide a simple and effective way for constructing high-performance biomass carbon materials with adjustable EMI shielding and ultra-low reflectivity.

Black Phosphorus/Nanocarbons Constructing a Dual-Carbon Conductive Network for High-Performance Sodium-Ion Batteries

Black phosphorus has been recognized as a prospective candidate anode material for sodium-ion batteries(SIBs)due to its ultrahigh theoretical capacity of 2596 mA·h/g and high electric conductivity of≈300 S/m.However,its large volume expansion and contraction during sodiation/desodiation lead to poor cycling stability.In this work,a BP/graphite nanoparticle/nitrogen-doped multiwalled carbon nanotubes(BP/G/CNTs)composite with a dual-carbon conductive network is successfully fabricated as a promising anode material for SIBs through a simple two-step mechanical milling process.The unique structure can mitigate the eff ect of volume changes and provide additional electron conduction pathways during cycles.Furthermore,the formation of P–O–C bonds helps maintain the intimate connection between phosphorus and carbon,thereby improving the cycling and rate performance.As a result,the BP/G/CNTs composite delivers a high initial Coulombic efficiency(89.6%)and a high specific capacity for SIBs(1791.3 mA·h/g after 100 cycles at 519.2 mA/g and 1665.2 mA·h/g after 100 cycles at 1298 mA/g).Based on these results,the integrated strategy of one-and two-dimensional carbon materials can guide other anode materials for SIBs.

Prediction Modeling and Mapping of Soil Carbon Content Using Artificial Neural Network, Hyperspectral Satellite Data and Field Spectroscopy

Soil organic carbon (SOC) is an important and reliable indicator of soil quality. In this study, soil spectra were characterized and analysed to predict the spatial soil organic carbon (SOC) content using multivariate predictive modeling technique-artificial neural network (ANN). EO1-Hyperion (400 - 2500 nm) hyperspectral image, field and laboratory scale data sets (350 - 2500 nm) were generated which consisted of laboratory estimated SOC content of collected soil samples (dependent variable) and their corresponding reflectance data of SOC sensitive spectral bands (predictive variables). For each data set, ANN predictive models were developed and all three datasets (image-scale, field-scale and lab-scale) revealed significant network performances for training, testing and validation indicating a good network generalization for SOC content. ANN based analysis showed high prediction of SOC content at image (R2 = 0.93, and RPD = 3.19), field (R2 = 0.92 and RPD = 3.17), and lab scale (R2 = 0.95 and RPD = 3.16). Validation results of ANN indicated that predictive models performed well (R2 = 0.90) with RMSE 0.070. The result showed that ANN methods had a great potential for estimating and mapping spatial SOC content. The study concluded that ANN model was potential tools in predicting SOC distribution in agricultural field using hyper-spectral remote sensing data at image-scale, field-scale and lab-scale.

Multiscale Theories and Applications:From Microstructure Design to Macroscopic Assessment for Carbon Nanotubes Networks

Carbon nanotube(CNT)networks enable CNTs to be used as building blocks for synthesizing novel advanced materials,thus taking full advantage of the superior properties of individual CNTs.Multiscale analyses have to be adopted to study the load transfer mechanisms of CNT networks from the atomic scale to the macroscopic scale due to the huge computational cost.Among them,fully resolved structural features include the graphitic honeycomb lattice(atomic),inter-tube stacking(nano)and assembly(meso)of CNTs.On an atomic scale,the elastic properties,ultimate stresses,and failure strains of individual CNTs with distinct chiralities and radii are obtained under various loading conditions by molecular mechanics.The dependence of the cohesive energies on spacing distances,crossing angles,size and edge effects between two CNTs is analyzed through continuum modeling in nanoscale.The mesoscale models,which neglect the atomic structures of individual CNTs but retain geometrical information about the shape of CNTs and their assembly into a network,have been developed to study the multi-level mechanism of material deformation and microstructural evolution in CNT networks under stretching,from elastic elongation,strengthening to damage and failure.This paper summarizes the multiscale theories mentioned above,which should provide insight into the optimal assembling of CNT network materials for elevated mechanical performance.

Modeling of thermal conductivity for disordered carbon nanotube networks

Several theoretical models have been developed so far to predict the thermal conductivities of carbon nanotube(CNT)networks.However,these models overestimated the thermal conductivity significantly.In this paper,we claimed that a CNT network can be considered as a contact thermal resistance network.In the contact thermal resistance network,the temperature of an individual CNT is nonuniform and the intrinsic thermal resistance of CNTs can be ignored.Compared with the previous models,the model we proposed agrees well with the experimental results of single-walled CNT networks.

Static dissolution-induced 3D pore network modification and its impact on critical pore attributes of carbonate rocks

To determine the effect of dissolution on pore network development in carbonate rocks, dissolution experiments, X-Ray microtomography, and thin section analysis were conducted on argillaceous limestone and grain limestone samples at different temperatures and constant pH, HCl concentration. The relationship between Ca^(2+) concentration and time was revealed through the experiments; pore size distribution before and after dissolution indicate that there is no correlation between the temperature and pore size variation, but pore size variation in grain limestone is more significant, indicating that the variation is mainly controlled by the heterogeneity of the rock itself(initial porosity and permeability) and the abundance of unstable minerals(related to crystal shape, size and mineral type). At different temperatures, the two kinds of carbonate rocks had very small variation in pore throat radius from 0.003 mm to 0.040 mm, which is 1.3 to 3.5 times more, 1.7 on average of the original pore throat radius. Their pore throat length varied from 0.05 mm to 0.35 mm. The minor changes in the pore throat radius, length and connectivity brought big changes to permeability of up to 1 000×10^(-3) μm^2.

Measurement and correlation study of silymarin solubility in supercritical carbon dioxide with and without a cosolvent using semi-empirical models and back-propagation artificial neural networks

The solubility data of compounds in supercritical fluids and the correlation between the experimental solubility data and predicted solubility data are crucial to the development of supercritical technologies. In the present work, the solubility data of silymarin(SM) in both pure supercritical carbon dioxide(SCCO2) and SCCO2 with added cosolvent was measured at temperatures ranging from 308 to 338 K and pressures from 8 to 22 MPa. The experimental data were fit with three semi-empirical density-based models(Chrastil, Bartle and Mendez-Santiago and Teja models) and a back-propagation artificial neural networks(BPANN) model. Interaction parameters for the models were obtained and the percentage of average absolute relative deviation(AARD%) in each calculation was determined. The correlation results were in good agreement with the experimental data. A comparison among the four models revealed that the experimental solubility data were more fit with the BPANN model with AARDs ranging from 1.14% to 2.15% for silymarin in pure SCCO2 and with added cosolvent. The results provide fundamental data for designing the extraction of SM or the preparation of its particle using SCCO2 techniques.

Solving the Carbon Dioxide Emission Estimation Problem: An Artificial Neural Network Model

Climate Pollution due to the Carbon Emission (CO2) from the different fossil fuels is considered as a great and important international challenge to many researchers. In this paper we are providing a solution to forecast the poison CO2 gas emerged from energy consumption. Four inputs data were considered the global oil, natural gas, coal, and primary energy consumption to build our system. In this paper, we used the Artificial Neural Network (ANN) as successful and powerful tool in handling a time series modeling problem. The proposed ANN model was used to train and test the yearly CO2 Emission. The data were trained from year 1982 to 2000, and tested for the year 2003 to 2010. From the results obtained we can see that ANN performance was Excellent and proved its efficiency as a useful tool in solving the climate pollution problems.

A Novel Hierarchical Porous 3D Structured Vanadium Nitride/Carbon Membranes for High-performance Supercapacitor Negative Electrodes

Transition-metal nitrides exhibit wide potential windows and good electrochemical performance, but usually experience imbalanced practical applications in the energy storage field due to aggregation, poor circulation stability, and complicated syntheses. In this study, a novel and simple multiphase polymeric strategy was developed to fabricate hybrid vanadium nitride/carbon(VN/C) membranes for supercapacitor negative electrodes, in which VN nanoparticles were uniformly distributed in the hierarchical porous carbon 3D networks. The supercapacitor negative electrode based on VN/C membranes exhibited a high specific capacitance of 392.0 F g^(-1) at 0.5 A g^(-1) and an excellent rate capability with capacitance retention of 50.5% at 30 A g^(-1). For the asymmetric device fabricated using Ni(OH)_2//VN/C membranes, a high energy density of 43.0 Wh kg^(-1) at a power density of800 W kg^(-1) was observed. Moreover, the device also showed good cycling stability of 82.9% at a current density of 1.0 A g^(-1) after 8000 cycles. This work may throw a light on simply the fabrication of other high-performance transition-metal nitridebased supercapacitor or other energy storage devices.

Application of Statistical Methods to Assess Carbon Monoxide Pollution Variations within an Urban Area

In recent years there have been considerable new legislation and efforts by vehicle manufactures aimed at reducing pollutant emission to improve air quality in urban areas. Carbon monoxide is a major pollutant in urban areas, and in this study we analyze monthly carbon monoxide (CO) data from Valencia City, a representative Mediterranean city in terms of its structure and climatology. Temporal and spatial trends in pollution were recorded from a monitoring net- work that consisted of five monitoring sites. A multiple linear model, incorporating meteorological parameters, annual cycles, and random error due to serial correlation, was used to estimate the temporal changes in pollution. An analysis performed on the meteorologically adjusted data reveals a significant decreasing trend in CO concentrations and an annual seasonal cycle. The model parameters are estimated by applying the least-squares method. The standard error of the parameters is determined while taking into account the serial correlation in the residuals. The decreasing trend im- plies to a certain extent an improvement in the air quality of the study area. The seasonal cycle shows variations that are mainly associated with traffic and meteorological patterns. Analysis of the stochastic spatial component shows that most of the intersite covariances can be analyzed using an exponential variogram model.

NETWORK ENCAPSULATION OF NANOMETER CaCO_3

The encapsulation of stearic acid coated nanometer CaCO3 by a polystyrene (PS) network via emulsion polymerization is described, where gamma-methacryloxypropyltrimethoxysilane (MPS) was used as an efficient crosslinker. The important factors such as the type and amount of surfactant and initiator and the content of CaCO3 are investigated as well as the role of MPS. It has been shown that little PS was extractable with only 0.6 wt% of MPS (relative to styrene). The cationic surfactant cetyl trimethylammonium bromide (CTAB) proved more effective than the anionic surfactant sodium dodecyl sulfonate (SDS). The yield rises, particles become smaller and size distribution broadens with increased amount of CTAB. It is also found that either 2,2'-azobis(isobutyronitrile) (AIBN) or ammonium persulfate (APS) is suitable for attaining high monomer conversion. With increased amount of CaCO3, the encapsulation ratio can be varied from 17.9 to 3.6, while monomer conversion and yield decrease slightly. FT-IR spectra of the products after extraction indicate tight encapsulation between PS and CaCO3, and TEM photographs of composite particles with well-defined core-shell structure give direct evidence of encapsulation.

Anaerobic tapered fluidized bed reactor for starch wastewater treatment and modeling using multilayer perceptron neural network

treatability of synthetic sago wastewater was investigated in a laboratory anaerobic tapered fluidized bed reactor （ATFBR） with a mesoporous granular activated carbon （GAC） as a support material. The experimental protocol was defined to examine the effect of the maximum organic loading rate （OLR）, hydraulic retention time （HRT）, the efficiency of the reactor and to report on its steady- state performance. The reactor was subjected to a steady-state operation over a range of OLR up to 85.44 kg COD/（m^3·d）. The COD removal efficiency was found to be 92% in the reactor while the biogas produced in the digester reached 25.38 m^3/（m^3·d） of the reactor. With the increase of OLR from 83.7 kg COD/（m^3·d）, the COD removal efficiency decreased. Also an artificial neural network （ANN） model using multilayer perceptron （MLP） has been developed for a system of two input variable and five output dependent variables. For the training of the input-output data, the experimental values obtained have been used. The output parameters predicted have been found to be much closer to the corresponding experimental ones and the model was validated for 30% of the untrained data. The mean square error （MSE） was found to be only 0.0146.

Study of Dibenzothiophene Adsorption Over Carbon Nanotube Supported CoMo HDS Catalysts

Adsorption properties of dibenzothiophene (DBT) on a CNT (carbon nanotube) support as well as on CoMoS/CNT and CoMoO/CNT catalysts have been studied. Consecutive desorption of adsorbates was measured by TGA. The commonly used carriers AC (activated carbon), γ-Al_2O_3, and their supported catalysts (CoMoO/AC, CoMoS/AC, CoMoO/γ-Al_2O_3, CoMoS/γ-Al_2O_3)were also subjected to analysis for comparison. The acidic properties of the samples were characterized using the NH_3-TPD technique. Correlation between the adsorption of DBT and the acidic properties of the catalysts has been established. It was found that the Co-Mo catalysts in the sulfide state adsorbed much more DBT molecules than the corresponding Co-Mo catalysts in the oxide state. The CoMoS/CNT catalyst exhibited very high HDS activity and selectivity, as compared with the CoMoS/γ-Al)_2O_3 catalysts. Based on the BET data and the high hydrogenolysis/hydrogenation selectivity of the CoMoS/CNT, it was deduced that more than 90% of the DBT molecules adsorbed on the CoMoS/CNT with an end-on mode, and the surface of the CoMoS/CNT catalyst was almost fully covered with DBT molecules. Although the AC support had very high surface area and high loading ability, the AC supported CoMoS catalyst showed lower HDS activity, as compared with the CoMoS/γ-Al_2O_3 catalyst.

基于BP神经网络的分层相控碳酸盐岩储层渗透率预测方法

碳酸盐岩储层受成岩作用影响大,孔隙-喉道结构复杂,孔隙度—渗透率相关性较低,渗透率预测难度较大,常规以线性关系为主的预测方法结果不理想。提出了基于BP神经网络的储层渗透率综合预测方法,可以有效预测碳酸盐岩储层渗透率。方法主要分3步。首先对岩心数据和测井数据进行质量控制;然后结合地质特征,优选预测测井曲线参数和神经网络模型的参数,建立预测模型;最后综合多来源资料,进行预测结果质量控制。将该方法应用于中东地区碳酸盐岩A油藏,渗透率预测结果较好。碳酸盐岩储集空间复杂,孔、洞、缝均发育,岩心塞的渗透率测量只能代表局部位置,而试井资料的动态有效渗透率测量范围较大,可以体现储集空间特征,加之储层黏土矿物含量低,不存在储层敏感性问题和各向异性较弱等因素,最终导致试井动态渗透率数值一般高于岩心渗透率。

Types of Karst-fractured and Porous Reservoirs in China's Carbonates and the Nature of the Tahe Oilfield in the Tarim Basin

Almost all the oil and gas reservoirs developed in marine sedimentary strata of China have undergone processes of multi-phase reservoir formation and later modification. The irregular reservoirs are classified into three types as the Naxi, Tahe and Renqiu ones, increasing successively in the development degree of karstificated pores and fissures and the connection degree of independent reservoirs. In these reservoirs, the unity in the fluid feature, pressure and oil-gas-water interface also increases successively from the Naxi to the Renqiu type. The main body of Ordovician reservoirs of the Tahe Oilfield in the Tarim Basin is a network pool rather than a stratified, massive, stratigraphically-unconformed or weathering-crust one. The fluid nature of oil, gas and water, the interface positions and the pressures, as well as the dynamic conditions of fluids within the reservoirs during the production are all different from those in stratified or massive oil and gas reservoirs. Carbonates in the Akekule uplift and the Tahe Oilfield are assemblages of various types of reservoirs, which have an overall oil-bearing potential and obvious uneven distribution. Testing and producing tests are the major means to evaluate this type of reservoirs and acid fracturing improvement is a key link in petroleum exploration and development.