Multiscale Model Research Articles

Dynamic analysis of HCV infection and drug resistance using an age-structured multiscale model

Direct-acting antiviral agents (DAAs) are known to interfere with various intracellular stages of the hepatitis C virus (HCV) life cycle and have demonstrated efficacy in treating HCV infection. However, DAA monotherapy can lead to drug resistance due to mutations. This paper explores the impact of DAA therapy on HCV dynamics using a multiscale age-structured partial differential equation (PDE) model that incorporates intracellular viral RNA replication within infected cells and two strains of viruses representing a drug-sensitive strain and a drug-resistant mutant variant, respectively. We derived an equivalent ordinary differential equation (ODE) model from the PDE model to simplify mathematical analysis and numerical simulations. We studied the dynamics of the two virus strains before treatment and investigated the impact of mutations on the evolution kinetics of drug-sensitive and drug-resistant viruses, as well as the competition between the two strains during treatment. We also explored the role of DAAs in blocking HCV RNA replication and releasing new virus particles from cells. During treatment, mutations do not significantly influence the dynamics of various virus strains; however, they can generate low-level HCV that may be completely inhibited due to their poor fitness. The fitness of the mutant strain compared to the drug-sensitive strain determines which strain dominates the virus population. We also investigated the prevalence and drug resistance evolution of HCV variants during DAA treatment.

The Protection and Management of Wapiti in Desert Oases: Bare Land Poses a Limitation to Wapiti Conservation.

The Helan Mountains, situated in the heart of the desert, act as a dividing line between China's arid and semi-arid zones. Often referred to as a "desert oasis", they create an ecological island with a uniquely distinctive geographical location, making this area a focal point of contemporary research. Ungulates play a critical role in this ecosystem. The Alashan wapiti (Cervus canadensis alashanicus), an isolated population of China's smallest wapiti (Cervus canadensis) subspecies, is found exclusively within the Helan Mountains Nature Reserve. The conservation of this isolated population is fraught with challenges, particularly during winter, the harshest season for northern ungulates. Winter habitats are crucial for ensuring population stability. Therefore, we used certain methods, such as factor screening and model parameter optimization to assess habitat suitability using multi-scale species distribution models. The optimized results show that suitable habitats overlap with areas of high vegetation coverage in the Helan Mountains, covering just 588.32 km2, which is less than a quarter of the reserve's total area. The bare land area and winter NDVI are the two primary factors influencing habitat suitability, with other factors having minimal impact, underscoring the critical importance of food resources for the Alashan wapiti. The limited availability of these resources poses significant conservation challenges. Our findings provide a more precise foundation for targeted habitat protection and restoration efforts. We recommend enhancing the protection and restoration of food resources, effectively conserving vegetated areas, and preventing desertification.

Multi-scale hybrid modeling of plant growth in response to environmental conditions and soil nutrients availability

Crop modeling plays a crucial role in agriculture, aiding our understanding and prediction of crop growth and yield in diverse environmental conditions. This study aims to develop a comprehensive mathematical model describing plant growth in response to environmental conditions and soil nutrient availability. To achieve this, we relied on a field experiment with lettuce plants under varying environmental conditions. Employing growth models such as logistic, Gompertz, Aikman & Scaife, and Scaife, Cox & Morris, we assessed the influence of time, day-degrees, and effective-day-degrees across different plant densities and during distinct periods throughout the year. In general, describing plant growth in terms of day-degrees or effective-day-degrees yielded an improved model fit and more precise estimations of growth parameters. As a result, we described the growth of plant length in terms of effective-day-degrees instead of time in the equations of the Bessonov-Volpert system. Additionally, we modified the equation describing plant length growth using previously fitted functions. By incorporating these adjustments, we characterized the one-dimensional growth of plant weight under varying environmental conditions without branching, using the Bessonov-Volpert model. This study contributes valuable insights into crop modeling techniques, refining our understanding of optimizing plant growth under different environmental conditions.

Quantitative evaluation of the effect of Circle of Willis structures on cerebral hyperperfusion: A multi-scale model analysis

Cerebral hyperperfusion occurs in some patients after superficial temporal artery–middle cerebral artery bypass surgery. However, there is uncertainty about cerebral hyperperfusion after bypass for patients with different Circle of Willis (CoW) structures.This study established a lumped–parameter model coupled with one–dimensional model (0–1D), whilst a deep learning model for predicting pressure drop (DLM–PD) caused by stenosis and a cerebral autoregulation model (CAM) were introduced into the model. Based on this model, 9 CoW structural models before and after bypass were constructed, to investigate the effects of different CoW structures on cerebral hyperperfusion after bypass. The model and the results were further validated by clinical data.The MSE of mean flow rates from 0–1D model calculation and from clinical measurement was 1.4%. The patients exhibited hyperperfusion in three CoW structures after bypass: missing right anterior segment of the anterior cerebral artery (mRACA1) (13.96% hyperperfusion); mRACA1 and foetal–type right anterior segment of posterior cerebral artery (12.81%), and missing anterior communicating artery and missing left posterior communicating artery (112.41%). The error between the average flow ratio from the model calculations and from clinical measurements was less than 5%.This study demonstrated that the CoW structure had a significant impact on hyperperfusion after bypass. The general 0–1D model coupled with DLM–PD and CAM proposed in this study, could accurately simulate the hemodynamic environment of different CoW structures before and after bypass, which might help physicians identify high–risk patients with hyperperfusion before surgery, and promote the development of non–invasive diagnosis and treatment of cerebrovascular diseases.

Study on the mechanical behavior of millennium-ancient bricks based on microscopic characteristics

The wide performance variation and inability for destructive testing make accurately determining ancient brick mechanical properties crucial for assessing structural safety in ancient masonry. This paper uses multiscale modeling with microscopic finite element analysis to quantitatively assess ancient brick macroscopic stress–strain relationship and compressive strength based on microstructural characteristics. The basic parameters such as porosity, pore size distribution, mineral composition and volume ratio, and microstructure characteristics of ancient bricks were obtained by corresponding methods. The Mori-Tanaka homogenization theory was applied to derive a three-phase equivalent brick matrix using elastic moduli and Poisson’s ratios of quartz, kaolinite, and montmorillonite at the microscale. The Representative Volume Element model with pores was created based on this matrix and pores, integrating Drucker-Prager plastic damage for finite element analysis of ancient brick mechanical behavior. The results indicate that stress–strain curves from the multi-scale micromechanics model resemble macroscopic experimental curves and maintain consistent peak strength. This shows that combining microscopic testing with finite element simulation for analyzing the mechanical properties of ancient bricks is feasible, providing a new non-destructive means of obtaining these properties.

Multi-scale modeling for low-temperature sealing performance of fuel cell

A unit cell of proton exchange membrane fuel cell (PEMFC) contains a special ‘sandwich’ sealing structure composed of membrane electrode assembly (MEA), the metal bipolar plate (BPP), and a rubber gasket, which is easily influenced by external impacts, vibrations, or temperature loads. In particular, PEMFCs have to go through low-temperature loads in using environments making the lifetime and reliability of the PEMFC reduced. The temperature-shrinkage at the macro-scale and the morphology change at the micro-scale of the rubber gasket are both going to appear if the temperature is low and the PEMFC sealing performance is going to be obviously affected. The macroscopic temperature-shrinkage which includes the size-variation and the spring-back weakening is considered to establish the actual compression ratio model mathematically for low-temperature sealing of rubber gaskets in this paper. Then the influence of gasket microscopic morphology on contact behaviors is studied to reveal the interfacial sealing mechanism by using numerical simulation methods. Finally, the multi-scale model for low-temperature sealing performance of PEMFC has been constructed in this paper. The accuracy of the multi-scale model could be verified by comparing experimental contact pressure results between the rubber gasket and the metal plate.

Numerical simulation of a laser beam welding process: From a thermomechanical model to the experimental inspection and validation

In laser beam welding (LBW) complex physical phenomena occur during laser-material interaction, which prolong the parameterisation of the process due to the extended trial and error tests. Therefore, predicting the process behaviour leads to a more productive and cost-reduced process experimental tuning. Besides, as LBW is a thermal process, part distortion plays a relevant role in the final result, and hence, thermal and mechanical analysis are both required. In view of this, in the present research, a novel multiscale numerical model, capable of predicting the thermomechanical behaviour of the LBW process has been developed. The significance of the model is its capability of forecasting the part distortion and thermal field employing two fully coupled modules, where the laser heat source automatically adapts to the welding regime without the need to consider the melt pool dynamics and at a low computational cost. The local model determines the melt pool dimensions and thermal field. Besides, the second module, the global model, figures out the part distortion based on the thermal results. Finally, the presented numerical simulations are experimentally validated with the corresponding temperature monitoring during the welding process, the posterior metallography inspection, and the part deformation measurement. The results present a high accuracy, with a maximum error below 10 % at the temperature measurements, an average dimensional deviation of 0.14 mm and 0.18 mm respectively for the weld bead depth and width, and a vertical deformation average error of 0.15 mm.

Multiscale modeling of heat and mass transfer in dry snow: influence of the condensation coefficient and comparison with experiments

Abstract. Temperature gradient metamorphism in dry snow is driven by heat and water vapor transfer through snow, which includes conduction/diffusion processes in both air and ice phases, as well as sublimation and deposition at the ice–air interface. The latter processes are driven by the condensation coefficient α, a poorly constrained parameter in the literature. In the present paper, we use an upscaling method to derive heat and mass transfer models at the snow layer scale for values of α in the range 10−10 to 1. A transition α value arises, of the order of 10−4, for typical snow microstructures (characteristic length ∼ 0.5 mm), such that the vapor transport is limited by sublimation–deposition below that value and by diffusion above it. Accordingly, different macroscopic models with specific domains of validity with respect to α values are derived. A comprehensive evaluation of the models is presented by comparison with three experimental datasets, as well as with pore-scale simulations using a simplified microstructure. The models reproduce the two main features of the experiments: the non-linear temperature profiles, with enhanced values in the center of the snow layer, and the mass transfer, with an abrupt basal mass loss. However, both features are underestimated overall by the models when compared to the experimental data. We investigate possible causes of these discrepancies and suggest potential improvements for the modeling of heat and mass transport in dry snow.

Framework for electrochemical-mechanical behavior of all-solid-state batteries: From the reconstruction method to multi-physics and multi-scale modeling

All-solid-state batteries (ASSBs) are high-energy, high-power batteries. To enhance the understanding of the electrochemical-mechanical behavior in ASSBs across different scales, we developed a multi-physics and multi-scale modeling framework. This framework incorporates elastoplastic finite deformation and electrode microstructures of ASSBs, and the role of gradient plasticity in the governing equation for multiple physical fields was discussed. Utilizing X-ray computed tomography, we reconstructed the microstructure through a machine learning (ML)-informed image segmentation process. Our study clarifies the impact of electrode microstructures on concentration, stress, voltage, delamination and buckling from AM to electrode scale. Comparative analysis of the Feret diameter distribution of active materials (AMs) shows that ML-informed image segmentation outperforms two traditional segmentation methods. We observed that the asynchronous diffusion saturation of AMs, varying in shape and size, significantly influences the electrochemical-mechanical behavior of ASSBs, resulting in complicated debonding indices and J-integral distribution at the interface. The proposed upscaling homogenization procedure is demonstrated to be efficient for buckling analysis, with the shape mode closely matching existing experimental observations. These results shed light on the critical multi-physics and multi-scale coupling mechanisms in ASSBs.

PCG-based exercise fatigue detection method using multi-scale feature fusion model

Accurate detection of exercise fatigue based on physiological signals is vital for reasonable physical activity. Existing studies utilize widely Electrocardiogram (ECG) signals to achieve exercise monitoring. Nevertheless, ECG signals may be corrupted because of sweat or loose connection. As a non-invasive technique, Phonocardiogram (PCG) signals have a strong ability to reflect the Cardiovascular information, which is closely related to physical state. Therefore, a novel PCG-based detection method is proposed, where the feature fusion of deep learning features and linear features is the key technology of improving fatigue detection performance. Specifically, Short-Time Fourier Transform (STFT) is employed to convert 1D PCG signals into 2D images, and images are fed into the pre-trained convolutional neural network (VGG-16) for learning. Then, the fusion features are constructed by concatenating the VGG-16 output features and PCG linear features. Finally, the concatenated features are sent to Support Vector Machines (SVM) and Linear Discriminant Analysis (LDA) to distinguish six levels of exercise fatigue. The experimental results of two datasets show that the best performance of the proposed method achieves 91.47% and 99.00% accuracy, 91.49% and 99.09% F1-score, 90.99% and 99.07% sensitivity, which has comparable performance to an ECG-based system which is as gold standard (94.32% accuracy, 94.33% F1-score, 94.52% sensitivity).

Prediction of mechanical properties of rolled steel based on dual-attention multiscale convolutional neural network

The mechanical properties of carbon steel sheet directly depend on the chemical composition and process parameters of the steel. There is a complex nonlinear relationship between chemical composition, process parameters, and mechanical properties, and the establishment of a model with the ability to automatically distinguish the importance of different parameters, and to have good prediction accuracy and generalisation in different data sets is one of the important issues in the field of carbon steel mechanical properties prediction. In this paper, we proposed a multi-scale convolutional network model based on dual-attention mechanism (DAM-MSDSC), where the dual-attention module mines from features and channels at the same time, making the model focus on the important features. The multiscale feature fusion module effectively improves the model prediction accuracy by mining features from the output of the middle layer of the network to obtain features containing multiscale fusion features. In hot-rolled steel, for the two parameters of carbon content and temperature, it is confirmed that the change of carbon content has a greater impact on TS, while FTT and CT have a greater impact on YS. Through comparative experiments on different datasets, the proposed model had the best prediction performance, in which the correct rates of YS, TS, and EL are 98 %, 98.7 %, and 99.4 %, respectively, in cold rolled steel, and 97.8 %, 98.6 %, and 99.2 %, respectively, in hot rolled steel.

An investigation of dynamic droplet wetting within the smoothed dissipative particle dynamics (SDPD) multi-scale modeling framework

The multi-scale numerical procedure proposed in our previous work based on smoothed dissipative particle dynamics (SDPD) is employed, and a new multi-phase interaction model based on the inter-particle force (IPF) that includes a consistent repulsion force is presented and verified. A comparative investigation utilizing smoothed particle hydrodynamics (SPH), SDPD, and our multi-scale methods is then carried out and the computational efficiency, droplet morphology, wetting flow field, and advantages of the multi-scale method are demonstrated. In addition, the droplet thickness is derived from the Navier–Stokes equations with a stochastic force, demonstrating the effect of thermal fluctuations on the mesoscopic scale. Finally, the wetting states are simulated at different surface roughness values, and the transition of states and some new mechanisms are clarified. When the roughness scale is smaller than the interaction range between particles, the wetting state may change significantly, and this effect becomes weaker when the roughness scale exceeds the interaction range. The results also show that the horizontal roughness (direction of droplet spreading) is more decisive than vertical one (perpendicular to the direction of droplet spreading), usually leading to a transition of the wetting states, while the vertical roughness usually plays a reinforcing role.

High-Order Models for Gas Transport in Multiscale Coal Rock.

The coal reservoirs exhibit great heterogeneity and strong anisotropy in multiscale pore/fracture structures. Developing highly accurate multiscale models for real-time prediction of microgaseous flow in complicated porous media with pronounced contrast in transport coefficients is crucial but not yet available, which is time-consuming, expensive, and even computationally impossible. In this study, a multiscale approximate solution of the gas flow and pressure field is derived in this paper for predicting coalbed methane (CBM) transport in macro-microscopic two-scale porous media of typical coal rocks in Guizhou Province, China, and the detailed finite element algorithm is established. The multiscale approximate solutions are constructed from the first- and second-order auxiliary cell functions and low- or high-order derivatives of the homogenized pressure field, which can accurately approximate the solution of the original problem to a certain extent. The numerical accuracy and validity of the multiscale approximate solutions under various working conditions are analyzed in detail by comparing and verifying the results with the direct numerical simulation results of fine-mesh high-precision finite elements. The results show that the homogenized approximate solution can only roughly capture the characteristics of the macroscopic pressure evolution, the first-order approximate solution can partially reproduce the macro-microscopic pressure oscillation phenomenon, and the second-order approximate solution can accurately predict the pressure profile and its evolution law with time in porous media with macro-micro configuration under various complex conditions. The second- and high-order two-scale approximate solutions constructed in this paper exhibit high numerical accuracy and excellent computational efficiency. We also develop multiscale approximate solutions for predicting microgaseous flow at a low reservoir pressure where the slippage effects are very pronounced. Potential applications of our high-order models to the coal reservoirs with a hierarchical matrix and fractures are discussed. The developed multiscale models exhibit excellent applicability to investigating the crossflow effects and coupling mechanisms between the matrix and cleats or fractures with multiple configurations in the CBM and shale gas reservoirs, which is crucial for the effective implementation of hydraulic fracturing technology. This study provides a fundamental understanding of gas transport in multiscale matrix-fracture networks, which helps to improve the optimization design of hydraulic fracturing in tight reservoirs.

Tensile performance prediction of CFRPs with voids using multiscale analysis and neural networks

The overall work provides a novel multiscale modeling strategy applicable to the mechanical response prediction of arbitrarily laid porous laminates under uniaxial tension by establishing representative defective volumes (RDVs) for the inter-ply and intra-ply regions. A representative defective volume modeling method based on void characterization results using X-ray computed tomography for the interlaminar region was proposed. Elastic properties, tensile strength, and fracture energies of porous and poreless microscale models were obtained and used in the macroscale model. Defective properties were assigned to partial elements in the macroscale model according to the preset distribution mode. The Extended Finite Element Method was taken to simulate the failure process. Tensile properties of porous laminates with stacking sequences of [90]8, [90/0/90/0]2S, and [45/90/-45/0]2S were experimentally tested and numerically simulated. The predicted tensile properties and crack propagation behavior coincide well with the test results. Besides, the effects of micro-voids and macroscopic void distribution were discussed in detail. Further, a back-propagation neural network was used to achieve a fast prediction of the tensile properties and save computational costs. The computation time (<1 s) greatly improved compared with multiscale modeling (>38 h). This work provides an efficient tool for composite performance prediction, design, and optimization.

A combined transport-defect evolution model of microstructure damage in silicon carbide induced by precise irradiation of focused helium ion beams

In this paper, a combined transport-defect evolution multiscale model describing the generation and the evolution of microstructure damage in silicon carbide (SiC) induced by focused helium ion beams is developed. In the proposed model, the transport of helium ions and displaced atoms in the SiC substrate and the generation of point defects are described by the Boltzmann transport equations, while the subsequent defect evolution is characterized by a set of rate equations with the contributions of the modeling of the bubble coalescence as well as the substrate swelling. The validity and superiority of the transport equations are verified by comparing the simulation results with the data from experimental measurements and available simulation methods. The subsurface amorphous profile, onsurface swelling profile, and the spatial and size distribution of helium bubbles in a SiC substrate irradiated by focused helium ion beams are simulated using the proposed multiscale model. The damage morphology simulated by the proposed model is in good agreement with the transmission electron microscopy images at different beam energies and doses. This work provides an effective tool for full-stage modeling of complex evolutionary mechanisms of microstructure damage induced by precise and high-throughput helium irradiation.

MSI-DTrans: A multi-focus image fusion using multilayer semantic interaction and dynamic transformer

Multi-focus image fusion (MFIF) aims to utilize multiple images with different focal lengths to fuse into a single full-focus image. This process enhances the realism and clarity of the resulting image. In this paper, a MFIF method called MSI-DTrans was proposed. On the one hand, in order to fully utilize all the effective information that the source image carries, the proposed method adopts a multilayer semantic interaction strategy to enhance the interaction of high-frequency and low-frequency information. This approach gradually mines more abstract semantic information, guiding the generation of feature maps from coarse to fine. On the other hand, a parallel multi-scale joint self-attention computation model is designed. The model adopts dynamic sense field and dynamic token embedding to overcome the performance degradation problem when dealing with multi-scale objects. This enables self-attention to integrate long-range dependencies between objects of different scales and reduces computational overhead. Numerous experimental results show that the proposed method effectively avoids image distortion, achieves better visualization results, and demonstrates good competitiveness with many state-of-the-art methods in terms of qualitative and quantitative analysis, as well as efficiency comparison. The source code is available at https://github.com/ouyangbaicai/MSI-DTrans.

Evaluation of the influence of climatic changes on the degradation of the historic buildings

While climatic change has been a widely studied topic, its impact on cultural heritage degradation remains a gap to overcome. The environment can contribute to the degradation of historic buildings and materials decay, especially temperature and humidity. So, characterizing the micro-climates of a landmark zone and understanding the influence of the recovery layers, building disposition, and street characteristics on environmental parameters are the first steps to investigating resilience strategies for heritage conservation and micro-climates. Thus, this paper presents a new approach to assess the influence of climatic warming on historic building degradation, combining multiscale experimental data and numerical modeling. The environmental parameters of the historic center of Aracati downtown, such as concentration of CO2, relative humidity, temperature, and air condition, were characterized and used together with urban volumetry to discuss the influence of on the urban microclimate and relate the data to the physical degradation of cultural heritage. An uncrewed aerial vehicle was used to register the volumetry of the historic center, and the data was used to simulate the wind conditions. Following this, numerical analysis was used to investigate the rising damp and thermal tension distributions under temperature variation over the years (from 2023 to 2100). The methodology’s applicability was demonstrated in recurrence with the Nosso Senhor do Bonfim Church, one of the most representative heritage constructions from Aracati downtown. Finally, the new methodology contributed to understanding how urban volumetry contributes to the climatic conditions of a historic area and demonstrates that the increase of the temperature can significantly affect the rising damps and the development of stress tension.

Multiscale modeling of catalyst deactivation in dry methane reforming

Dry methane reforming (DRM) presents a promising strategy to convert greenhouse gases (CH4 and CO2) into syngas, a valuable precursor for the production of long-chain alkanes. However, catalyst deactivation remains a major issue, primarily caused by the deep cracking of CH4 and CO2 as well as the metal-catalyzed growth of carbon whiskers. To address this challenge, we introduce a multiscale model that analyzes DRM reactions on the Ni surface and predicts whisker formation. The multiscale modeling approach integrates microscopic kinetic Monte Carlo (kMC) simulations for surface reactions, mesoscopic pellet-scale modeling for predicting whisker growth length and macroscopic modeling to consider packed bed porosity and pressure drop across the reactor. Further, by systematically introducing time-steppers using the gap-tooth scheme, we significantly improved the computational efficiency, reducing the computational time from 17 days to 6 h at the expense of minimal accuracy loss in predicting whisker length. Based on this, we assert that such a multiscale model enables the analysis of various operating conditions affecting catalyst deactivation and kinetics of surface reaction, aiding in the design and optimization of DRM process.

Numerical Assessment of Effective Elastic Properties of Needled Carbon/Carbon Composites Based on a Multiscale Method

Needled carbon/carbon composites contain complex microstructures such as irregular pores, anisotropic pyrolytic carbon, and interphases between fibers and pyrolytic carbon matrices. Additionally, these composites have hierarchical structures including weftless plies, short-cut fiber plies, and needled regions. To predict the effective elastic properties of needled carbon/carbon composites, this paper proposes a novel sequential multiscale method. At the microscale, representative volume element (RVE) models are established based on the microstructures of the weftless ply, short-cut fiber ply, and needled region, respectively. In the microscale RVE model, a modified Voronoi tessellation method is developed to characterize anisotropic pyrolytic carbon matrices. At the macroscale, an RVE model containing hierarchical structures is developed to predict the effective elastic properties of needled carbon/carbon composites. For the data interaction between scales, the homogenization results of microscale models are used as inputs for the macroscale model. By comparing these against the experimental results, the proposed multiscale model is validated. Furthermore, the effect of porosity on the effective elastic properties of needled carbon/carbon composites is investigated based on the multiscale model. The results show that the effective elastic properties of needled carbon/carbon composites decrease with the increase in porosity, but the extent of decrease is different in different directions.

Molecular insights into CO2 enhanced hydrocarbon recovery and its sequestration in multiscale shale reservoirs

CO2 huff-n-puff (HNP) is an attractive enhanced oil recovery (EOR) technique in shale development due to its low cost, high efficiency, and environmentally friendly feature. Molecular dynamics (MD) simulations have been widely used to investigate the mechanisms of CO2 HNP process. However, the process of CO2 HNP is highly complex, involving the CO2 adsorption and transport in multiscale porous media, while considering the effect of different hydrocarbon compositions of shale oil. In this work, we developed a novel multiscale model (nano + bulk) with the modified Eagle Ford shale oil and water as the reservoir fluid. We investigated the adsorption behaviors and transport dynamics of CO2 in the CO2 HNP process. We quantitatively evaluated the CO2 swelling effect and its miscibility with hydrocarbons. Our results show the preferential adsorption to the graphene wall is pentane < CO2 < octane. CO2 can effectively desorb the light and intermediate hydrocarbons from the graphene wall. In addition, CO2 has good swelling effect and miscibility with the light and intermediate components. Due to the large molecular diameter and the strong interactions with the nanopore wall, the heavy components tend to be trapped in the nanopore after the CO2 HNP process. However, CO2 has similar swelling effect on the light and heavy components, indicating that the heavy components in the bulk pore can be effectively swelled and produced. Different from the unstable diffusion edge in the inorganic nanopores, CO2 has a stable diffusion edge in the organic nanopores. From the adsorption perspective, CO2 has favorable sequestration performance in the inorganic nanopores in shale gas reservoirs. This work provides a comprehensive understanding of the CO2 HNP process and practical implications for CO2 sequestration in shale reservoirs.