Diffusion Of Clusters Research Articles

Analysis of the Impact of Clusters on Productivity of Multi-Fracturing Horizontal Well in Shale Gas

Shale gas reservoirs with nanoporous media have become one of the primary resources for natural gas development. The nanopore diameters of shale reservoirs range from 5 to 200 nm, with permeability ranging from 1 × 10−9 to 1 × 10−6 μm2. The natural gas production from shale gas reservoirs is low, necessitating the use of multi-stage hydraulic fracturing in horizontal wells. Segmented multi-cluster perforation fracturing is an effective method for shale gas extraction in these wells. The number of clusters significantly impacts the productivity of horizontal wells. Therefore, it is essential to analyze the impact of cluster numbers on fracture productivity in shale gas reservoir development. In this study, the equivalent flow resistance method was applied to establish a productivity model for multi-stage hydraulic fracturing horizontal wells in shale gas reservoirs considering diffusion and slip. An approximate analytical solution was obtained, and the effects of cluster length, diffusion coefficient, and fracture network permeability on productivity were analyzed. The results show that gas production gradually increases with the increase in the number of clusters and cluster length. However, as the number of clusters increases, the interference between clusters leads to a decrease in the productivity of individual clusters. As the fracture permeability, fracture network permeability, and diffusion coefficient increase, shale gas production also gradually increases. The permeability of the fracture network has the greatest impact on productivity. These research results are beneficial for the design of clusters in horizontal well fracturing and are of great importance for the development and production of shale gas reservoirs.

Passivation of Deep‐Level Defects through Chemical Environment Regulation for Efficient CZTSSe Solar Cells Based on Active Selenium Adsorption–Desorption Process

AbstractInsufficient selenization and uneven distribution of elements caused by the poor diffusion and reaction activity of selenium clusters is one of the main issues limiting the efficiency of Cu2ZnSn(S,Se)4 (CZTSSe) solar cells. Here, this work designs a simple and feasible strategy to improve the activity of selenium (Se) by implementing high‐temperature treatment on graphite boxes loaded with Se pellets. The rapid adsorption/desorption characteristics of graphite on active gaseous small‐molecule selenium have successfully introduced hyperactive Se4, Se3, and Se2 into the selenization process. The results indicate that the adsorbed non‐toxic gaseous active Se3 and Se4 can quickly and uniformly diffuse into the precursor film at low temperatures, thereby inducing nucleation and grain growth at both surface and back interface simultaneously, which inhibits the upward migration and aggregation of cations, especially Cu, and promotes the homogenization of elements. The overall relatively Cu‐poor chemical environment suppresses the formation of CuZn defects and [2CuZn+SnZn] defect clusters, and also promotes the generation of favorable VCu. The band tail states and non‐radiative recombination are then optimized. Finally, the CZTSSe solar cells achieve a power conversion efficiency (PCE) of 14.5%, with VOC/VOCSQ of 67% being one of the highest in the literature.

Effect of grain boundaries on the helium degradation mechanisms of alloy 800H: A molecular dynamics study

Alloy 800H is currently used as structural material in light-water cooled nuclear reactors, and it is also considered as candidate materials for various advanced reactor designs. Under operation, the exposure of alloy 800H to neutron irradiation results in the formation of helium (He), mainly from Ni by the transmutation reaction (n, α). In this work, we present a molecular dynamics (MD) simulation study of the behavior and effects of He on the microstructure and mechanical properties of alloy 800H. Our results show that the population of clusters made of 5 to 9 He atoms is nearly constant throughout the simulation time (10 ns), while the population of larger clusters increases as the simulation time increases. The growth of clusters is controlled by either the dissociation and diffusion of smaller clusters towards nearby larger clusters or the merging of larger clusters initially located nearby to each other. A significant accumulation of He is observed at the grain boundaries (GB), while a depletion zone is found at the neighboring regions. As a result, the density of He cluster is significantly higher at the GBs as compared to the intra-granular regions. The nucleation and growth of He clusters also results in the formation of Frenkel pairs (FP), whose associated self-interstitial atoms (SIA) agglomerate into interstitial clusters in the alloy 800H matrix. As a consequence, dislocation segments, mostly of the Shockley type, are generated in the microstructure, and often located next to He clusters. The combination of the aforementioned defect structures and the high density of He clusters at the GBs results in a substantial degradation of the mechanical properties of alloy 800H single crystal and bicrystals.

Exploring defect behavior in helium-irradiated single-crystal and nanocrystalline 3C-SiC at 800°C: A synergy of experimental and simulation techniques

In this study, single crystal (sc) and nanocrystalline (nc) 3C-SiC samples were subjected to 30 keV He ion irradiation across various doses while maintaining a temperature of 800 °C. Employing techniques including Raman spectroscopy, transmission electron microscopy (TEM), and nanoindentation, the alterations in microstructure and hardness resulting from He irradiation with various fluences were examined.In sc-SiC, irradiation prompted the formation of He platelets, resulting in a hardness increase of 7 GPa. In contrast, nc-SiC, characterized by a higher stacking fault density, exhibited the formation of bubbles, primarily at grain boundaries (GBs), with fewer occurrences within the grain interior, leading to a hardness increase of 1 GPa. Notably, in both sc- and nc-SiC, hardness reached saturation and subsequently stabilized or declined with increasing He fluence.Through molecular dynamics (MD) cascade simulations, we discerned that various planar defects do not uniformly contribute to enhancing radiation resistance. For example, intrinsic stacking faults (ISF) and twins in SiC played a substantial role in altering defect density and configurations, thereby facilitating point defect annihilation. Conversely, extrinsic stacking faults (ESF) and Σ3 GBs had a limited impact on defect production during a cascade.Furthermore, calculations of cluster diffusivity revealed an accelerated movement of He-vacancy towards GBs compared to bulk material and other planar defects. Moreover, the scarcity of point defects and constrained mobility of He atoms towards stacking faults in nc-SiC elucidated the marked tendency of He to form platelets in sc-SiC.Additionally, our findings established a correlation between the calculated indentation hardness and the geometry of He defects, consistent with experimental results from nanoindentation. These results significantly contribute to ongoing efforts to design SiC materials with heightened radiation tolerance.

Particle topology-regulated relaxation dynamics in cluster-ordering.

The granular materials of soft particles (SPs) demonstrate unique viscoelasticity distinct from general colloidal and polymer systems. Exploiting dynamic light scattering measurements, together with molecular dynamics simulations, we study the diffusive dynamics of soft particle clusters (SPCs) with spherical and cylindrical brush topologies, respectively, in the melts of SPs. A topologically constrained relaxation theory is proposed by quantitatively correlating the relaxation time to the topologies of the SPCs, through the mean free space (Va) of tethered SPs in the cluster. The tethered SPs in SPCs are crowded by SPs of the melts to form the cage zones, and the cooperative diffusion of the tether SPs in the zones is required for the diffusive motion of SPCs. The cage zone serves as an entropic barrier for the diffusion of SP clusters, while its strength is determined by Va. Three characteristic modes can be confirmed: localized non-diffusive mode around critical Va, diffusive mode with Va deviating far from the critical value, and a sub-diffusive mode as an interlude between two limits. Our studies raise attention to the emergent physical properties of materials based on SPs via a topological design while opening new avenues for the design of soft structural materials.

A hybrid rate theory model of radiation-induced growth

Differences in the diffusivity of radiation-produced defects along zirconium’s prismatic and basal planes result in radiation-induced growth (RIG) and creep. The difference in anisotropy of diffusion (DAD) model explains this behavior by assuming vacancies and self-interstitial atoms (SIAs) possess dissimilar ratios of in-basal-plane vs out-basal-plane diffusion coefficients. The self-interstitial cluster diffusion (SICD) model was proposed as an alternative to DAD, and relates growth to the formation of self-interstitial clusters which diffuse solely along the 〈a〉 direction. Here, we propose a hybrid model, in which the contribution of both effects are considered, which provides additional flexibility as compared to DAD or SICD taken individually. In this model, both anisotropic diffusing point defects and 1-dimensional (1-D) glissile SIA clusters are considered, which are removed in the internal and boundary sinks. Thirty-two DAD, SICD and hybrid models are fitted to a set of thirty-two RIG experiments. Good agreement between DAD and the experiments can be reached only by assuming unrealistically large SIA bias factors. In 9 of the 32 scenarios, good agreement between SICD and experiments could not be reached, indicating that the model lacks flexibility. Good agreement between the hybrid model and the experiments can be reached using realistic SIA bias factors; however, it requires assuming considerably lower vacancy bias factors than those found by ab initio calculations aimed at mono-vacancies, suggesting small vacancy clusters and loops play a more important role in RIG than previously thought.

Evaluation of dimensionality reduction and unsupervised clustering methods in breast datasets

This paper delves into the issues related to handling high-dimensional data in massive datasets, such as computational challenges and uneven data distribution owing to diminished data point density. Various dimensionality reduction techniques such as Principal Component Analysis (PCA), Kernel Principal Component Analysis (KPCA), and Diffusion Maps are discussed and evaluated for their efficiency in extracting crucial data features. This aids in gaining a comprehensive understanding of the data. The study also examines unsupervised clustering methods like K-means, DBSCAN, and spectral clustering. By integrating these clustering methods with dimensionality reduction techniques, we aim to uncover potential synergies. The principles and methodology behind spectral clustering and unsupervised nonlinear diffusion learning are further dissected. Various datasets are employed to evaluate the efficiency of these techniques empirically. The final section of the paper comprises an evaluation of the clustering results and a discussion on potential avenues for future research.

Could breast multiparametric MRI discriminate between pure ductal carcinoma in situ and microinvasive carcinoma?

Ductal carcinoma in situ (DCIS) is often reclassified as invasive cancer in the final pathology report of the surgical specimen. It is of significant clinical relevance to acknowledge the possibility of underestimating invasive disease when utilizing preoperative biopsies for a DCIS diagnosis. In cases where such histologic upgrades occur, it is imperative to consider them in the preoperative planning process, including the potential inclusion of sentinel lymph node biopsy due to the risk of axillary lymph node metastasis. To assess the capability of breast multiparametric magnetic resonance imaging (MP-MRI) in differentiating between pure DCIS and microinvasive carcinoma (MIC). Between January 2018 and November 2022, this retrospective study enrolled patients with biopsy-proven DCIS who had undergone preoperative breast MP-MRI. We assessed various MP-MRI features, including size, morphology, margins, internal enhancement pattern, extent of disease, presence of peritumoral edema, time-intensity curve value, diffusion restriction, and ADC value. Subsequently, a logistic regression analysis was conducted to explore the association of these features with the pathological outcome. Of 129 patients with biopsy-proven DCIS, 36 had foci of micro-infiltration on surgical specimens and eight were diagnosed with invasive ductal carcinoma (IDC). The presence of micro-infiltration foci was significantly associated with several MP-MRI features, including tumor size (P <0.001), clustered ring enhancement (P <0.001), segmental distribution (P <0.001), diffusion restriction (P = 0.005), and ADC values <1.3 × 10-3 mm2/s (P = 0.004). Breast MP-MRI has the potential to predict the presence of micro-infiltration foci in biopsy-proven DCIS and may serve as a valuable tool for guiding therapeutic planning.

Dual Contrast-Driven Deep Multi-view Clustering.

Consensus representation learning is one of the most popular approaches in the field of multi-view clustering. However, most of the existing methods cannot learn discriminative representations with a clustering-friendly structure since these methods ignore the separation among clusters and the compactness within each cluster. To tackle this issue, we propose a new deep multi-view clustering network with a dual contrastive mechanism to learn clustering-friendly representations. Specifically, our method employs dual contrasting losses: a dynamic cluster diffusion loss to maximize the distance between different clusters and a reliable neighbor-guided positive alignment loss to enhance compactness within each cluster. Our approach includes several key components: view-specific encoders to extract high-level features from each view, and an adaptive feature fusion strategy to obtain consensus representations across multiple views. The dynamic cluster diffusion module ensures inter-cluster separation by maximizing distances between different clusters in the consensus feature space. Simultaneously, the reliable neighbor-guided positive alignment module improves within-cluster compactness through a pseudo-label and nearest neighbor structure-driven contrastive loss. Experimental results on several datasets show that our method can acquire clustering-friendly representations with both good properties of inter-cluster separation and within-cluster compactness, and outperforms the existing state-of-the-art approaches in clustering performance.

Migration of zeolite-encapsulated subnanometre platinum clusters via reactive neural network potentials.

The migration of atoms and small clusters is an important process in sub-nanometre scale heterogeneous catalysis, affecting activity, accessibility and deactivation through sintering. Control of migration can be partially achieved via encapsulation of sub-nanometre metal particles into porous media such as zeolites. However, a general understanding of the migration mechanisms and their sensitivity to particle size and framework environment is lacking. Here, we extend the time-scale and sampling of atomistic simulations of platinum cluster diffusion in siliceous zeolite frameworks, by introducing a reactive neural network potential of density functional quality. We observe that Pt atoms migrate in a qualitatively different manner from clusters, occupying the dense region of the framework and avoiding the free pore space. We also find that for cage-like zeolite CHA there exists a maximum in self diffusivity for the Pt dimer beyond which, confinement effects hinder intercage migration. By extending the quality of sampling, NNP-based methods allow for the discovery of novel dynamical processes at the atomistic scale, bringing modelling closer to operando experimental characterization of catalytic materials.

Agglomeration inhibition mechanism of SiO2 in the Ca(OH)2/CaO thermochemical heat storage process: A reactive molecular dynamics study

As a promising thermochemical heat storage material, the Ca(OH)2/CaO can be agglomerated after repeated heat storage cycles, which will affect its performance severely. Experimental evidence indicated that the dopant of SiO2 particles alleviated the agglomeration phenomenon of Ca(OH)2/CaO. However, the mechanisms have not been clarified in theory, including the agglomeration of Ca(OH)2/CaO and the agglomeration inhibition of SiO2. Investigating these mechanisms is important for the broad application of Ca(OH)2/CaO thermochemical heat storage technology. In this paper, reactive molecular dynamics is employed to simulate and quantify the agglomeration of Ca(OH)2 and the inhibition by SiO2 during the heat storage process. Then, the agglomeration phenomena under the two processes are quantitatively described. The mechanisms are analyzed by using the parameters of kinetics and thermodynamics. From the phenomenological analysis, smaller size changes, larger mass center distances, and smaller atomic displacements indicate that SiO2 doping reduces the tendency of nanoparticles to agglomerate into a larger cluster. From the kinetic perspective, the diffusion of pure Ca(OH)2 clusters conforms to viscous flow, while the diffusion of the SiO2-doped clusters is close to surface diffusion. The dopant of SiO2 increases the activation energy of the diffusion process, which would increase the atomic mobility resistance and thus inhibit the agglomeration. From the thermodynamic perspective, cluster agglomeration is mainly caused by the work done by van der Waals forces, which leads to a decrease in potential energy, especially surface potential energy. By doping SiO2, the interatomic van der Waals forces are increased, which lowers the stable potential energy surface of the clusters, thereby obtaining a more stable structure to inhibit agglomeration.

Size-dependent diffusion of 3D nanovoids in a bcc solid

We studied the diffusion of 3D nanovoids in a bcc solid by kinetic Monte Carlo simulations. The diffusion coefficient as a function of the void size increases, reaches a maximum, and then decreases. The first increase is particularly interesting, as the diffusion of clusters is generally considered a decreasing function of the cluster size. We attribute this behavior to a curvature-dependent energy barrier for mass transport. We propose an analytical modeling of the void diffusion coefficient that reproduces the simulation data over the whole size range. In addition, for low temperatures and small sizes, the void diffusion coefficient vs size displays valleys, i.e., regions where the diffusion coefficient is smaller than the general trend. This behavior cannot be explained with analytical developments and is due to the formation of compact shapes for certain magic void sizes. In these shapes, the atoms at the void surface are strongly bound, displace less, and thus also void diffusion is slower.

Preparation of a Stable Superhydrophobic Mortar Surface Using a One-Step Method.

Research efforts have intensified on developing superhydrophobic surfaces on concrete structures to limit the damage caused by the natural porosity and hydrophilicity of cementitious materials. However, the feasibility idea is impeded by the complex preparation process and weak adhesion/stability performance. Therefore, superhydrophobic coatings were rapidly prepared on mortar surfaces by a straightforward and effective one-step method using zinc oxide (ZnO) modified with stearic acid and epoxy resin. The microstructure, physical/chemical properties, hydrophobic properties, and stability of the coatings were systematically investigated. The experimental results showed that the water contact angle of the samples reached a maximum value of 164.2° and a sliding angle of 4.6° when the stearic acid content was 0.5 g. The water absorption of the coated superhydrophobic mortar was reduced by approximately 61% compared to that of ordinary mortar, and neither particulate pollutants nor liquid pollutants could contaminate the superhydrophobic mortar. The coating maintained a superhydrophobic state and exhibited good physical durability after sandpaper abrasion, tape peeling, and high-temperature resistance tests. The water cluster diffusion coefficient on surface of the ordinary coating was 0.1645 × 10-4 and 0.0328 × 10-4 cm2/s on surface of the modified coating after molecular dynamics simulation.

Anisotropic diffusion of radiation-induced self-interstitial clusters in HCP zirconium: A molecular dynamics and rate-theory assessment

Under irradiation, Zr and Zr alloys undergo growth in the absence of applied stress. This phenomenon is thought to be associated with the anisotropy of diffusion of either or both radiation-induced point defects and defect clusters. In this work, molecular dynamic simulations are used to study the anisotropy of diffusion of self-interstitial atom clusters. Both near-equilibrium clusters generated by aggregation of self-interstitial atoms and cascade-induced clusters were considered. The cascade-induced clusters display more anisotropy than their counterparts produced by aggregation. In addition to 1-dimensional diffusing clusters, 2-dimensional diffusing clusters were observed. Using our molecular dynamic simulations, the input parameters for the “self-interstitial atom cluster bias” rate-theory model were estimated. The radiation-induced growth strains predicted using this model are largely consistent with experiments, but are highly sensitive to the choice of interatomic interaction potential.

Thin-Film Rheology and Tribology of Imidazolium Ionic Liquids.

Ionic liquids (ILs) are organic molten salts with low-temperature melting points that hold promise as next-generation environmentally friendly boundary lubricants. This work examines the relationship between tribological and rheological behavior of thin films of five imidazolium ILs using a surface force apparatus to elucidate lubrication mechanisms. When confined to films of a few nanometers, the rheological properties change drastically as a function of the number of confined ion layers; not only the viscosity increases by several orders of magnitude but ILs can also undergo a transition from Newtonian to viscoelastic fluid and to an elastic solid. This behavior can be justified by the confinement-induced formation of supramolecular clusters with long relaxation times. The quantized friction coefficient is explained from the perspective of the strain relaxation via diffusion of these supramolecular clusters, where higher friction correlates with longer relaxation times. A deviation from this behavior is observed only for 1-ethyl-3-methylimidazolium ethylsulfate ([C2C1Im][EtSO4]), characterized by strong hydrogen bonding; this is hypothesized to restrict the reorganization of the confined IL into clusters and hinder (visco)elastic behavior, which is consistent with the smallest friction coefficient measured for this IL. We also investigate the contrasting influence of traces of water on the thin-film rheology and tribology of a hydrophobic IL, 1-ethyl-3-methylimidazolium tris(pentafluoroethyl)trifluorophosphate, [C2C1Im][FAP], and a hydrophilic IL, [C2C1Im][EtSO4]. [C2C1Im][EtSO4] remains Newtonian under both dry and humid conditions and provides the best lubrication, while [C2C1Im][FAP], characterized by a prominent solid-like behavior under both conditions, is a poor lubricant. The results of this study may inspire molecular designs to enable efficient IL lubrication.

ATP Acts as a Hydrotrope to Regulate the Phase Separation of NBDY Clusters.

Nonannotated P-body dissociating polypeptide (NBDY) is a recently discovered human microprotein that has been found to be a novel component of the mRNA decapping complex. Previous studies have shown that the phosphorylation of NBDY promotes the liquid phase of the NBDY remixing in vitro. Typically, during the process of phosphorylation, a phosphate group is added to the protein through adenosine triphosphate (ATP) hydrolysis. It has been shown that ATP acts as a biological hydrotrope, affecting the phase separation of proteins in solution. In this study, we utilized simulation methods to investigate the dynamic properties of the NBDY clusters at various ATP concentrations. Our findings demonstrate that ATP can regulate the phase separation of NBDY clusters. Specifically, we identified a critical point in the concentration ratio between ATP and NBDY that exhibits a dual effect on the phase separation of NBDY. We observed that the nonsaturated ATP concentration can facilitate the formation of phase separation, while oversaturated ATP concentration promotes the diffusion of NBDY, and the oversaturated ATP-NBDY interaction impedes the phase separation of NBDY. Additionally, we found that ATPs can bind to the protein surface by aggregating into ATP clusters, which further hinders the diffusion of NBDY clusters. Our work provides general insight into the role of ATP in the phase separation of protein condensates.

Hyperactive Selenium Source Yields Kesterite Solar Cells with 12.86% Efficiency

AbstractOne of the main issues that limits the efficiency of kesterite solar cells is the low diffusion and chemical activity of selenium clusters. Here, this work proposes a simple and effective pre‐selenization strategy using Na2(Se2S) solution, which enables the direct introduction of hyperactive Se2, Se3, and Se4 into the precursors. The results demonstrate that Se2, Se3, and Se4 promote the formation of the Cu2‐xSe liquid phase and enhance the diffusion of elements from different micro‐regions. Consequently, the ratios of Cu/(Zn + Sn) and Sn/Zn in different micro‐regions of the absorber are controlled within the optimal range, exhibiting reduced fluctuations. The controlled environment suppresses the formation of CuZn and SnZn defects, as well as [2CuZn + SnZn] defect clusters. Finally, a power conversion efficiency (PCE) of 12.86% is achieved, which is the highest PCE for kesterite solar cells made under ambient pressure and with N,N‐dimethylformamide solvent.

Diffusion of small anti-Schottky clusters in UO2

The aggregation of irradiation-induced defect clusters in UO2 leads to the formation of dislocation loops and cavities which contribute swelling and ultimately produce deleterious effects on the fuel. Despite their fundamental role in fuel evolution, the kinetics of these defect clusters are not currently well understood. In this work, we investigate the diffusive behavior of interstitial clusters via Molecular Dynamics (MD) simulations. Our investigation considers a range of defect cluster sizes, each composed of N UO2 units or anti-Schottky defects. We report a complex cluster size - mobility relation; increasing cluster size corresponds to an increase in mobility up until a critical size (N=4) where diffusivity reaches a maximum, after which the trend reverts and further increasing size corresponds to a decrease in mobility. The rapid migration observed correlates well with the very low barriers reported in a few historic experimental studies. Further analysis of the cluster shape and orientation reveals a strong structural preference for near-planar configurations oriented normal to the 〈100〉 direction. Rotational energy barriers are found to be similar in both magnitude and trend to the observed diffusion barriers. Lastly, connections are drawn to the behavior of large defect clusters including the formation of (1/3)〈111〉 Frank dislocation loops.

The green to circular bioeconomy transition: Innovation and resilience among Italian enterprises

AbstractThe bioeconomy is an essential part of the transition from a linear to a circular production model, which repositions production systems to favour sustainability and innovation. There are two main contributors to the development of this process: the choices made by public decision‐makers (national and European), and the strategies adopted by companies and the recent increased diffusion of new business networks and clusters in Europe, which favour industrial and technological transformation processes. We move from analysis of the international context and how Italy's positioning in the new circular bioeconomy is evolving to focus on the diffusion of business clusters and networks in Europe linked to the circular bioeconomy to propose a preliminary empirical study of corporate behaviour. For this we use survey data on Italian bio‐industries that are part of the cluster SPRING (Sustainable Processes and Resources for Innovation and National Growth). The results show how SPRING cluster firms are pushed to improve their positioning in numerous fields characterizing the most innovative business models.

Diffusion Characteristics of Clusters of Self-Interstitial Atoms in Vanadium: Molecular Dynamics Data

The temperature dependences of diffusion characteristics of the irradiation-induced defects, namely, clusters of self-interstitial atoms (SIAs) containing up to five atoms, in bcc V (vanadium) have been studied by the method of molecular dynamics in the temperature range of 300–1000 K. The diffusion characteristics include the coefficient of diffusion, the tracer correlation factor, the average displacement before changing the direction of migration, and the frequency of changing the direction of migration. The values of the activation energy of diffusion and the activation energy of changing the direction of migration for the considered types of defects in different temperature ranges have been determined. The dependences of the mechanism of (1D vs 3D) diffusion of SIA clusters on the temperature and cluster size and their possible influence on the parameters of phenomenological models of changes in the microstructure of a material under irradiation (sink strengths of spherical absorbers) are discussed.