Deposition Model Research Articles

Predictive Modeling of Laser Metal Deposition with Moving Mesh Theory: Impact of Secondary Laser Heat Sources on Track Morphology

Predictive Modeling of Laser Metal Deposition with Moving Mesh Theory: Impact of Secondary Laser Heat Sources on Track Morphology

Observation and kinetic modeling of carbon dioxide deposition under reduced pressures at cryogenic temperatures

Observation and kinetic modeling of carbon dioxide deposition under reduced pressures at cryogenic temperatures

Accounting for melting properties in the multi-solid paraffin prediction model for analyzing well production at fields in the Republic of Kazakhstan

The deposition of paraffin on pipeline walls is a serious flow issue. leading to a reduction in oil production due to the decreased flow cross-sectional area. and in severe cases. to complete blockage. This problem also results in increased energy consumption and causes failure of surface equipment due to paraffin plugs. Traditional methods predict paraffin crystallization temperature by considering the impact of temperature on the solubility parameters of individual components in both liquid and solid phases as well as their molar volumes. The goal of this study is to improve the prediction of paraffin deposition in Kazakhstan crude oil by incorporating an evaluation of melting properties into the multi-solid (MS) body prediction model. The process of calculating and adjusting melting properties to enhance the accuracy of the MS model is described in detail. This approach helps to overcome the limitations faced by most existing models when adapting to different types of crude oil. In this study, the focus is on optimizing the multi-solid (MS) body prediction model for paraffin deposition by integrating the melting properties of crude oil components. The improvement of the MS model is crucial because paraffin deposition is a significant issue in Kazakhstan's oil fields, where the wide range of reservoir conditions can cause standard prediction models to fall short. By enhancing the accuracy of melting point calculations, the updated model can better predict the onset of paraffin crystallization and its deposition tendencies under varying pressure and temperature conditions. Keywords: melting properties; multi solid solution; petroleum; solid solution; paraffin deposition

Comparison study of modified and classical Hamilton–Crosser models for electrophoretic and thermophoretic particle deposition in stagnation point flow of diamond –SiC–Co3O4/diathermic oil-based trihybrid nanofluid

Comparison study of modified and classical Hamilton–Crosser models for electrophoretic and thermophoretic particle deposition in stagnation point flow of diamond –SiC–Co3O4/diathermic oil-based trihybrid nanofluid

Decadal Response of Atmospheric Inorganic Nitrogen Dry Deposition into Offshore Areas to Policy Controls and Environmental Significance.

Accurately assessing the dry deposition fluxes of inorganic nitrogen aerosol (aerosol-IN) is crucial for mitigating the ecological damage caused by excessive nitrogen in oceanic equilibria. We developed a dry deposition model to assess the dry deposition fluxes of aerosol-IN into Chinese offshore areas over a decade, with the range of 2.81 × 106-1.89 × 107 g N km-2 year-1. Our findings showed a clear decline in the dry deposition fluxes of aerosol-IN following the implementation of environmental laws and policies. Promoting cleaner production, using new energy vehicles, the rational use of nitrogenous fertilizers, and standardized straw burning will all reduce pollution caused by excessive nitrogen deposition. High ratios of dry deposit to river transport nitrogen (0.48-17.89) indicate aerosol-IN is a significant source of inorganic nitrogen in Chinese offshore areas. The implementation of the policy has significantly reduced aerosol-IN deposition into the ocean, but its contribution remained substantial and should be considered when protecting the Chinese marine ecological environment.

Modeling the Influence of Deposition Parameters on the Crystalline Degree in the Simulation of Polycrystalline Silicon

Polycrystalline silicon (poly‐Si) has been and still is a pivotal material, particularly in the electronics and solar energy industries. Controlling crystallization is one of the challenges, e.g., in producing poly‐Si films for radio frequency applications. Since film growth by deposition is a random process, producing a specific grain size distribution for poly‐Si is challenging. By combining molecular dynamics simulation data with surface diffusion physics, novel transparent models are constructed that shed light on the physics behind the deposition of poly‐Si thin films and assist the selection of simulation parameters. Both probabilistic and geometric approaches are used to find relevant simulation parameters and their bounds to describe the complex grain–grain boundary interactions in the growth of poly‐Si thin films. Poly‐Si growth simulations provide valuable information to better understand the features of optimal growth conditions. The constructed parameterized deposition model is fitted to the simulation data. In addition to further refining the simulation of customized poly‐Si films, the presented modeling concept can also be used more generally in the analysis of physical vapor deposition.

Construction of the time since deposition (TsD) model in saliva stains with 16S rRNA full-length sequencing technology and microbial markers.

Determining the time since deposition (TsD) and sex of saliva stains is crucial for revealing the time of the crime's occurrence and clarifying the nature of the crime. This process not only shortens the time required to solve the case but also helps narrow down the scope of investigation, thereby enhancing the efficiency of case resolution. Currently, the forensic study of the microbial composition in long-term saliva stains remains a relatively underexplored field. The purpose of this study was to explore the succession pattern of long-placed human saliva stains microbial communities and identify relevant microbial markers for estimating TsD and identifying the sex of the donor, in order to be an effective alternative tool for solving practical forensic cases. Therefore, in this study, saliva stains exposed to indoor environmental conditions for up to 140days were collected and 16S rRNA full-length sequencing was performed using single-molecule real-time sequencing technology based on the PacBio sequencing platform. The study reveals that after 140days of placement, the relative abundance of Firmicutes significantly decreased (p = 0.00304). At the genus level, the relative abundances of Streptococcus (p = 0.0008), Rothia (p = 0.0448), Gemella (p = 0.016), and Veillonella (p = 0.0208) also significantly decreased. Additionally, significant differences were found in the microbial communities between saliva stains from males and females (p = 0.00013). Then, we constructed a TsD estimating model for microbial community markers based on random forest, and the results showed that the mean absolute error was 9.59days, and the accuracy of sex classification model based on stepwise logistic regression model and 4 bacterial markers was 84.21%. This indicates that saliva stains that have been in place for a long time still retain significant forensic value, and microbial markers can be used to determine the time since deposition (TsD) of dried saliva stains as well as to identify the sex of the donor.

Based on network pharmacology, the mechanism of Dioscin in alleviating renal tubular epithelial cell injury induced by calcium oxalate crystals was explored.

The commencement of kidney stone formation involves a crucial initial phase characterized by injury to renal tubular cells caused by calcium oxalate (CaOx). Dioscin (Dio) has been acknowledged for its potent anti-inflammation and anti-apoptotic properties; nevertheless, the impact and underlying Investigation into the molecular basis underlying the action of Dioscin in mitigating inflammation and apoptotic induced by exposure to calcium oxalate crystals in renal tissues remain unexplored.To comprehend the precise mechanism of Dioscin in the treatment of crystalline nephropathy, we conducted experiments utilizing a murine model of CaOx crystal deposition, induced by intraperitoneal administration of glyoxylate. An in vitro model was constructed using HK-2 cells exposed to calcium oxalate monohydrate (COM). To evaluate the effect of Dioscin on calcium oxalate crystal deposition by ROS assay, Western blotting, immunohistochemistry, Periodic Acid-Schiff staining (PAS) staining, hematoxylin-eosin (H&E) staining. Using network pharmacology and molecular docking methods, we explored the molecular mechanism of Dioscin in the treatment of CaOx-induced renal tubular epithelial cell injury. Subsequently, we conducted experiments to verify our findings.We observed a significant protective effect of Dioscin treatment against kidney oxidative stress and inflammation induced by CaOx. Then we predicted through network pharmacology that Dioscin exerts its anti-apoptotic effect through the NF-kappa B signaling pathway. Then we verified in vitro and in vivo that administration of Dioscin can alleviate the elevation of TLR4 and activation of the NF-kappa B signaling pathway induced by calcium oxalate, as well as attenuate renal apoptosis. Instead, the beneficial impact of this protection of Dioscin was reversed after overexpression of the TLR4.Dioscin has the potential to alleviate the activation of the NF-kappa B signaling pathway through TLR4, thereby exerting anti-inflammatory and anti-apoptotic effects. This study provides new ideas for the prevention and treatment of kidney stones.

Multi-Objective Trajectory Optimization for Rigid-Flexible Coupling Spray-Painting Robot Integrated with Coating Process Constraints

Robot-automated spraying is widely used in various fields, such as the automotive, metalworking, furniture, and aerospace industries. Spraying quality is influenced by multiple factors, including robot speed, acceleration, end-effector trajectory, and spraying process constraints. To achieve high-quality spraying under the influence of multiple factors, this study proposes a multi-objective optimization method for the spraying trajectory that integrates spraying process constraints into the optimization process. First, a 7-degree-of-freedom rigid-flexible coupling serial spray painting robot system is introduced, which includes a motion decoupling mechanism and a tension amplification mechanism. Subsequently, a paint deposition model for the spray gun was established, and the influence of process constraints on spraying quality was analyzed. Trajectory planning for the spray painting robot, based on the septic B-spline interpolation method, was then performed. Based on this foundation, objective functions and constraint equations for spraying trajectory optimization were established. A multi-objective trajectory optimization method for spraying by the robot is proposed based on the NSGA-II, which integrates the spraying process constraints. Finally, a prototype system of a 7-degree-of-freedom rigid-flexible coupling serial spray painting robot was constructed. Simulations and spraying experiments were conducted to verify the effectiveness of the proposed multi-objective trajectory optimization method. This paper presents a multi-objective optimization method for the spraying trajectory of a robot. In the proposed method, the optimized spraying trajectory is generated with the spraying process as the constraint and time, energy consumption, and impact during the spraying operation of the robot as the optimization objectives.

Quantitative Characterization Method of Additional Resistance Based on Suspended Particle Migration and Deposition Model

The phenomenon of pore blockage caused by injected suspended particles significantly impacts the efficiency of water injection and production capacity release in offshore oilfields, leading to increased additional resistance during the injection process. To enhance water injection volumes in injection wells, it is essential to quantitatively study the additional resistance caused by suspended particle blockage during water injection. However, there is currently no model for calculating the additional resistance resulting from suspended particle blockage. Therefore, this study establishes a permeability decline model based on the microscopic dispersion kinetic equation of particle transport. The degree of blockage is characterized by the reduction in fluid volume, and the additional resistance caused by particle migration and blockage during water injection is quantified based on the fluid volume decline. This study reveals that over time, suspended particles do not continuously migrate deeper into the formation but tend to deposit near the wellbore, blocking pores and increasing additional resistance. Over time, the concentration of suspended particles near the wellbore approaches the initial concentration of the injected water. An increase in seepage velocity raises the peak concentration of suspended particles, but when the seepage velocity reaches a certain threshold, its effect on particle migration stabilizes. The blockage location of suspended particles near the wellbore is significantly influenced by seepage velocity and time. An increase in particle concentration and size accelerates blockage formation but does not change the blockage location. As injection time increases, the fitted injection volume and permeability exhibit a power-law decline. Based on the trend of injection volume reduction, the additional resistance caused by water injection is calculated to range between 0 and 3.85 MPa. Engineering cases indicate that blockages are challenging to remove after acidification, and the reduction in additional resistance is limited. This study provides a quantitative basis for understanding blockage patterns during water injection, helps predict changes in additional resistance, and offers a theoretical foundation for targeted treatment measures.

Deposition of nanoparticles through a cylindrical tube of human respiratory system

This paper proposes a mathematical model for nanoparticle deposition in the respiratory bronchiole of the human lung airways. The length of the respiratory bronchiole of the human lung is 1.2 mm, and the diameter is 0.5 mm. Therefore, to investigate the impact of inhaled nanoparticles within the lung bronchioles, first, we used the Navier–Stokes equation by including the Darcy term together with Newton’s equation of motion. Then we non-dimensionalized the governing equation, and numerical solutions are obtained using the finite difference technique. Under the assumption of laminar pulsatile flow conditions and axial symmetry, the problem is simplified to a two-dimensional computational domain. The computational framework is implemented using MATLAB R2018 through user-defined code. Velocity propagation, the Reynolds number, and the Darcy number effect are performed to examine the flow conditions inside the respiratory bronchioles.

Phase diagram and boundary of impact outcomes

This study investigates the outcomes of droplet impact under varying impact velocities (Weber numbers) and surface wettability, using experimental methods, with a particular focus on the boundaries between these outcomes. Three primary outcomes were identified at lower Weber numbers (We ≤ 100): deposition, partial rebound, and complete rebound. Discrepancies were observed between existing boundary models for rebound (partial and complete) and deposition, and the actual experimental results. To address this, a new model was developed based on the “water spring model” proposed by Balance et al., incorporating the effects of contact line dissipation and adhesion forces. This model showed improved accuracy in predicting these outcomes. At higher Weber numbers (We > 100) and contact angles (θ > 100°), two main outcomes, receding breakup and prompt splash, were observed. By comparing the instability stresses driving these outcomes, a novel predictive boundary for receding breakup and prompt splash was proposed. This model's accuracy was validated through experimental data from phase diagrams. This research offers new insights into understanding droplet impact behavior under different surface wettability conditions.

Cell type specific contributions of mTOR inhibition that drive neuroprotective mechanisms in Alzheimer’s disease

AbstractBackgroundStudies investigating mTOR signaling provide compelling and reproducible evidence of the extension of lifespan across model organisms by treatment with the mTOR inhibitor rapamycin, and preclinical data suggests neuroprotective benefits of rapamycin in models of Alzheimer’s disease (AD). Rapamycin has potent immunosuppressive and autophagy activating effects though it remains unknown whether rapamycin’s neuroprotective and lifespan enhancing effects are achieved through modulating systemic inflammation, augmenting autophagy, or via some combination of modifying both these factors. Relatedly, the cellular and molecular mechanisms that contribute to rapamycin’s neuroprotective effects in AD remain unclear. The purpose of these studies is to identify the brain‐specific cell types that contribute to the protective effects of mTOR inhibition in progressive AD.MethodFor these studies we crossed a transgenic mouse model of familial early onset amyloid plaque deposition (5XFAD; JAX#034848) with a mouse genetically engineered with reduced mTOR expression to ∼25% of WT levels (mTORΔ/Δ). Previous studies have demonstrated that these mTOR hypomorph mice exhibit a 20% extension in lifespan. We then generated chimeric mice by crossing mTORΔ/Δ/5XFAD carriers to neuronal‐, microglial‐, and astrocyte‐specific Cre+ lines, resulting in mTOR expression reduced to 25% of WT except in the specific Cre+ cell type. Male and female subjects were evaluated longitudinally across the lifespan for measures of healthspan using a frailty assessment and compared to WT littermate controls.ResultOffspring from all crosses of mTORΔ/Δ; 5xFAD; and neuronal (Syn1‐cre; JAX#003966), astroglial (Gfap‐cre; JAX#012896) or microglial (Cx3cr1‐cre; JAX#025524) cre‐expressing mice were viable with cohorts bred to N = 10/sex/genotype/cre line for longitudinal lifespan and healthspan assessments. Evaluation of frailty scores assessed at 12 months of age reveal the expected increase in frailty in 5XFAD relative to WT littermate controls with reduced frailty scores in mTORΔ/Δ carrier mice including mTORΔ/Δ/5XFAD crosses.ConclusionThe present interim results confirm and extend the protective effects of ubiquitous reduction of mTOR expression. Ongoing studies in Cre+ lines will determine in which cell types reduced mTOR signaling is necessary for neuroprotection by comprehensive characterization of these models in functional assessments, AD biomarkers and relevant pathology.

A spark energy deposition model in mixture fraction space for simulations of turbulent non-premixed flame ignition

A spark energy deposition model in mixture fraction space for simulations of turbulent non-premixed flame ignition

Beam-ion losses velocity-space distribution under neutral-beam injection on EAST

Abstract The velocity-space distribution of the fast-ion loss in EAST neutral-beam injection (NBI) heating discharge is obtained both from Scintillator-based fast-ion loss detector (FILD) signals and by ASCOT5 and FILDSIM simulations. The results of simulations are in good agreement with the distribution of beam-ion losses measured with FILD of EAST and the correctness of the fast-ion loss distribution has been demonstrated. Simulations indicate that the beam-ion losses observed by the FILD probe are attributed to the fast ions from both the high-field side (HFS) and the low-field side (LFS). However, the beam-ion losses from the HFS (associated with NBI1L) have not been observed experimentally due to the limited detecting range of the FILD probe. Therefore, an upgrade and modification of the FILD probe was carried out in 2022 to enable the detection of fast-ion loss with smaller pitch angles. Comparative analysis is conducted in neutral-beam injection (NBI2R) discharges after the upgrade, which indicates that the velocity-space distribution of beam-ion losses from the HFS has strong agreement between experimental measurements and simulation results. However, the experimental and simulated results of the velocity-space distribution of beam-ion losses from the LFS shows inconsistencies, primarily because the BBNBI module in the simulation does not consider the contributions of boundary neutral particles to neutral-beam deposition (ionization reactions). These conclusions not only provide valuable references for improving the neutral-beam deposition model but also establish a fundamental basis for further exploring the mechanisms of fast-ion loss under various conditions on the EAST tokamak.

Feasibility verification of deep-learning based collimator-less imaging system using a voxelated GAGG(Ce) single volume detector: A Monte Carlo simulation

A 4π-field of view deep-learning-based collimator-less imaging system was designed with the Monte Carlo method and performance of the system was studied to verify the feasibility of system. A 4 × 4 × 4 voxelated single-volume GAGG(Ce) system and 57Co, 133Ba, 22Na, and 137Cs point sources at 2000 positions were modeled using Monte-Carlo N-particle transport code version 6 (MCNP6). Two types of the localized energy deposition acquired with a voxelated detector system with and without energy bins, were calculated. The F6 tally was used to provide the entire energy deposited in each voxel and the F8 tally to provide energy spectrum data for each voxel. This system utilized these energy deposition patterns depending on the source type and position to reconstruct the source distribution image. A fully convolutional network which is advantageous for the prediction of image outputs was used to estimate source distribution. The models utilizing energy deposition patterns generated on total energy deposition and energy spectrum data were trained with labels from 30° to 10 degree of full-width half-maximum (FWHM). As a result of training with single and multiple source data, types of isotopes and source locations were discriminated up to 5 sources when using energy spectral data, and the average image similarity between ground truth images and predicted ones were 0.9936 for total energy deposition model and 0.9966 for divided energy bin model. These results showed the feasibility of a collimator-less imaging system based on deep learning method that requires no filtration of any type of interaction.

Seasonal variation and inhalation health risk of atmospheric polyfluoroalkyl and perfluoroalkyl substances in a metropolitan city centre of southwest China

Fifty-six total suspended particle (TSP) samples, covering four seasons from April 2022 to January 2023, were collected from the centre of Chongqing, a metropolitan city of southwest China, using an aerosol sampler. The samples were analyzed for 20 targeted poly- and per-fluoroalkyl substances (PFAS) by HPLC-MS/MS. The concentrations of PFAS ranged from 125 pg/m3 to 200 pg/m3 and were dominated by perfluorobutanoic acid (PFBA), perfluorooctanoic acid (PFOA), 2-perfluorohexyl ethanoic acid (6:2 FTCA) and perfluorooctane sulfonate (PFOS). Seasonal variations of PFAS were distinct, with the highest concentrations in winter, followed by autumn, and with lowest levels in summer. Positive matrix factorization (PMF) and a dry deposition model were used to apportion sources and to estimate the dry deposition fluxes of the 20 PFAS, respectively. PMF analysis indicated that, based on the annual average, paper packaging production (28.1%) and the degradation of precursor compounds and emissions from PFCA production (27.8%) were the two major sources, followed by electronic product manufacturing (24.0%) and textile production (20.1%), although there were significant differences in sources between seasons. The average annual dry deposition flux of PFAS was estimated to be 18.0 ng/m2/day. However, the wet deposition of PFAS was estimated to account for 82.2% of the total atmospheric deposition flux, suggesting it plays a more important role. Estimated daily intakes (EDI) and hazard quotients (HQ) were utilized to evaluate the risks to humans via exposure to PFAS through inhalation and were found to be insignificant (HQ ≪1). This study provides important information on the contamination status and exposure risk for atmospheric PFAS in a mountainous megacity, and for similar urban centers across China.

Effect of Stack Pressure on Morphological Stability of Lithium Metal Anodes

Morphological instability of the lithium-electrolyte interface is a critical problem limiting the development of lithium-metal anodes for batteries. At current densities typical of liquid cells, unstable deposition produces filaments (or "dendrites") and dense arrays of filaments termed "moss" (1,2). Filaments grow by addition of Li atoms to their base or "root" (3). Experimental evidence indicates that filament growth is fed by solid-state diffusion, and that filaments relieve compressive stress in the metal generated by electrodeposition (4). Instability may be suppressed by application of stack pressure in the range 0.1 - 1 MPa, within which Li metal deforms by creep (5,6). Mathematical models for unstable deposition have typically not included base growth, instead assuming that filaments grow by direct addition of Li atoms at the surface (7).A model is presented for morphological stability of the Li metal-electrolyte interface during plating and stripping cycles in liquid cells. The model includes stress-driven diffusion of Li in and out of the anode during plating and stripping, respectively (8). Diffusion-induced strain is accommodated by creep deformation of the metal. Stack pressure is applied uniformly at the metal-electrolyte interface. Morphology evolution is tracked by linear stability analysis; that is, small periodic disturbances are applied at the interface, and the growth rate of the disturbance amplitude is predicted as a function of disturbance wavelength. The model can be adapted to solid-state cells with additional accounting for solid electrolyte elasticity.Calculations show that in the absence of stack pressure, the Li-electrolyte interface is unstable during both plating and stripping. Instability arises because diffusion in the metal is impeded at "peaks" of the interface profile during plating, and enhanced at interface "valleys" during stripping. The emergent interface roughness is determined by destabilization due to diffusion at large wavelengths, in combination with stabilization by surface energy-driven creep at small wavelengths. Calculations further reveal that the interface is stable when a sufficient stack pressure is applied. Stack pressure induces shear stress at interface peaks that drives creep away from peaks and toward valleys. The effectiveness of stack pressure diminishes at small anode thickness, because of resistance to creep due to friction at the lithium-current collector interface. These effects of stack pressure and anode thickness are also observed experimentally (5, 9).REFERENCES P. Bai, J. Li, F. R. Brushett, M. Z. Bazant, Energy Environ. Sci., 9, 3221 (2016).L Frenck, G. K. Sethi, J. A. Maslyn, N. P. Balsara, Front. Energy Res., 7, 115 (2016)A. Kushima, et al., Nano Energy, 32, 271 (2017). X. Wang, et al., Nat. Energy, 3, 227 (2018). C. C. Fang et al., Nat. Energy, 6, 987 (2021). W. S. Le Page et al., J. Electrochem. Soc., 166, A89 (2019). C. W. Monroe and J. Newman, J. Electrochem. Soc., 152, A396 (2005). K. R. Hebert, J. Electrochem. Soc., 170, 110537 (2023).K. Lee, E. Kazyak, M. J. Wang, N. P. Dasgupta, J. Sakamoto, Joule, 6, 2547 (2022).

Three-Dimensional Microstructure Modeling of Lithium Deposition and Dendrite Formation during Automotive Relevant Cycling

Dendrites generally form during rapid charging of lithium-ion batteries when lithium plating is favored over intercalation. If not removed during discharging cycles, the potential exists for dendrites to grow through the separator and short between the anode and cathode. One metric for diagnosing lithium plating conditions in a computational model is the minimum local potential at the anode, where lithium plating is seen to be thermodynamically favorable below 0.0 V with respect to Li metal. [1-2] Three-dimensional (3D) microstructure model can provide insight to predict the possibility of local lithium plating as well as the possible formation of dendrites. This model can also be used as a tool to detect possible global lithium plating onset. The moving boundary technique with mesh movement can be employed to separate the electrolyte region into a liquid phase and a solid phase.[3] This approach is applied in our 3D microstructure model of a lithium-ion battery[4] to observe the growth of lithium plating as well as dendrites at the anode during rapid charging cycles. Under beginning conditions at the solid/electrolyte interface (SEI), the solid metal phase is deposited at the SEI. Upon growth, the overall electrolyte resistance at the tip of the metal phase is reduced, leading to dendrite growth. The 3D microstructure model provides valuable insight into various conditions that may cause plating, if dendrites are formed due to plating, and whether the dendrites formed may proceed through the separator. References T. R. Garrick, J. Gao, X. Yang and B. J. Koch, J Electrochem Soc, 168, 010530 (2021). U. Janakiraman, T. R. Garrick and M. E. Fortier, J Electrochem Soc, 167, 160552 (2020). T. Jang, L. Mishra, S. A. Roberts, B. Planden, A. Subramaniam, M. Uppaluri, D. Linder, M. P. Gururajan, J.-G. Zhang and V. R. Subramanian, J Electrochem Soc, 169, 080516 (2022). J. Lopata, T. R. Garrick, F. Wang, H. Zhang, Y. Zeng, S. Shimpalee, Journal of The Electrochemical Society, 170, 020530 (2023).

Mathematical modeling of deposition and erosion in porous media with branching channels

Mathematical modeling of deposition and erosion in porous media with branching channels