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Quantitative characterizations of anisotropic dynamic properties in organic-rich shale with different kerogen content

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Petroleum Science 2024年第2期21卷 855-865页

作者： Jian-Yong Xie Yan-Ping Fang xing-Hua Wu Jian'er Zhao jun-cheng Dai jun-xing c.o Ji-Xin Deng State Key Laboratory of oil and Gas Reservoir Geology and Exploitation Chengdu University of TechnologyChengdu610059SichuanChina Key Lab of Earth Exploration&Information Techniques of Ministry of Education Geophysical InstituteChengdu University of TechnologyChengdu610059SichuanChina Petrochina Southwest oil&Gasfield company Chengdu610017SichuanChina

Understanding the quantitative responses of anisotropic dynamic properties in organic-rich shale with different kerogen content(Kc)is of great significance in hydrocarbon exploration and *** controlled experiments with a single variable is challenging for natural shales due to their high variations in components,diagenesis conditions,or pore *** employed the hot-pressing technique to construct 11 well-controlled artificial shale with varying *** artificial shale samples were successive machined into prismatic shape for ultrasonic measurements along different *** revealed bedding perpendicular P-wave velocities are more sensitive to the increasing Kc than bedding paralleling velocities due to the preferential alignments of *** elastic stiffnesses except c_(13)are generally decreasing with the increasing Kc,the variation of c_(1) and c_(33)on kerogen content are more sensitive than those of c_(44)and c_(66).Apparent dynamic mechanical parameters(v and E)were found to have linear correlation with the true ones from complete anisotropic equations independent of Kc,which hold value towards the interpretation of well logs consistently ac.oss formations,Anisotropic mechanical parameters(ΔE and brittlenessΔB)tend to decrease with the reducing Kc,withΔB showing great sensitivity to Kc *** the range of low Kc(o demonstrated a linearly negative correlation with Kc,and the V_(P)/V_(S) ratio magnitude of less than 1.75may serve as a significant characterization for highly organic-rich(>10%)shale,compilation of data from natural organic rich-shales globally verified the similar systematic relationships that can be empirically used to predict the fraction of Kc in shales.

关键词： Elastic properties organic shale Anisotropy Kerogen content Physical modeling

A hybrid WUDT-NAFnet for simultaneous source data deblending

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Petroleum Science 2024年第3期21卷 1649-1659页

作者： chao-Fan Ke Shao-Huan Zu jun-xing c.o Xu-Dong Jiang chao Li xing-Ye Liu Key Laboratory of Earth Exploration and Information Technology of Ministry of Education Chengdu University of Technology State Key Laboratory of oil and Gas Reservoir Geology and Exploitation Chengdu University of Technology college of Geophysics Chengdu University of Technology

Simultaneous source technology,which reduces seismic survey time and improves the quality of seismic data by firing more than one source with a narrow time interval,is compromised by the massive blended ***,deblending algorithms have been developed to separate this ***,deep learning(DL)has been proved its great potential in suppressing the *** most popular DL method employs neural network as a filter to attenuate the blended noise in an iterative estimation and subtraction framework(IESF).However,there are still amplitude distortion and blended noise residual problems,especially when dealing with weak signal submerged in strong *** address these problems,we propose a hybrid WUDT-NAFnet,which contains two *** first network is a wavelet based U-shape deblending transformer network(WUDTnet),incorporated into IESF as a robust regularization term to iteratively separate the blended *** second network is a nonlinear activate free network(NAFnet)designed to recover the event amplitude and further suppress the weak noise residual in *** the hybrid network,the blended noise can be separated purposefully and *** using synthetic and field seismic data demonstrate that the WUDTNAFnet outperforms traditional curvelet transform(cT)based method and the deblending transformer(DT)model in terms of ***,for field applications,the data augmentation method of bicubic interpolation is applied to mitigate the feature difference between synthetic and field ***,the trained network exhibits strong signal preservation ability in numerical field example without requiring additional training.

关键词： Simultaneous-source Deblending Deep learning Transformer

Tuning heterogeneous mic.ostructures to enhance mechanical properties of nano-TiN particle reinforced Haynes 230 composites by laser powder bed fusion

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Rare Metals 2024年第9期43卷 4548-4565页

作者： Wen-Jie Liu Hui Li Qian-xing Yin He-jun Du Sc.ool of Power and Mechanical Engineering Wuhan UniversityWuhan 430072China The Institute of Technological Sciences Wuhan UniversityWuhan 430072China Sc.ool of Mechanical&Aerospace Engineering Nanyang Technological UniversitySingapore 639798Singapore

Laser powder bed fusion(LPBF)is considered to be one of the most promising additive manufacturing technologies for producing components with geometries and high geometrical precision that are unattainable by traditional *** superalloy exhibits exceptional mechanical and high-temperature performances,rendering it a prime candidate for advanced aero-engine *** the high demand for LPBF-manufactured superalloys,the superalloy is one of the materials manufactured difficultly by LPBF due to their large laser absorptivity fluctuation,poor molten pool stability and sharp temperature ***,superalloys are characterized by severe pores,undesirable coarse columnar grains and poor mechanical *** this work,the effect of nano-TiN particles on defects,molten pool characteristics and mic.ostructure and performance of the composites were *** 4.5 wt%TiN/Haynes230 samples exhibited exceptional nanohardness and elastic modulus with maximum values reaching 5.53 GPa and 240.03 GPa,*** superior mechanical properties were attributed to the combined effects of spatter and gas pore inhibition,grain refinement and duplex nanophases ***,the stability of molten pool was enhanced,and spatter was effectively suppressed by adding nano-TiN particles,while grain refinement and columnar to equiaxed transitions were ***,the matrix exhibited a high dislocation density due to a significant hindrance of dislocation movement caused by massive nano-phases(e.g.,TiN and M_(23)c_(6)),resulting in the formation of extensive dislocation tangles and *** work offers novel insights into the role of nanoparticles reinforced superalloy composites by LPBF.

关键词： composites Laser powder bed fusion Molten pool characteristics Mic.ostructure Mechanical properties

Evaluating the tumor stratification with a lysosomal pH sensitive-probe by fluorescence lifetime imaging

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chinese chemical Letters 2024年第3期35卷 262-267页

作者： jun Yan xing Liang Qian Zhang Luolin Wang Weiying Lin Guangxi Key Laboratory of Electrochemical Energy Materials Institute of Optical Materials and Chemical BiologySchool of Chemistry and Chemical EngineeringGuangxi UniversityNanning 530004China

owing to the anaerobic metabolism in the tumor,abundant acidic metabolites are produced and accumulated in the ***,the cells in different tumor layers are directly linked to the pH ***,due to the lack of robust tools,the high-efficient evaluation of the acidic mic.o-environment of tumor stratification faces the challenge of accurate *** designed a new pH sensitive fluorescent lifetime probe target to *** we expected,the fluorescence lifetime of PLN possesses a good linear fit to the pH value,which could detect the pH change at a single lysosome level in real time,and then evaluate the different acidity of tumor *** probe PLN is successfully used to evaluate the tumor stratification by fluorescence lifetime imaging mic.oscopy(FLIM)for the first time,which is of great significance in the preoperative diagnosis of clinical tumor treatment or evaluation of drug delivery effect.

关键词： FLIM pH Fluorescence imaging Lysosome Tumor imaging

Tuning mechanical and electrical performances of B_(4)c-TiB_(2)ceramics in a two-step spark plasma sintering process

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Journal of Advanced ceramics 2024年第4期13卷 518-528页

作者： jun Zhao xingshuo Zhang Zongning Ma Dong Wang xing Jin Songlin Ran Advanced ceramics Research center School of Materials Science and EngineeringAnhui University of TechnologyMaanshan 243002China Anhui Engineering Research center for High Efficiency Intelligent Photovoltaic Module Chaohu UniversityHefei 238024China

B_(4)c–TiB_(2)is an advanced electrically conductive ceramic with excellent mechanical and electrical discharge machinable *** is challenging and rewarding to achieve highly conductive and hard B_(4)c–TiB_(2)composites at a minimum content of conductive TiB2 that has inferior hardness but double specific gravity of the B_(4)c matrix.A novel strategy was used to construct conductive networks in B_(4)c‒15 vol%TiB2 composite ceramics with B_(4)c,Tic,and amorphous B as raw materials by a two-step spark plasma sintering *** influences of particle size matching between B_(4)c and Tic.on the conducting of the strategy and the mic.ostructure were discussed based on the selective matrix grain growth *** mechanical and electrical properties were also systematically *** B_(4)c–15 vol%TiB2 composite ceramic prepared from 10.29µm B_(4)c and 0.05µm Tic powders exhibited a perfect three-dimensional interconnected conductive network with a maximum electrical conductivity of 4.25×10^(4)S/m,together with excellent mechanical properties including flexural strength,Vickers hardness,and fracture toughness of 691±58 MPa,30.30±0.61 GPa,and 5.75±0.32 MPa·m^(1/2),respectively,while the composite obtained from 3.12µm B_(4)c and 0.8µm Tic powders had the best mechanical properties including flexural strength,Vickers hardness,and fracture toughness of 827±35 MPa,32.01±0.51 GPa,and 6.45±0.22 MPa·m^(1/2),together with a decent electrical conductivity of 0.65×10^(4)S/m.

关键词： B_(4)c-TiB_(2) mechanical properties electrical conductivity conductive network

Runout prediction of potential landslides based on the multi-source data collaboration analysis on historical cases

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china Geology 2024年第2期7卷 264-276页

作者： jun Sun Yu Zhuang Ai-guo xing Guizhou Geology and Mineral Engineering construction co. LtdGuiyang 550000China State Key Laboratory of ocean Engineering Shanghai Jiao Tong UniversityShanghai 200240China

Long runout landslides involve a massive amount of energy and can be extremely hazardous owing to their long movement distance,high mobility and strong destructive *** methods have been widely used to predict the landslide runout but a fundamental problem remained is how to determine the reliable numerical *** study proposes a framework to predict the runout of potential landslides through multi-source data collaboration and numerical analysis of historical landslide ***,for the historical landslide cases,the landslide-induced seismic signal,geophysical surveys,and possible in-situ drone/phone videos(multi-source data collaboration)can validate the numerical results in terms of landslide dynamics and deposit features and help calibrate the numerical(rheological)***,the calibrated numerical parameters can be used to numerically predict the runout of potential landslides in the region with a similar geological setting to the recorded *** of the runout prediction approach to the 2020 Jiashanying landslide in Guizhou,china gives reasonable results in comparison to the field *** numerical parameters are determined from the multi-source data collaboration analysis of a historical case in the region(2019 Shuicheng landslide).The proposed framework for landslide runout prediction can be of great utility for landslide risk assessment and disaster reduction in mountainous regions worldwide.

关键词： Landslide runout prediction Drone survey Multi-source data collaboration DAN3D numerical modeling Jianshanying landslide Guizhou Province Geological hazards survey engineering

Mac.oscopic model and statistical model to characterize electromagnetic information of a digital coding metasurface

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National Science Review 2024年第3期11卷 159-171页

作者： Rui Wen Shao jun Wei Wu Zheng xing Wang Hui Xu Han Qing Yang Qiang cheng Tie jun cui Institute of Electromagnetic Space Southeast University State Key Laboratory of Millimeter Waves Southeast University Peng cheng Laboratory Pazhou Laboratory(Huangpu)

A digital coding metasurface is a platform connecting the digital space and electromagnetic wave space, and has therefore gained much attention due to its intriguing value in reshaping wireless channels and realizing new communication architectures. correspondingly, there is an urgent need for electromagnetic information theory that reveals the upper limit of communication capacity and supports the accurate design of metasurface-based communication systems. To this end, we propose a mac.oscopic model and a statistical model of the digital coding metasurface. The mac.oscopic model uniformly ac.ommodates both digital and electromagnetic aspects of the meta-atoms and predicts all possible scattered fields of the digital coding metasurface based on a small number of simulations or measurements. Full-wave simulations and experimental results show that the mac.oscopic model is feasible and accurate. A statistical model is further proposed to correlate the mutual coupling between meta-atoms with covariance and to calculate the entropy of the equivalent currents of digital coding metasurface. These two models can help reconfigurable intelligent surfaces achieve more accurate beamforming and channel estimation, and thus improve signal power and coverage. Moreover, the models will encourage the creation of a precoding codebook in metasurface-based direct digital modulation systems, with the aim of approaching the upper limit of channel capacity. With these two models, the concepts of current space and current entropy, as well as the analysis of information loss from the coding space to wave space, is established for the first time, helping to bridge the gap between the digital world and the physical world, and advancing developments of electromagnetic information theory and new-architecture wireless systems.

关键词： electromagnetic information theory current entropy current space digital coding metasurface mac.oscopic model statistical model

Superatomic Ag_(58) nanoclusters incorporating a [MS_(4)@Ag_(12)]^(2+)(M = Mo or W) kernel show aggregation-induced emission

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chinese chemical Letters 2024年第10期35卷 452-456页

作者： jun-Jie Fang Zheng Liu Yun-Peng Xie xing Lu State Key Laboratory of Materials Processing and Die&Mould Technology School of Materials Science and EngineeringHuazhong University of Science and TechnologyWuhan 430074China

In core-shell silver nanoclusters,the control of core structure presents a more formidable challenge compared to that of the shell ***,we report the successful synthesis and characterization of four distinct silver thiolate nanoclusters[MS_(4)@Ag_(12)@Ag_(46)S_(24)(dppb)_(12)](M=Mo or W),each incorporating a cup-like[MS_(4)@Ag_(12)]^(2+)*** nanoclusters were meticulously prepared using(NH_(4))2Mo S4or(NH_(4))_(2)WS_(4)as both a template and a controlled source of ***,we have observed a unique configuration within these eight-electron superatomic Ag_(58) nanoclusters,where the zerovalent Ag atoms reside exclusively within the inner[MS_(4)@Ag_(12)]^(2+)*** stands in contrast to other superatomic clusters possessing an Ag(0)***,the introduction of phenyl-containing compounds during the synthesis process induced a transformation in the space group symmetry from c_(2)/c to I 4ˉ.This transformative effect was found to originate from the interplay between adjacent 1,4-bis(diphenylphosphino)butane(dppb)ligands,which facilitated enhanced emission through aggregationinduced intermolecular interactions,specifically c-H···π***,our findings contribute substantively to the understanding of the intricate relationship between nanocluster structures and their corresponding properties,shedding light on the crucial roles played by templates and diphosphine ligands in this context.

关键词： Superatom Silver nanocluster Template synthesis Photoluminescence Aggregation-induced emission

Intelligent geochemical interpretation of mass chromatograms:Based on convolution neural network

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Petroleum Science 2024年第2期21卷 752-764页

作者： Kai-Ming Su jun-Gang Lu Jian Yu Zi-xing Lu Shi-Jia chen Hubei Key Laboratory of Petroleum Geochemistry and Environment Wuhan430100HubeiChina college of Resources and Environment Yangtze UniversityWuhan430100HubeiChina Sc.ool of Geoscience and Technology Southwest Petroleum UniversityChengdu610500SichuanChina changqing oilfield company PetroChinaXi'an710021ShaanxiChina

Gas chromatography-mass spectrometry(Gc-MS)is an extremely important analytical technique that is widely used in organic *** is the only approach to capture biomarker features of organic matter and provides the key evidence for oil-source correlation and thermal maturity ***,the conventional way of processing and interpreting the mass chromatogram is both timeconsuming and labor-intensive,which increases the research cost and restrains extensive applications of this *** overcome this limitation,a correlation model is developed based on the convolution neural network(cNN)to link the mass chromatogram and biomarker features of samples from the Triassic Yanchang Formation,ordos Basin,*** this way,the mass chromatogram can be automatically *** research first performs dimensionality reduction for 15 biomarker parameters via the fac.or analysis and then quantifies the biomarker features using two indexes(*** and PMI)that represent the organic matter thermal maturity and parent material type,***,training,interpretation,and validation are performed multiple times using different cNN models to optimize the model structure and hyper-parameter setting,with the mass chromatogram used as the input and the obtained MI and PMI values for supervision(label).The optimized model presents high accuracy in automatically interpreting the mass chromatogram,with R2values typically above 0.85 and0.80 for the thermal maturity and parent material interpretation results,*** significance of this research is twofold:(i)developing an efficient technique for geochemical research;(ii)more importantly,demonstrating the potential of artificial intelligence in organic geochemistry and providing vital references for future related studies.

关键词： organic geochemistry Biomarker Mass chromatographic analysis Automated interpretation convolution neural network Machine learning