Resonantly enhanced second-and third-harmonic generation in dielectric nonlinear metasurfaces

Nonlinear dielectric metasurfaces provide a promising approach to control and manipulate frequency conversion optical processes at the nanoscale,thus facilitating both advances in fundamental research and the development of new practical applications in photonics,lasing,and sensing.Here,we employ symmetry-broken metasurfaces made of centrosymmetric amorphous silicon for resonantly enhanced second-and third-order nonlinear optical response.Exploiting the rich physics of optical quasi-bound states in the continuum and guided mode resonances,we comprehensively study through rigorous numerical calculations the relative contribution of surface and bulk effects to second-harmonic generation(SHG)and the bulk contribution to third-harmonic generation(THG) from the meta-atoms.Next,we experimentally achieve optical resonances with high quality factors,which greatly boosts light-matter interaction,resulting in about 550 times SHG enhancement and nearly 5000-fold increase of THG.A good agreement between theoretical predictions and experimental measurements is observed.To gain deeper insights into the physics of the investigated nonlinear optical processes,we further numerically study the relation between nonlinear emission and the structural asymmetry of the metasurface and reveal that the generated harmonic signals arising from linear sharp resonances are highly dependent on the asymmetry of the meta-atoms.Our work suggests a fruitful strategy to enhance the harmonic generation and effectively control different orders of harmonics in all-dielectric metasurfaces,enabling the development of efficient active photonic nanodevices.

In vivo label-free measurement of blood flow velocity symmetry based on dual line scanning third-harmonic generation microscopy excited at the 1700 nm window

Measurement of bloodflow velocity is key to understanding physiology and pathology in vivo.While most measurements are performed at the middle of the blood vessel,little research has been done on characterizing the instantaneous bloodflow velocity distribution.This is mainly due to the lack of measurement technology with high spatial and temporal resolution.Here,we tackle this problem with our recently developed dual-wavelength line-scan third-harmonic generation(THG)imaging technology.Simultaneous acquisition of dual-wavelength THG line-scanning signals enables measurement of bloodflow velocities at two radially symmetric positions in both venules and arterioles in mouse brain in vivo.Our results clearly show that the instantaneous bloodflow velocity is not symmetric under general conditions.

Quantitative effect of kerogen type on the hydrocarbon generation potential of Paleogene lacustrine source rocks,Liaohe Western Depression,China

Kerogen types exert a decisive effect on the onset and capacity of hydrocarbon generation of source rocks.Lacustrine source rocks in the Liaohe Western Depression are characterized by thick deposition,high total organic carbon(TOC)content,various kerogen types,and a wide range of thermal maturity.Consequently,their hydrocarbon generation potential and resource estimation can be misinterpreted.In this study,geochemical tests,numerical analysis,hydrocarbon generation kinetics,and basin modeling were integrated to investigate the differential effects of kerogen types on the hydrocarbon generation potential of lacustrine source rocks.Optimized hydrocarbon generation and expulsion(HGE)models of different kerogen types were established quantitatively upon abundant Rock-Eval/TOC/vitrinite reflectance(R_(o))datasets.Three sets of good-excellent source rocks deposited in the fourth(Es4),third(Es3),and first(Es1)members of Paleogene Shahejie Formation,are predominantly types I-II_(1),II_(1)-II_(2),and II-III,respectively.The activation energy of types I-II_(2)kerogen is concentrated(180-230 kcal/mol),whereas that of type III kerogen is widely distributed(150-280 kcal/mol).The original hydrocarbon generation potentials of types I,II_(1),II_(2),and III kerogens are 790,510,270,and 85 mg/g TOC,respectively.The Ro values of the hydrocarbon generation threshold for type I-III source rocks gradually increase from 0.42%to 0.74%,and Ro values of the hydrocarbon expulsion threshold increase from 0.49%to 0.87%.Types I and II_(1)source rocks are characterized by earlier hydrocarbon generation,more rapid hydrocarbon expulsion,and narrower hydrocarbon generation windows than types II_(2)and III source rocks.The kerogen types also affect the HGE history and resource potential.Three types(conventional,tight,and shale oil/gas)and three levels(realistic,expected,and prospective)of hydrocarbon resources of different members in the Liaohe Western Depression are evaluated.Findings suggest that the Es3 member has considerable conventional and unconventional hydrocarbon resources.This study can quantitatively characterize the hydrocarbon generation potential of source rocks with different kerogen types,and facilitate a quick and accurate assessment of hydrocarbon resources,providing strategies for future oil and gas exploration.

Multi-objective optimization and evaluation of supercritical CO_(2) Brayton cycle for nuclear power generation

The supercritical CO_(2) Brayton cycle is considered a promising energy conversion system for Generation IV reactors for its simple layout,compact structure,and high cycle efficiency.Mathematical models of four Brayton cycle layouts are developed in this study for different reactors to reduce the cost and increase the thermohydraulic performance of nuclear power generation to promote the commercialization of nuclear energy.Parametric analysis,multi-objective optimizations,and four decision-making methods are applied to obtain each Brayton scheme’s optimal thermohydraulic and economic indexes.Results show that for the same design thermal power scale of reactors,the higher the core’s exit temperature,the better the Brayton cycle’s thermo-economic performance.Among the four-cycle layouts,the recompression cycle(RC)has the best overall performance,followed by the simple recuperation cycle(SR)and the intercooling cycle(IC),and the worst is the reheating cycle(RH).However,RH has the lowest total cost of investment(C_(tot))of$1619.85 million,and IC has the lowest levelized cost of energy(LCOE)of 0.012$/(kWh).The nuclear Brayton cycle system’s overall performance has been improved due to optimization.The performance of the molten salt reactor combined with the intercooling cycle(MSR-IC)scheme has the greatest improvement,with the net output power(W_(net)),thermal efficiencyη_(t),and exergy efficiency(η_(e))improved by 8.58%,8.58%,and 11.21%,respectively.The performance of the lead-cooled fast reactor combined with the simple recuperation cycle scheme was optimized to increase C_(tot) by 27.78%.In comparison,the internal rate of return(IRR)increased by only 7.8%,which is not friendly to investors with limited funds.For the nuclear Brayton cycle,the molten salt reactor combined with the recompression cycle scheme should receive priority,and the gas-cooled fast reactor combined with the reheating cycle scheme should be considered carefully.

A Multi-Stage Differential-Multifactorial Evolutionary Algorithm for Ingredient Optimization in the Copper Industry

Ingredient optimization plays a pivotal role in the copper industry,for which it is closely related to the concentrate utilization rate,stability of furnace conditions,and the quality of copper production.To acquire a practical ingredient plan,which should exhibit long duration time with sufficient utilization and feeding stability for real applications,an ingredient plan optimization model is proposed in this study to effectively guarantee continuous production and stable furnace conditions.To address the complex challenges posed by this integer programming model,including multiple coupling feeding stages,intricate constraints,and significant non-linearity,a multi-stage differential-multifactorial evolution algorithm is developed.In the proposed algorithm,the differential evolutionary(DE)algorithm is improved in three aspects to efficiently tackle challenges when optimizing the proposed model.First,unlike traditional time-consuming serial approaches,the multifactorial evolutionary algorithm is utilized to optimize multiple complex models contained in the population of evolutionary algorithm caused by the feeding stability in a parallel manner.Second,a repair algorithm is employed to adjust infeasible ingredient lists in a timely manner.In addition,a local search strategy taking feedback from the current optima and considering the different positions of global optimum is developed to avoiding premature convergence of the differential evolutionary algorithm.Finally,the simulation experiments considering different planning horizons using real data from the copper industry in China are conducted,which demonstrates the superiority of the proposed method on feeding duration and stability compared with other commonly deployed approaches.It is practically helpful for reducing material cost as well as increasing production profit for the copper industry.

Harnessing overlapped temperature-salinity gradient in solar-driven interfacial seawater evaporation for efficient steam and electricity generation

Solar-driven interfacial water evaporation(SIWE)offers a superb way to leverage concentrated solar heat to minimize energy dissipation during seawater desalination.It also engenders overlapped temperaturesalinity gradient(TSG)between water-air interface and adjacent seawater,affording opportunities of harnessing electricity.However,the efficiency of conventional SIWE technologies is limited by significant challenges,including salt passivation to hinder evaporation and difficulties in exploiting overlapped TSG simultaneously.Herein,we report self-sustaining hybrid SIWE for not only sustainable seawater desalination but also efficient electricity generation from TSG.It enables spontaneous circulation of salt flux upon seawater evaporation,inducing a self-cleaning evaporative interface without salt passivation for stable steam generation.Meanwhile,this design enables spatial separation and simultaneous utilization of overlapped TSG to enhance electricity generation.These benefits render a remarkable efficiency of90.8%in solar energy utilization,manifesting in co-generation of solar steam at a fast rate of 2.01 kg m^(-2)-h^(-1)and electricity power of 1.91 W m^(-2)with high voltage.Directly interfacing the hybrid SIWE with seawater electrolyzer constructs a system for water-electricity-hydrogen co-generation without external electricity supply.It produces hydrogen at a rapid rate of 1.29 L h^(-1)m^(-2)and freshwater with 22 times lower Na+concentration than the World Health Organization(WHO)threshold.

Multi-Stage Multidisciplinary Design Optimization Method for Enhancing Complete Artillery Internal Ballistic Firing Performance

To enhance the comprehensive performance of artillery internal ballistics—encompassing power,accuracy,and service life—this study proposed a multi-stage multidisciplinary design optimization(MS-MDO)method.First,the comprehensive artillery internal ballistic dynamics(AIBD)model,based on propellant combustion,rotation band engraving,projectile axial motion,and rifling wear models,was established and validated.This model was systematically decomposed into subsystems from a system engineering perspective.The study then detailed the MS-MDO methodology,which included Stage I(MDO stage)employing an improved collaborative optimization method for consistent design variables,and Stage II(Performance Optimization)focusing on the independent optimization of local design variables and performance metrics.The methodology was applied to the AIBD problem.Results demonstrated that the MS-MDO method in Stage I effectively reduced iteration and evaluation counts,thereby accelerating system-level convergence.Meanwhile,Stage II optimization markedly enhanced overall performance.These comprehensive evaluation results affirmed the effectiveness of the MS-MDO method.

Identifying multidisciplinary problems from scientific publications based on a text generation method

Purpose:A text generation based multidisciplinary problem identification method is proposed,which does not rely on a large amount of data annotation.Design/methodology/approach:The proposed method first identifies the research objective types and disciplinary labels of papers using a text classification technique;second,it generates abstractive titles for each paper based on abstract and research objective types using a generative pre-trained language model;third,it extracts problem phrases from generated titles according to regular expression rules;fourth,it creates problem relation networks and identifies the same problems by exploiting a weighted community detection algorithm;finally,it identifies multidisciplinary problems based on the disciplinary labels of papers.Findings:Experiments in the“Carbon Peaking and Carbon Neutrality”field show that the proposed method can effectively identify multidisciplinary research problems.The disciplinary distribution of the identified problems is consistent with our understanding of multidisciplinary collaboration in the field.Research limitations:It is necessary to use the proposed method in other multidisciplinary fields to validate its effectiveness.Practical implications:Multidisciplinary problem identification helps to gather multidisciplinary forces to solve complex real-world problems for the governments,fund valuable multidisciplinary problems for research management authorities,and borrow ideas from other disciplines for researchers.Originality/value:This approach proposes a novel multidisciplinary problem identification method based on text generation,which identifies multidisciplinary problems based on generative abstractive titles of papers without data annotation required by standard sequence labeling techniques.

Research on Regulation Method of Energy Storage System Based on Multi-Stage Robust Optimization

To address the scheduling problem involving energy storage systems and uncertain energy,we propose a method based on multi-stage robust optimization.This approach aims to regulate the energy storage system by using a multi-stage robust optimal control method,which helps overcome the limitations of traditional methods in terms of time scale.The goal is to effectively utilize the energy storage power station system to address issues caused by unpredictable variations in environmental energy and fluctuating load throughout the day.To achieve this,a mathematical model is constructed to represent uncertain energy sources such as photovoltaic and wind power.The generalized Benders Decomposition method is then employed to solve the multi-stage objective optimization problem.By decomposing the problem into a series of sub-objectives,the system scale is effectively reduced,and the algorithm’s convergence ability is improved.Compared with other algorithms,the multi-stage robust optimization model has better economy and convergence ability and can be used to guide the power dispatching of uncertain energy and energy storage systems.

The application of cellulosic-based materials on interfacial solar steam generation for highly efficient wastewater purification: A review

The world's population is growing,leading to an increasing demand for freshwater resources for drinking,sanitation,agriculture,and industry.Interfacial solar steam generation(ISSG)can solve many problems,such as mitigating the power crisis,minimizing water pollution,and improving the purification and desalination of seawater,rivers/lakes,and wastewater.Cellulosic materials are a viable and ecologically sound technique for capturing solar energy that is adaptable to a range of applications.This review paper aims to provide an overview of current advancements in the field of cellulose-based materials ISSG devices,specifically focusing on their applications in water purification and desalination.This paper examines the cellulose-based materials ISSG system and evaluates the effectiveness of various cellulosic materials,such as cellulose nanofibers derived from different sources,carbonized wood materials,and two-dimensional(2D)and 3D cellulosic-based materials from various sources,as well as advanced cellulosic materials,including bacterial cellulose and cellulose membranes obtained from agricultural and industrial cellulose wastes.The focus is on exploring the potential applications of these materials in ISSG devices for water desalination,purification,and treatment.The function,advantages,and disadvantages of cellulosic materials in the performance of ISSG devices were also deliberated throughout our discussion.In addition,the potential and suggested methods for enhancing the utilization of cellulose-based materials in the field of ISSG systems for water desalination,purification,and treatment were also emphasized.

A Railway Fastener Inspection Method Based on Abnormal Sample Generation

Regular fastener detection is necessary to ensure the safety of railways.However,the number of abnormal fasteners is significantly lower than the number of normal fasteners in real railways.Existing supervised inspectionmethods have insufficient detection ability in cases of imbalanced samples.To solve this problem,we propose an approach based on deep convolutional neural networks(DCNNs),which consists of three stages:fastener localization,abnormal fastener sample generation based on saliency detection,and fastener state inspection.First,a lightweight YOLOv5s is designed to achieve fast and precise localization of fastener regions.Then,the foreground clip region of a fastener image is extracted by the designed fastener saliency detection network(F-SDNet),combined with data augmentation to generate a large number of abnormal fastener samples and balance the number of abnormal and normal samples.Finally,a fastener inspection model called Fastener ResNet-8 is constructed by being trained with the augmented fastener dataset.Results show the effectiveness of our proposed method in solving the problem of sample imbalance in fastener detection.Qualitative and quantitative comparisons show that the proposed F-SDNet outperforms other state-of-the-art methods in clip region extraction,reaching MAE and max F-measure of 0.0215 and 0.9635,respectively.In addition,the FPS of the fastener state inspection model reached 86.2,and the average accuracy reached 98.7%on 614 augmented fastener test sets and 99.9%on 7505 real fastener datasets.

Enhanced picosecond terahertz wave generation based on cascade effects in a terahertz parametric generator

Enhanced terahertz wave generation via a Stokes cascade process has been demonstrated using picosecond pulse pumped terahertz parametric generation at 1 kHz repetition rate.Clear cascade saturation of terahertz output was observed,and the corresponding cascade-Stokes spectra were analyzed.The maximum terahertz wave average power was 22μW under a pump power of 30 W,whereas the maximum power conversion efficiency was 8×10^(-7)under a pump power of 21 W.The THz power fluctuation was measured to be about 1%in 20 min.This THz parametric source with a relatively stable output is suitable for a variety of practical applications.

Efficient C-N coupling in electrocatalytic urea generation on copper carbonate hydroxide electrocatalysts

Urea generation through electrochemical CO_(2) and NO_(3)~-co-reduction reaction(CO_(2)NO_(3)RR)is still limited by either the low selectivity or yield rate of urea.Herein,we report copper carbonate hydroxide(Cu_2(OH)_2CO_(3))as an efficient CO_(2)NO_(3)RR electrocatalyst with an impressive urea Faradaic efficiency of45.2%±2.1%and a high yield rate of 1564.5±145.2μg h~(-1)mg_(cat)~(-1).More importantly,H_(2) evolution is fully inhibited on this electrocatalyst over a wide potential range between-0.3 and-0.8 V versus reversible hydrogen electrode.Our thermodynamic simulation reveals that the first C-N coupling follows a unique pathway on Cu_2(OH)_2CO_(3) by combining the two intermediates,~*COOH and~*NHO.This work demonstrates that high selectivity and yield rate of urea can be simultaneously achieved on simple Cu-based electrocatalysts in CO_(2)NO_(3)RR,and provide guidance for rational design of more advanced catalysts.

Impact of volcanism on the formation and hydrocarbon generation of organic-rich shale in the Jiyang Depression, Bohai Bay Basin, China

Globally,most organic-rich shales are deposited with volcanic ash layers.Volcanic ash,a source for many sedimentary basins,can affect the sedimentary water environment,alter the primary productivity,and preserve the organic matter(OM)through physical,chemical,and biological reactions.With an increasing number of breakthroughs in shale oil exploration in the Bohai Bay Basin in recent years,less attention has been paid to the crucial role of volcanic impact especially its influence on the OM enrichment and hydrocarbon formation.Here,we studied the petrology,mineralogy,and geochemical characteristics of the organic-rich shale in the upper submember of the fourth member(Es_(4)^(1))and the lower submember of the third member(Es_(3)^(3))of the Shahejie Formation,aiming to better understand the volcanic impact on organic-rich shale formation.Our results show that total organic carbon is higher in the upper shale intervals rich in volcanic ash with enriched light rare earth elements and moderate Eu anomalies.This indicates that volcanism promoted OM formation before or after the eruption.The positive correlation between Eu/Eu*and Post-Archean Australian Shale indicates hydrothermal activity before the volcanic eruption.The plane graph of the hydrocarbon-generating intensity(S1+S2)suggests that the heat released by volcanism promoted hydrocarbon generation.Meanwhile,the nutrients carried by volcanic ash promoted biological blooms during Es_(4)^(1 )and Es_(3)^(3) deposition,yielding a high primary productivity.Biological blooms consume large amounts of oxygen and form anoxic environments conducive to the burial and preservation of OM.Therefore,this study helps to further understand the organic-inorganic interactions caused by typical geological events and provides a guide for the next step of shale oil exploration and development in other lacustrine basins in China.

Automatic Generation of Artificial Space Weather Forecast Product Based on Sequence-to-sequence Model

Both analyzing a large amount of space weather observed data and alleviating personal experience bias are significant challenges in generating artificial space weather forecast products.With the use of natural language generation methods based on the sequence-to-sequence model,space weather forecast texts can be automatically generated.To conduct our generation tasks at a fine-grained level,a taxonomy of space weather phenomena based on descriptions is presented.Then,our MDH(Multi-Domain Hybrid)model is proposed for generating space weather summaries in two stages.This model is composed of three sequence-to-sequence-based deep neural network sub-models(one Bidirectional Auto-Regressive Transformers pre-trained model and two Transformer models).Then,to evaluate how well MDH performs,quality evaluation metrics based on two prevalent automatic metrics and our innovative human metric are presented.The comprehensive scores of the three summaries generating tasks on testing datasets are 70.87,93.50,and 92.69,respectively.The results suggest that MDH can generate space weather summaries with high accuracy and coherence,as well as suitable length,which can assist forecasters in generating high-quality space weather forecast products,despite the data being starved.

Automatic Generation of Attribute-Based Access Control Policies from Natural Language Documents

In response to the challenges of generating Attribute-Based Access Control(ABAC)policies,this paper proposes a deep learning-based method to automatically generate ABAC policies from natural language documents.This method is aimed at organizations such as companies and schools that are transitioning from traditional access control models to the ABAC model.The manual retrieval and analysis involved in this transition are inefficient,prone to errors,and costly.Most organizations have high-level specifications defined for security policies that include a set of access control policies,which often exist in the form of natural language documents.Utilizing this rich source of information,our method effectively identifies and extracts the necessary attributes and rules for access control from natural language documents,thereby constructing and optimizing access control policies.This work transforms the problem of policy automation generation into two tasks:extraction of access control statements andmining of access control attributes.First,the Chat General Language Model(ChatGLM)isemployed to extract access control-related statements from a wide range of natural language documents by constructing unique prompts and leveraging the model’s In-Context Learning to contextualize the statements.Then,the Iterated Dilated-Convolutions-Conditional Random Field(ID-CNN-CRF)model is used to annotate access control attributes within these extracted statements,including subject attributes,object attributes,and action attributes,thus reassembling new access control policies.Experimental results show that our method,compared to baseline methods,achieved the highest F1 score of 0.961,confirming the model’s effectiveness and accuracy.

Spin-controlled generation of a complete polarization set with randomly-interleaved plasmonic metasurfaces

Optical metasurfaces,comprising subwavelength quasi-planar nanostructures,constitute a universal platform for manipulating the amplitude,phase,and polarization of light,thus paving a way for the next generation of highly integrated multifunctional optical devices.In this work,we introduce a reflective metasurface for the generation of a complete(angularly resolved)polarization set by randomly interleaving anisotropic plasmonic meta-atoms acting as nanoscale wave plates.In the proof-of-concept demonstration,we achieve multidirectional beam-steering into different polarization channels forming a complete set of polarization states,which can also be dynamically altered by switching the spin of incident light.The developed design concept represents a significant advancement in achieving flat polarization optics with advanced functionalities.

Natural high-porous diatomaceous-earth based self-floating aerogel for efficient solar steam power generation

The application of solar steam generation in seawater desalination is an effective way to solve the shortage of fresh water resources.At present,many kinds of photothermal conversion materials have been developed and used as evaporators in seawater desalination.However,some evaporators need additional thermal insulation or water supply devices to achieve efficient photothermal conversion.In addition,their complex,time consuming and no scalable preparation process,high cost of raw materials and poor salt resistance hinder the practical application of these evaporator.Owing to its distinctive nanoporous structure,diatomite as fossilized single-cells algae diatoms is a promising natural silica-based material for seawater desalination.They are taken from sea and that makes true sense to use them in the sea.Herein,we report the first example of synthesis robust three-dimensional(3D)natural-diatomite composite by assembling polyaniline nanoparticles covered diatomite into the polyvinyl alcohol pre-treated melamine foam frameworks and demonstrate its application as new evaporator for seawater desalination.The porous framework does not only improve the sunlight scattering efficiency,but also offer large network of channels for water transportation.The inherent mechanism behind salt desalination process involves the absorption of water molecules on the surface of the internal silica micro-nano pores,and evaporation under the heat induced by the polyaniline absorbed sunlight.Meanwhile,the metal ions are segregated by many available pores and channels to achieve the self-desalting effect.The developed evaporator possesses the superiority of multi-stage pore structure,strong hydrophilicity,low thermal conductivity,excellent light absorption,fast water transportation and salt-resistant crystallization as well as good durability.The evaporation rate without an additional device is found to be 1.689 kg m^(-2)h^(-1)under 1-Sun irradiation,and the energy conversion efficiency is as high as 95%.This work creates a platform and develops the prospect of employing green and sustainable natural-diatomite composite evaporator for practical applications of seawater desalination.

An Enhanced GAN for Image Generation

Generative adversarial networks(GANs)with gaming abilities have been widely applied in image generation.However,gamistic generators and discriminators may reduce the robustness of the obtained GANs in image generation under varying scenes.Enhancing the relation of hierarchical information in a generation network and enlarging differences of different network architectures can facilitate more structural information to improve the generation effect for image generation.In this paper,we propose an enhanced GAN via improving a generator for image generation(EIGGAN).EIGGAN applies a spatial attention to a generator to extract salient information to enhance the truthfulness of the generated images.Taking into relation the context account,parallel residual operations are fused into a generation network to extract more structural information from the different layers.Finally,a mixed loss function in a GAN is exploited to make a tradeoff between speed and accuracy to generate more realistic images.Experimental results show that the proposed method is superior to popular methods,i.e.,Wasserstein GAN with gradient penalty(WGAN-GP)in terms of many indexes,i.e.,Frechet Inception Distance,Learned Perceptual Image Patch Similarity,Multi-Scale Structural Similarity Index Measure,Kernel Inception Distance,Number of Statistically-Different Bins,Inception Score and some visual images for image generation.

Influence of Magmatic Intrusion on Abnormal Hydrocarbon Generation and Expulsion of Source Rock:A Case Study of the Dongying Sag,Bohai Bay Basin

How gabbro affects the generation and expulsion of hydrocarbons in muddy surrounding rocks is clarified by analyzing thin section,major and trace elements,total organic carbon(TOC),pyrolysis,extracts and vitrinite reflectance data from source rocks in the Chunxi area the Dongying Sag,Bohai Bay Basin,eastern China.The results show that a magma intrusion brings copious heat to the source rocks,which promotes abnormal maturation of organic matter(OM)and rapid hydrocarbon generation.The CH_(4)and H_(2)produced by gabbro alteration play a role in hydrocarbon generation of source rocks.The hydrothermal process during magma intrusion provides many different minerals to the source rock,resulting in carbonate-rich surrounding mudstone.The carbonate and clay minerals produced by volcanic mineral alteration jointly catalyze the hydrocarbon generation of the source rock.The high-temperature baking of the intrusion results in hydrothermal pressurization and hydrocarbon generation pressurization,causing many fractures in the surrounding rock.The generated oil and gas are discharged through the fractures under diffusion and pressure.Mantle-derived CO_(2)is also conducive to the expulsion of hydrocarbons because of its strong enrichment capacity for hydrocarbons.