The effect of the carbon components on the performance of carbonbased transition metal electrocatalysts for the hydrogen evolution reaction

The hydrogen evolution reaction(HER)is a promising way to produce hydrogen,and the use of non-precious metals with an excellent electrochemical performance is vital for this.Carbon-based transition metal catalysts have high activity and stability,which are important in reducing the cost of hydrogen production and promoting the development of the hydrogen production industry.However,there is a lack of discussion regarding the effect of carbon components on the performance of these electrocatalysts.This review of the literature discusses the choice of the carbon components in these catalysts and their impact on catalytic performance,including electronic structure control by heteroatom doping,morphology adjustment,and the influence of self-supporting materials.It not only analyzes the progress in HER,but also provides guidance for synthesizing high-performance carbon-based transition metal catalysts.

Improved random noise attenuation using f-x empirical mode decomposition and local similarity

Conventional f-x empirical mode decomposition（EMD） is an effective random noise attenuation method for use with seismic profiles mainly containing horizontal events.However,when a seismic event is not horizontal,the use of f-x EMD is harmful to most useful signals.Based on the framework of f-x EMD,this study proposes an improved denoising approach that retrieves lost useful signals by detecting effective signal points in a noise section using local similarity and then designing a weighting operator for retrieving signals.Compared with conventional f-x EMD,f-x predictive filtering,and f-x empirical mode decomposition predictive filtering,the new approach can preserve more useful signals and obtain a relatively cleaner denoised image.Synthetic and field data examples are shown as test performances of the proposed approach,thereby verifying the effectiveness of this method.

Structure, synthesis, and catalytic properties of nanosize cerium-zirconium-based solid solutions in environmental catalysis

Nanosize cerium-zirconium solid solution(CZO)with a special fluorite structure has received an increasing research interest due to their remarkable advantages such as excellent oxygen storage capacity and great flexibility in their composition and structure.By partial metal(including rare earth,transition,alkaline earth or other metal)doping into CZO,the physicochemical properties of these catalytic materials can be controllable adjusted for the study of specific reactions.To date,nanosize CZO has been prepared by co-precipitation,sol-gel,surfactant-assisted approach,solution combustion,micro-emulsion,high energy mechanical milling,etc.The advent of these methodologies has prompted researchers to construct well-defined networks with customized micromorphology and functionalities.In this review,we describe not only the basic structure and synthetic strategies of CZO,but also their relevant applications in environmental catalysis,such as the purification for CO,nitrogen oxides(NOx),volatile organic compounds(VOC),soot,hydrocarbon(HC),CO2 and solid particulate matters(PM),and some reaction mechanisms are also summarized.

Three-dimensional ordered macroporous perovskite-type La_(1-x)K_xNiO_3 catalysts with enhanced catalytic activity for soot combustion: the Effect of K-substitution

Three-dimensional ordered macroporous (3DOM) La1?xKxNiO3 perovskite-type catalysts were successfully prepared by a colloidal crystal template method and characterized by scanning electron microscopy, transmission electron microscopy, high-resolution transmission electron microscopy, energy-dispersive X-ray scattering elemental mapping, X-ray diffraction, Raman and X-ray photoelectron spectroscopy, and temperature-programmed reduction of H2. Further, their catalytic activity in soot combustion was determined by temperature-programmed oxidation reaction. K substitution into the LaNiO3 lattice led to remarkably improved catalytic activity of this catalyst in soot combustion. Amongst various catalysts, La0.95K0.05NiO3 exhibited the highest activity in soot combustion (with its T50 and CO2 S values being 338 °C and 98.2%, respectively), which is comparable to the catalytic activities of Pt-based catalysts under the condition of poor contact between the soot and the catalyst. K-substitution improves the valence state of Ni and increases the number of oxygen vacancies, thereby leading to increased density of surface-active oxygen species. The active oxygen species play a vital role in catalyzing the elimination of soot. The perovskite-type La1?xKxNiO3 nanocatalysts with 3DOM structure without noble metals have potential for practical applications in the catalytic combustion of diesel soot particles.

Novel indirect Z-scheme g-C3N4/Bi2MoO6/Bi hollow microsphere heterojunctions with SPR-promoted visible absorption and highly enhanced photocatalytic performance

The surface plasmonic resonance(SPR)effect of Bi can effectively improve the light absorption abilities and photogenerated charge carrier separation rate.In this study,a novel ternary heterojunction of g-C3N4/Bi2MoO6/Bi(CN/BMO/Bi)hollow microsphere was successfully fabricated through solvothermal and in situ reduction methods.The results revealed that the optimal ternary 0.4 CN/BMO/9 Bi photocatalyst exhibited the highest photocatalytic efficiency toward rhodamine B(RhB)degradation with nine times that of pure BMO.The DRS and valence band of the X-ray photoelectron spectroscopy spectrum demonstrate that the band structure of 0.4 CN/BMO/9 Bi is a z-scheme structure.Quenching experiments also provided solid evidence that the·O^2-(at-0.33 eV)is the main species during dye degradation,and the conduction band of g-C3N4 is only the reaction site,demonstrating that the transfer of photogenerated charge carriers of g-C3N4/Bi2 MoO 6/Bi is through an indirect z-scheme structure.Thus,the enhanced photocatalytic performance was mainly ascribed to the synergetic effect of heterojunction structures between g-C3N4 and Bi2MoO6 and the SPR effect of Bi doping,resulting in better optical absorption ability and a lower combination rate of photogenerated charge carriers.The findings in this work provide insight into the synergism of heterostructures and the SPR absorption ability in wastewater treatment.

Synthesis of isosorbide-based polycarbonates via melt polycondensation catalyzed by quaternary ammonium ionic liquids

A series of quaternary ammonium ionic liquids(ILs)were synthesized and employed as catalysts for the production of poly(isosorbide carbonate)(PIC)from diphenyl carbonate and isosorbide via a melt polycondensation process.The relationship between the anions of the ILs and the catalytic activities was investigated,and the readily‐prepared IL tetraethylammonium imidazolate(TEAI)was found to exhibit the highest catalytic activity.After optimizing the reaction conditions,a PIC with a weight‐average molecular weight(Mw)of25600g/mol was obtained,in conjunction with an isosorbide conversion of92%.As a means of modifying the molecular flexibility and thermal properties of the PIC,poly(aliphatic diol‐co‐isosorbide carbonate)s(PAIC)s were successfully synthesized,again using TEAI,and polymers with Mw values ranging from29000to112000g/mol were obtained.13C NMR analyses determined that the PAIC specimens had random microstructures,while differential scanning calorimetry demonstrated that each of the PAICs were amorphous and had glass transition temperatures ranging from50to115°C.Thermogravimetric analyses found Td‐5%values ranging from316to332°C for these polymers.Based on these data,it is evident that the incorporation of linear or cyclohexane‐based diol repeating units changed the thermal properties of the PIC.

Novel PtPd alloy nanoparticle-decorated g-C_3N_4 nanosheets with enhanced photocatalytic activity for H_2 evolution under visible light irradiation

PtPd bimetallic alloy nanoparticle (NP)-modified graphitic carbon nitride (g-C3N4) nanosheet photocatalysts were synthesized via chemical deposition precipitation. Characterization of the photocatalytic H2 evolution of the g-C3N4 nanosheets shows that it was significantly enhanced when PtPd alloy NPs were introduced as a co-catalyst. The 0.2 wt% PtPd/g-C3N4 composite photocatalyst gave a maximum H2 production rate of 1600.8 μmol g^–1 h^–1. Furthermore, when K2HPO4 was added to the reaction system, the H2 production rate increased to 2885.0 μmol g^–1 h^–1. The PtPd/g-C3N4 photocatalyst showed satisfactory photocatalytic stability and was able to maintain most of its photocatalytic activity after four experimental photocatalytic cycles. In addition, a possible mechanism for the enhanced photocatalytic activity was proposed and verified by various photoelectric techniques. These results demonstrate that the synergistic effect between PtPd and g-C3N4 helps to greatly improve the photocatalytic activity of the composite photocatalyst.

Acoustic reflection well logging modeling using the frequency-domain finite-element method with a hybrid PML

In this paper, we propose a hybrid PML （H-PML） combining the normal absorption factor of convolutional PML （C-PML） with tangential absorption factor of Mutiaxial PML （M-PML）. The H-PML boundary conditions can better suppress the numerical instability in some extreme models, and the computational speed of finite-element method and the dynamic range are greatly increased using this HPML. We use the finite-element method with a hybrid PML to model the acoustic reflection of the interface when wireline and well logging while drilling （LWD）, in a formation with a reflector outside the borehole. The simulation results suggests that the PS- and SP- reflected waves arrive at the same time when the inclination between the well and the outer interface is zero, and the difference in arrival times increases with increasing dip angle. When there are fractures outside the well, the reflection signal is clearer in the subsequent reflection waves and may be used to identify the fractured zone. The difference between the dominant wavelength and the model scale shows that LWD reflection logging data are of higher resolution and quality than wireline acoustic reflection logging.

Inversion-based data-driven time-space domain random noise attenuation method

Conventional time-space domain and frequency-space domain prediction filtering methods assume that seismic data consists of two parts, signal and random noise. That is, the so-called additive noise model. However, when estimating random noise, it is assumed that random noise can be predicted from the seismic data by convolving with a prediction error filter. That is, the source-noise model. Model inconsistencies, before and after denoising, compromise the noise attenuation and signal-preservation performances of prediction filtering methods. Therefore, this study presents an inversion-based time-space domain random noise attenuation method to overcome the model inconsistencies. In this method, a prediction error filter （PEF）, is first estimated from seismic data; the filter characterizes the predictability of the seismic data and adaptively describes the seismic data＇s space structure. After calculating PEF, it can be applied as a regularized constraint in the inversion process for seismic signal from noisy data. Unlike conventional random noise attenuation methods, the proposed method solves a seismic data inversion problem using regularization constraint; this overcomes the model inconsistency of the prediction filtering method. The proposed method was tested on both synthetic and real seismic data, and results from the prediction filtering method and the proposed method are compared. The testing demonstrated that the proposed method suppresses noise effectively and provides better signal-preservation performance.

Three-dimensionally ordered macroporous CeO_2/Al_2O_3-supported Au nanoparticle catalysts: Effects of CeO_2 nanolayers on catalytic activity in soot oxidation

A series of catalysts consisting of three‐dimensionally ordered macroporous(3DOM)x‐CeO2/Al2O3‐supported Au nanoparticles(x=2,10,20,and40wt%)were successfully synthesized using a reduction‐deposition method.These catalysts were characterized using scanning electron microscopy,the Brunauer‐Emmett‐Teller method,X‐ray diffraction,transmission electron microscopy,ultraviolet‐visible spectroscopy,and temperature‐programmed reduction by H2.Au nanoparticles of mean particle size5nm were well dispersed and supported on the inner walls of uniform macropores.The3DOM structure improved the contact efficiency between soot and the catalyst.An Al‐Ce‐O solid solution was formed in the multilayer support,i.e.,x‐CeO2/Al2O3,by the incorporation of Al3+ions into the CeO2lattice,which resulted in the creation of extrinsic oxygen vacancies.Strong interactions between the metal(Au)and the support(Ce)increased the amount of active oxygen species,and this promoted soot oxidation.The catalytic performance in soot combustion was evaluated using a temperature‐programmed oxidation technique.The presence of CeO2nanolayers in the3DOM Au/x‐CeO2/Al2O3catalysts clearly improved the catalytic activities in soot oxidation.Among the prepared catalysts,3DOM Au/20%CeO2/Al2O3showed high catalytic activity and stability in diesel soot oxidation.

Ultrasonic attenuation estimation based on time-frequency analysis

The quality factor(or Q value)is an important parameter for characterizing the inelastic properties of rock.Achieving a Q value estimation with high accuracy and stability is still challenging.In this study,a new method for estimating ultrasonic attenuation using a spectral ratio based on an S transform(SR-ST)is presented to improve the stability and accuracy of Q estimation.The variable window of ST is used to solve the time window problem.We add two window factors to the Gaussian window function in the ST.The window factors can adjust the scale of the Gaussian window function to the ultrasonic signal,which reduces the calculation error attributed to the conventional Gaussian window function.Meanwhile,the frequency bandwidth selection rules for the linear regression of the amplitude ratio are given to further improve stability and accuracy.First,the feasibility and influencing factors of the SR-ST method are studied through numerical testing and standard sample experiments.Second,artificial samples with different Q values are used to study the adaptability and stability of the SR-ST method.Finally,a further comparison between the new method and the conventional spectral ratio method(SR)is conducted using rock field samples,again addressing stability and accuracy.The experimental results show that this method will yield an error of approximately 36%using the conventional Gaussian window function.This problem can be solved by adding the time window factors to the Gaussian window function.The frequency bandwidth selection rules and mean slope value of the amplitude ratio used in the SR-ST method can ensure that the maximum error of different Q values estimation(Q>15)is less than 10%.

Prediction of brittleness based on anisotropic rock physics model for kerogen-rich shale

The construction of a shale rock physics model and the selection of an appropriate brittleness index （B/） are two significant steps that can influence the accuracy of brittleness prediction. On one hand, the existing models of kerogen-rich shale are controversial, so a reasonable rock physics model needs to be built. On the other hand, several types of equations already exist for predicting the BI whose feasibility needs to be carefully considered. This study constructed a kerogen-rich rock physics model by performing the self- consistent approximation and the differential effective medium theory to model intercoupled clay and kerogen mixtures. The feasibility of our model was confirmed by comparison with classical models, showing better accuracy. Templates were constructed based on our model to link physical properties and the BL Different equations for the BI had different sensitivities, making them suitable for different types of formations. Equations based on Young＇s Modulus were sensitive to variations in lithology, while those using Lame＇s Coefficients were sensitive to porosity and pore fluids. Physical information must be considered to improve brittleness prediction.

lp norm inverse spectral decomposition and its multi-sparsity fusion interpretation

Spectral decomposition has been widely used in the detection and identifi cation of underground anomalous features(such as faults,river channels,and karst caves).However,the conventional spectral decomposition method is restrained by the window function,and hence,it mostly has low time–frequency focusing and resolution,thereby hampering the fi ne interpretation of seismic targets.To solve this problem,we investigated the sparse inverse spectral decomposition constrained by the lp norm(0<p≤1).Using a numerical model,we demonstrated the higher time–frequency resolution of this method and its capability for improving the seismic interpretation for thin layers.Moreover,given the actual underground geology that can be often complex,we further propose a p-norm constrained inverse spectral attribute interpretation method based on multiresolution time–frequency feature fusion.By comprehensively analyzing the time–frequency spectrum results constrained by the diff erent p-norms,we can obtain more refined interpretation results than those obtained by the traditional strategy,which incorporates a single norm constraint.Finally,the proposed strategy was applied to the processing and interpretation of actual three-dimensional seismic data for a study area covering about 230 km^(2) in western China.The results reveal that the surface water system in this area is characterized by stepwise convergence from a higher position in the north(a buried hill)toward the south and by the development of faults.We thus demonstrated that the proposed method has huge application potential in seismic interpretation.

Comparison of different inversion methods of D-T_(2)two-dimensional nuclear magnetic resonance logging and applicability analysis

D-T_(2)two-dimensional nuclear magnetic resonance(2D NMR)logging technology can distinguish pore fluid types intuitively,and it is widely used in oil and gas exploration.Many 2D NMR inversion methods(e.g.,truncated singular value decomposition(TSVD),Butler-Reds-Dawson(BRD),LM-norm smoothing,and TIST-L1 regularization methods)have been proposed successively,but most are limited to numerical simulations.This study focused on the applicability of different inversion methods for NMR logging data of various acquisition sequences,from which the optimal inversion method was selected based on the comparative analysis.First,the two-dimensional NMR logging principle was studied.Then,these inversion methods were studied in detail,and the precision and computational efficiency of CPMG and diffusion editing(DE)sequences obtained from oil-water and gas-water models were compared,respectively.The inversion results and calculation time of truncated singular value decomposition(TSVD),Butler-Reds-Dawson(BRD),LM-norm smoothing,and TIST-L1 regularization were compared and analyzed through numerical simulations.The inversion method was optimized to process SP mode logging data from the MR Scanner instrument.The results showed that the TIST-regularization and LM-norm smoothing methods were more accurate for the CPMG and DE sequence echo trains of the oil-water and gas-water models.However,the LM-norm smoothing method was less time-consuming,making it more suitable for logging data processing.A case study in well A25 showed that the processing results by the LM-norm smoothing method were consistent with GEOLOG software.This demonstrates that the LM-norm smoothing method is applicable in practical NMR logging processing.

Backpropagation neural network method in data processing of ultrasonic imaging logging-while-drilling

The existing methods for extracting the arrival time and amplitude of ultrasonic echo cannot eff ectively avoid the local interference of ultrasonic signals while drilling,which leads to poor accuracy of the echo arrival time and amplitude extracted by an ultrasonic imaging logging-while-drilling tool.In this study,a demodulation algorithm is used to preprocess the ultrasonic simulation signals while drilling,and we design a backpropagation neural network model to fit the relationship between the waveform data and time and amplitude.An ultrasonic imaging logging model is established,and the finite element simulation software is used for forward modeling.The response under diff erent measurement conditions is simulated by changing the model parameters,which are used as the input layer of the neural network model;The ultrasonic echo signal is considered as a low-frequency signal modulated by a high-frequency carrier signal,and a low-pass fi lter is designed to remove the high-frequency signal and obtain the low-frequency envelope signal.Then the amplitude of the envelope signal and its corresponding time are extracted as an output layer of the neural network model.By comparing the application eff ects of the various training methods,we fi nd that the conjugate gradient descent method is the most suitable method for solving the neural network model.The performance of the neural network model is tested using 11 groups of simulation test data,which verify the eff ectiveness of the model and lay the foundation for further practical application.

Incremental semi-supervised learning for intelligent seismic facies identification

Intelligent seismic facies identification based on deep learning can alleviate the time-consuming and labor-intensive problem of manual interpretation,which has been widely applied.Supervised learning can realize facies identification with high efficiency and accuracy;however,it depends on the usage of a large amount of well-labeled data.To solve this issue,we propose herein an incremental semi-supervised method for intelligent facies identification.Our method considers the continuity of the lateral variation of strata and uses cosine similarity to quantify the similarity of the seismic data feature domain.The maximum-diff erence sample in the neighborhood of the currently used training data is then found to reasonably expand the training sets.This process continuously increases the amount of training data and learns its distribution.We integrate old knowledge while absorbing new ones to realize incremental semi-supervised learning and achieve the purpose of evolving the network models.In this work,accuracy and confusion matrix are employed to jointly control the predicted results of the model from both overall and partial aspects.The obtained values are then applied to a three-dimensional(3D)real dataset and used to quantitatively evaluate the results.Using unlabeled data,our proposed method acquires more accurate and stable testing results compared to conventional supervised learning algorithms that only use well-labeled data.A considerable improvement for small-sample categories is also observed.Using less than 1%of the training data,the proposed method can achieve an average accuracy of over 95%on the 3D dataset.In contrast,the conventional supervised learning algorithm achieved only approximately 85%.

Serial structure multi-task learning method for predicting reservoir parameters

Buiding data-driven models using machine learning methods has gradually become a common approach for studying reservoir parameters.Among these methods,deep learning methods are highly effective.From the perspective of multi-task learning,this paper uses six types of logging data—acoustic logging(AC),gamma ray(GR),compensated neutron porosity(CNL),density(DEN),deep and shallow lateral resistivity(LLD)and shallow lateral resistivity(LLS)—that are inputs and three reservoir parameters that are outputs to build a porosity saturation permeability network(PSP-Net)that can predict porosity,saturation,and permeability values simultaneously.These logging data are obtained from 108 training wells in a medium₋low permeability oilfield block in the western district of China.PSP-Net method adopts a serial structure to realize transfer learning of reservoir-parameter characteristics.Compared with other existing methods at the stage of academic exploration to simulating industrial applications,the proposed method overcomes the disadvantages inherent in single-task learning reservoir-parameter prediction models,including easily overfitting and heavy model-training workload.Additionally,the proposed method demonstrates good anti-overfitting and generalization capabilities,integrating professional knowledge and experience.In 37 test wells,compared with the existing method,the proposed method exhibited an average error reduction of 10.44%,27.79%,and 28.83%from porosity,saturation,permeability calculation.The prediction and actual permeabilities are within one order of magnitude.The training on PSP-Net are simpler and more convenient than other single-task learning methods discussed in this paper.Furthermore,the findings of this paper can help in the re-examination of old oilfield wells and the completion of logging data.

Seismic wavefield modeling based on time-domain symplectic and Fourier finite-difference method

Seismic wavefield modeling is important for improving seismic data processing and interpretation. Calculations of wavefield propagation are sometimes not stable when forward modeling of seismic wave uses large time steps for long times. Based on the Hamiltonian expression of the acoustic wave equation, we propose a structure-preserving method for seismic wavefield modeling by applying the symplectic finite-difference method on time grids and the Fourier finite-difference method on space grids to solve the acoustic wave equation. The proposed method is called the symplectic Fourier finite-difference （symplectic FFD） method, and offers high computational accuracy and improves the computational stability. Using acoustic approximation, we extend the method to anisotropic media. We discuss the calculations in the symplectic FFD method for seismic wavefield modeling of isotropic and anisotropic media, and use the BP salt model and BP TTI model to test the proposed method. The numerical examples suggest that the proposed method can be used in seismic modeling of strongly variable velocities, offering high computational accuracy and low numerical dispersion. The symplectic FFD method overcomes the residual qSV wave of seismic modeling in anisotropic media and maintains the stability of the wavefield propagation for large time steps.

Model-data-driven AVO inversion method based on multiple objective functions

The model-driven inversion method and data-driven prediction method are eff ective to obtain velocity and density from seismic data.The former necessitates initial models and cannot provide high-resolution inverted parameters because it primarily employs medium-frequency information from seismic data.The latter can predict parameters with high resolution,but it require a signifi cant number of accurate training samples,which are typically in limited supply.To solve the problems mentioned for these two methods,we propose a model-data-driven AVO inversion method based on multiple objective functions.The proposed method implements network training,network optimization,and network inversion by using three independent objective functions.Tests on synthetic and fi eld data show that the proposed method can invert high-accuracy and high-resolution velocity and density with a few training samples.

Experimental study on the multiscale scattering of high-velocity heterogeneous bodies

There are complex heterogeneous entities in the underground medium,and the heterogeneous scale has a substantial impact on wave propagation.In this study,we used a set of 11 samples of glass beads as high-velocity heterogeneous bodies to evaluate the impact of such heterogeneous bodies on the propagation of P-wave.We vary the heterogeneous scale by changing the diameter of the glass beads from 0.18 to 11 mm while keeping the same volume proportion(10%)of the beads for the set of 11 samples.The pulse transmission method was used to record measurements at the ultrasonic frequencies of 0.34,0.61,and 0.84 MHz in the homogeneous matrix.The relationship between P-wave fi eld features and heterogeneity scale,P-wave velocity,and the multiple of the wave number and heterogeneous scale(ka)was observed in the laboratory,which has sparked widespread interest and research.Heterogeneous scale affects P-wave propagation,and its wave field changes are complex.The waveform,amplitude,and velocity of the recorded P-waves correlate with the heterogeneous scale.For the forward scattering while large-scale heterogeneities,noticeable direct and diff racted waves are observed in the laboratory,which indicates that the infl uence of direct and diff racted waves cannot be ignored for large-scale heterogeneities.The relationship between velocity and ka shows frequency dependence;the reason is that the magnitude of change in velocity caused by wave number is diff erent from that caused by heterogeneous scale.According to the change in the recorded waveform,amplitude variation,or the relationship between the velocity measured at diff erent frequencies and the heterogeneous scale,the identifi ed turning points of the ray approximation are all around ka=10.When ka is less than 1,the velocity changes slowly and gradually approaches the eff ective medium velocity.The ray velocity measured for heterogeneous media with large velocity perturbations in the laboratory is signifi cantly smaller than the velocity predicted by the perturbation theory.