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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Modeling the Photoelectrochemical Evolution of Lead-Based, Mixed-Halide Perovskites Due to Photosegregation

Lead-based, mixed-halide perovskites such as methylammonium lead iodide-bromide [MAPb(I 1-x Br x ) 3 ] undergo anion photosegregation under illumination. This is observed as low band gap photoluminescence from photogenerated iodine-rich domains due to favorable band offsets that induce carrier funneling into them. Unfortunately, theoretical rationalizations of mixed-halide photosegregation are complicated by biases inherent to photoluminescence-based observations. Recent compositionally-weighted X-ray diffraction (XRD) measurements now reveal broad distributions of photosegregated stoichiometries not captured by existing photosegregation models. To better bridge experiment and theory, we perform kinetic Monte Carlo (KMC) simulations of photosegregation within the context of a band gap-based thermodynamic model, which has previously accounted for numerous experimental observations. Our KMC simulations are modified to consider high carrier density Fermi-Dirac statistics that result from carrier funneling and accumulation within photosegregated I-rich domains. Obtained KMC results reproduce broad x terminal distributions seen experimentally and illustrate how they are characterized by a central, heavily I-enriched stoichiometry. I-rich domain “drifting” during photosegregation rationalizes the long photosegregation timescales seen experimentally with drifting simultaneously producing a wake of variable stoichiometry I-rich inclusions that form the lion’s share of stoichiometric heterogeneities seen in compositionally-weighted XRD measurements. These simulations and accompanying rationalizations further reveal a general criterion for realizing favorable free energies to induce demixing. Central to the criterion is the statistical occupation of low gap inclusions in the parent alloy by excitations. Furthermore, the resulting model thus provides a general framework for conceptualizing mixed-halide perovskite light and temperature sensitivities, mediated by photocarriers.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Effects of surface diffusion in electrocatalytic CO 2 reduction on Cu revealed by kinetic Monte Carlo simulations

We report Kinetic Monte Carlo (KMC) methods are frequently used for mechanistic studies of thermally driven heterogeneous catalysis systems but are underused for electrocatalysis. Here, we develop a lattice KMC approach for electrocatalytic CO 2 reduction. The work is motivated by a prior experimental report that performed electroreduction of a mixed feed of 12 CO 2 and 13 CO on Cu; differences in the 13 C content of C2 products ethylene and ethanol (Δ 13 C) were interpreted as evidence of site selectivity. The lattice KMC model considers the effect of surface diffusion on this system. In the limit of infinitely fast diffusion (mean-field approximation), the key intermediates 12 CO* and 13 CO* would be well mixed on the surface and no evidence of site selectivity could have been observed. Using a simple two-site model and adapting a previously reported microkinetic model, we assess the effects of diffusion on the relative isotope fractions in the products using the estimated surface diffusion rate of CO* from literature reports. We find that the size of the active sites and the total surface adsorbate coverage can have a large influence on the values of Δ 13 C that can be observed. Δ 13 C is less sensitive to the CO* diffusion rate as long as it is within the estimated range. We further offer possible methods to estimate surface distribution of intermediates and to predict intrinsic selectivity of active sites based on experimental observations. This work illustrates the importance of considering surface diffusion in the study of electrochemical CO 2 reduction to multi-carbon products. Our approach is entirely based on a freely available open-source code, so will be readily adaptable to other electrocatalytic systems.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Adaptive tau-leaping methods for microscopic-lattice kinetic Monte Carlo simulations

Traditional Kinetic Monte Carlo (KMC) approaches, rooted in Gillespie’s stochastic simulation algorithm, become computationally demanding in systems with a large range of timescales. The goal of this work is to propose and study new adaptive lattice-KMC time integration strategies for spatially non-uniform systems. To that end, two novel adaptive tau-leaping methods and their corresponding time integration strategies are developed based on the idea of the “n-fold” direct KMC method. These strategies allow for the simultaneous execution of multiple reactions, advancing time by adaptively selected coarse increments. We present numerical experiments comparing the proposed methods with existing approaches in a catalytic surface kinetics application involving ammonia decomposition.

Bimolecular reactions↗
DOE OSTI2022

Dynamic Data-Driven Multiscale Modeling for Predicting Microstructure and Mechanical Properties of 316L Stainless Steel

We have developed a long short-term memory stacked ensemble (LSTM-SE) surrogate modeling approach that can provide rapid predictions of microstructural evolution and the resultant mechanical properties of American Iron and Steel Institute (AISI) 316L series stainless steel (316LSS) fuel cladding under conditions of varying temperature and radiation dose rate. To acquire training data, we developed and implemented a kinetic Monte Carlo (KMC) model to simulate precipitation kinetics of M 23 C 6 , γ', and G phases within SS316L cladding. Experimentally reported precipitation kinetics of SS316L in literature were linked to the kinetic parameters of the simulated precipitation in our KMC model. The model was then used to simulate microstructure evolution under synthetically generated treatments of varying temperature and radiation dose rate, for periods of up to 3000 hours. Changes in volume fraction, number density, and particle size of precipitates were recorded, and particle area fractions were correlated using statistical methods to develop the surrogate model. Simultaneously, the mechanical properties of the simulated microstructures were evaluated using microstructure-based finite element method (FEM) analysis to determine the elastic modulus, yield stress, ultimate tensile strength, and elongation to failure of the aged microstructures. Using this approach, our surrogate model can predict precipitation behavior within 0.25% volume fraction and mechanical properties within 6% relative error from the values predicted by the KMC and FEM models using 50 training simulations as input. The trained recurrent neural network-based model can return estimations of precipitation kinetics and mechanical properties ~1000 times faster than the physics-based codes. This work demonstrates, as a proof of concept, that reactor material service lifetimes under variable service conditions can be predicted for a statistics-based model from a practicably obtainable dataset.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Bulk and surface diffusion of neodymium in alpha-uranium: Ab initio calculations and kinetic Monte Carlo simulations

A fundamental understanding of lanthanide transport in metallic fuels is critical for high fidelity modeling of the fuel-cladding chemical interaction (FCCI) phenomenon, which can lead to the formation of brittle intermetallic compounds and premature failure of the cladding. Here we report a combined ab initio density functional theory (DFT) and kinetic Monte Carlo (KMC) study of the bulk diffusivity of Nd in α-U, fully taking into account the effect of radiation enhanced diffusion. In this work, the vacancy mechanism is considered to be the dominant mechanism for the bulk diffusion of Nd since a Nd interstitial is found to be intrinsically unstable in α-U. The surface diffusivity of a Nd adatom on α-U (001) surface has been further predicted using KMC simulations parameterized by DFT calculations. The present study suggests that Nd transport via the surface diffusion mechanism can be many orders of magnitude faster than bulk diffusion. Furthermore, the results from the present lower length scale study can be used to inform mesoscale phase-field simulations to determine the effective diffusion coefficient of Nd through α-U with a porous microstructure.

36 MATERIALS SCIENCE↗
DOE OSTI2025

From Micro-environments to Macroscopic Effects: How the Alkaline Hydrogen Evolution Reaction Drives Cu Cathodic Corrosion

Cathodic corrosion of copper (Cu) has posed a significant challenge for over a century, impeding various technological progresses such as electrochemical conversion of CO 2 (eCO 2 RR) into fuels and other value-added carbon products. Here, in this study, employing a combined Density Functional Theory (DFT) and kinetic Monte Carlo (kMC) simulation approach, we delve into the atomistic level mechanism driving this phenomenon in Cu. Our hypothesis posits the pivotal role of alkaline hydrogen evolution reaction (HER) in facilitating cathodic corrosion in Cu. We rigorously develop a pH-dependent hydroxide (OH) adsorption mechanism and calculate the equilibrium OH coverage (𝜃 OH ) at varying pH levels, the thermodynamic stability of subsurface oxygen (O sub ), as well as the Cu-vacancy mediated diffusion of subsurface oxygens (O sub ). Through comprehensive analysis, we establish correlation among various microenvironments, including oxygen diffusion in subsurface layers, pH-dependent OH adsorption, and Cu dissolution into the electrolyte as (Cu-OH) complexes. Furthermore, our investigation explores the correlation between surface coordination environment of active sites and cathodic corrosion of Cu. Finally, by integrating DFT-derived thermodynamic data into a kMC model, we successfully predict the formation of experimentally observed corrosion pits on Cu-surfaces. This combined approach not only advances our fundamental understanding of Cu cathodic corrosion but also offers insights crucial for developing effective corrosion mitigation strategies.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

A dissolution model of alite coupling surface topography and ions transport under different hydrodynamics conditions at microscale

Portland cement is the most produced material in the world. The hydration process of cement consists of a group of complex chemical reactions. In order to investigate the mechanism of cement hydration, it is vital to study the hydration of each phase separately. An integrated model is proposed in this paper to simulate the dissolution of alite under different hydrodynamic conditions at microscale, coupling Kinetic Monte Carlo model (KMC), Lattice Boltzmann method (LBM) and diffusion boundary layer (DBL). The dissolution of alite is initialised with KMC. Two Multiple-relaxation-time (MRT) LB models are used to simulate the fluid flow and transport of ions, respectively. For solid-liquid interface, DBL is adapted to calculate the concentration gradient and dissolution flux. The model is validated with experiment from literature. The simulation results show good agreements with the results published in the literature.

36 MATERIALS SCIENCE↗
DOE OSTI2023

A combined kinetic Monte Carlo and phase field approach to model thermally activated dislocation motion

In this work, a kinetic Monte Carlo (KMC) scheme is integrated into a phase field dislocation dynamics (PFDD) model to account for thermal activation. While the implementation is general and could be applied to many different thermally activated processes, we apply the model to simulate a dislocation transmitting through an interface. Two cases are analyzed: a dislocation transmitting through a strong interface, and a dislocation transmitting through a more realistic grain boundary in tungsten. In conclusion, results and trends for both cases are discussed in detail, and ultimately show that the combined KMC-PFDD algorithm is effective in capturing thermally activated dislocation processes.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Pilgrim : A thermal rate constant calculator and a chemical kinetics simulator

Pilgrim is a program written in Python and designed to use direct dynamics in the calculation of thermal rate constants of chemical reactions by the variational transition state theory (VTST), based on electronic structure calculations for the potential energy surface. Pilgrim can also simulate reaction mechanisms using kinetic Monte Carlo (KMC). For reaction processes with many elementary steps, the rate constant of each of these steps can be calculated by means of conventional transition state theory (TST) or by using VTST. In the current version, Pilgrim can evaluate thermal rates using the canonical version of reaction-path VTST, which requires the calculation of the minimum energy path (MEP) associated with each elementary step or transition structure. Multi-dimensional quantum effects can be incorporated through the small-curvature tunneling (SCT) approximation. These methodologies are available both for reactions involving a single structure of the reactants and the transition state and also for reactions involving flexible molecules with multiple conformations of the reactant and/or of the transition state. For systems with many conformers, the program can evaluate each of the elementary reaction rate constants by multipath canonical VTST or multi-structural VTST. Moreover, the reactant can be unimolecular or bimolecular. Torsional anharmonicity can be incorporated through either the MSTor or the Q2DTor programs. Dual-level calculations are also available in Pilgrim: automatic high-level single-point energies can be used to correct the energy of reactants, transition states, products, and MEP points using the interpolated single-point energies (ISPE) algorithm. When the rate constants of all the chemical processes of interest are known, by means of their calculation using Pilgrim or alternatively through analytical fits to the rate constants as functions of temperature, it is possible to simulate a multistep mechanism under specified laboratory conditions using KMC. Finally, this algorithm allows performing a kinetic simulation to monitor the evolution of each chemical species with time and obtain the product yields.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2021

Consequences of adsorbate-adsorbate interactions for apparent kinetics of surface catalytic reactions

Lateral adsorbate interactions at catalyst surfaces are known to influence adsorption energies and reaction rates. Lattice-based kinetic Monte Carlo (kMC) simulations are able to capture these influences, but such models are typically parameterized for a specific reaction network and catalyst surface. Here we report kMC simulations to probe the influence of lateral interactions on simulated rates, rate orders, apparent activation energies, and Sabatier plots. We construct a simple, two-step reaction network involving a single adsorbate and rate- limiting diatomic dissociation, employ a generic repulsive lateral interaction model consistent with known adsorbate-adsorbate interactions on metal surfaces, and a rate model consistent with known Brønsted-Evans-Polyani relationships for diatomic dissocations. Furthermore, we juxtapose reaction kinetics over a wide range of reaction conditions and catalyst binding energies, as a function of interaction strength. We find that at a given zero-coverage binding energy and external conditions, adsorbate coverage decreases monotonically with increasing interaction strength, but absolute rates can vary linearly or nonlinearly. Interactions flatten the Sabatier volcano and shift the maximum towards stronger binding. Influences on apparent rate orders and activation energies are modest and are sensitive to interaction-induced adsorbate ordering. Model predictions are sensitive to lattice size effects at high coverages. The results, modelled for a simple reaction system, highlight the generic consequences of lateral interactions and guidance for identifying their signatures in observed kinetics.

kinetic Monte Carlo↗
DOE OSTI2021

A slip-spring framework to study relaxation dynamics of entangled wormlike micelles with kinetic Monte Carlo algorithm

Hypothesis: Wormlike micelles (WLMs) formed due to the self-assembly of amphiphiles in aqueous solution have similar viscoelastic properties as polymers. Owing to this similarity, in this work, it is postulated that kinetic Monte Carlo (kMC) sampling of slip-springs dynamics, which is able to model the rheology of polymers, can also be extended to capture the relaxation dynamics of WLMs. Theory: The proposed modeling framework considers the following relaxation mechanisms: reptation, union-scission, and constraint release. Specifically, each of these relaxation mechanisms is simulated as separate kMC events that capture the relaxation dynamics while considering the living nature of WLMs within the slip-spring framework. As a case study, the model is implemented to a system of sodium oleate and sodium chloride to predict the linear rheology and the characteristic relaxation times associated with the individual relaxation mechanisms at different pH and salt concentrations. Findings: Linear rheology predictions were found to be in good agreement with experimental data. Furthermore, the calculated relaxation times highlighted that reptation contributed to a continuous increase in viscosity while union-scission contributed to the decrease in viscosity of WLM solutions at a higher salinity and pH. Finally, this manifests the proposed model’s capability to provide insights into the key processes governing WLM’s rheology.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Decoupling thermal and irradiation effects on grain boundary segregation

Radiation-induced segregation (RIS) is most often measured by peak solute concentration at a boundary. However, this may give an incomplete picture of segregation quantity and phenomena. Radiation-induced and thermal segregation at grain boundaries was investigated in Fe-9.6 at.% Cr after 9 MeV Fe 3+ ion irradiation at 400 °C. The experimental results were compared to kinetic Monte Carlo (KMC) simulations. The study revealed that Cr enrichment (peak segregation) at the grain boundaries was comparable in both the irradiated and non-irradiated conditions, although irradiation resulted in broader segregation profiles in both experiments and simulations, indicating an overall increase in grain boundary segregation due to irradiation. This broadening is attributed to back diffusion into the grain interior. While it is an established phenomenon, this study offers a quantitative evaluation using experimental data and KMC modeling. Further, these results emphasize the importance of analyzing the entire segregation profile and decoupling the thermal and irradiation contributions to solute segregation.

Grain boundary segregation↗
DOE OSTI2024

CO–induced roughening of Cu(111): formation and detection of reactive nanoclusters on metal surfaces

The formation of nanoclusters on metal surfaces in the presence of reactive environments is a phenomenon with important implications for catalysis. These nanoclusters are composed of atoms ejected from undercoordinated sites such as step edges, and their presence alters the catalytic properties of solid materials. We perform density functional theory (DFT) and kinetic Monte Carlo (KMC) simulations to investigate the formation and reactivity of copper clusters on Cu(111). Our results indicate a considerably higher reactivity of small copper nanoclusters, with up to seven atoms in size on roughened copper surfaces than on pristine Cu(111) and Cu(211). Regarding the restructuring events that give rise to nanoclusters under CO atmospheres, we determine that the ejection of Cu atoms from step edges and their migration therefrom to adjacent Cu(111) terraces are, by and large, driven by CO coverage effects. By means of KMC simulations, which account for CO–CO lateral interactions and CO–induced surface restructuring, we show that temperature programmed desorption (TPD) holds promise for the detection of highly reactive nanoclusters. Furthermore, our approach showcases how surface restructuring and surface–adsorbate bond breaking can be combined when modeling surface reactions and contributes to the development of an advanced understanding of the nature of active site under reaction conditions.

catalyst dynamic restructuring↗
DOE OSTI2023

Revealing Intermetallic Active Sites of PtNi Nanocatalysts for Reverse Water Gas Shift Reaction

Bimetallic nanoparticles have gained great interest due to their potential applications in catalysis. By combining Lattice Boltzmann Modeling (LBM), Density Functional Theory (DFT) calculations, and kinetic Monte Carlo (KMC) simulation, here we rationalize the superior performance of supported Pt-Ni alloy nanostructures over Pt and Ni alone during the reverse Water Gas Shift (rWGS) reaction, which was observed previously. The LBM simulation including the diffusion and flow of the reacting gas molecules within the supporting nanoporous channels in SBA-15 indicates that the simple combination of contributions from Ni and Pt alone is not enough to result in the observed promotion in rWGS activity and selectivity of the Pt-Ni alloy. Instead, the synergy between Pt and Ni via a unique intermetallic motif is responsible according to the DFT and KMC simulation. In conclusion, compared to the monometallic counterparts, the intermetallic-introduced ensemble effect at the low-coordinated Pt-Ni hybrid site of Pt-Ni alloy nanocatalysts enables the enhanced activation of CO 2 and promoted removal of CO/H 2 O as gas phase product.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Computational Investigation of the Role of Active Site Heterogeneity for aSupported Organovanadium(III) Hydrogenation Catalyst

A crucial consideration for supported heterogeneous catalysts is the nonuniformity of the active sites, particularly for supported organometallic catalysts. Standard spectroscopic techniques, such as X-ray absorption spectroscopy, reflect the nature of the most populated sites, which are often intrinsically structurally distinct from the most active catalytic sites. Additionally, with computational models, often, only a few representative structures are used to depict catalytic active sites on a surface, even though there are numerous observable factors of surface heterogeneity that contribute to the kinetically favorable active species. A previously reported study on the mechanism of a surface organovanadium-(III) catalyst [(SiO 2 )V III (Mes)(THF)] for styrene hydrogenation yielded two possible mechanisms: heterolytic cleavage and redox cycling. These two mechanistic scenarios are challenging to differentiate experimentally since the kinetic readouts of the catalyst are identical. To showcase the importance of modeling surface heterogeneity and its effect on catalytic activity, density functional theory (DFT) computational models of a series of potential active sites of [(SiO 2 )V III (Mes)(THF)] for the reaction pathways are applied in combination with kinetic Monte Carlo (kMC) simulations. Computed results were then compared to the previously reported experimental kinetic study: (1) DFT free-energy reaction pathways indicated the likely active site and pathway for styrene hydrogenation, a heterolytic cleavage pathway requiring a bare tripodal vanadium site. (2) From the kMC simulations, a mixture of different bond lengths from the support oxygen to the metal center was required to qualitatively describe the experimentally observed kinetic aspects of a supported organovanadium(III) catalyst for olefin hydrogenation. This work underscores the importance of modeling surface heterogeneity in computational catalysis.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Highly Selective Methane to Methanol Conversion on Inverse SnO 2 /Cu 2 O/Cu(111) Catalysts: Unique Properties of SnO 2 Nanostructures and the Inhibition of the Direct Oxidative Combustion of Methane

Direct methane to methanol (CH 4 → CH 3 OH) conversion in heterogeneous catalysis has been a long-standing challenge due to the difficulties in equalizing the activation of methane and protection of the methanol product at the same reaction conditions. Here, we report an inverse catalyst, consisting of small structures of SnO 2 (0.5-1 nm in size) dispersed on Cu 2 O/Cu(111), for highly selective CH 3 OH production from CH 4 . This system was investigated by combining theoretical [density functional theory calculations (DFT), kinetic Monte Carlo simulations (KMC)] and experimental methods [scanning tunneling microscopy (STM), ambient-pressure X-ray photoelectron spectroscopy (AP-XPS)]. The DFT and AP-XPS studies showed that on SnO 2 /Cu 2 O/Cu(111) the conversion of CH 4 by oxygen (O 2 ) preferred complete combustion to carbon dioxide (CO 2 ). The addition of water (H 2 O) enhanced the production of CH 3 OH to nearly 100% selectivity in KMC simulations. This trend was consistent with results of AP-XPS. The presence of water in the reaction environment rendered an extremely high amount of methoxy species (*CH 3 O), a precursor for CH 3 OH production. Further, the high CH 3 OH selectivity of SnO 2 /Cu 2 O/Cu(111) reflected the unique atomic and electronic structure of the supported SnO 2 nanoparticles. As a result, the O 2 adsorption and dissociation, and thus the full combustion of CH 4 to CO 2 , was completely suppressed; while the H 2 O dissociative adsorption was still feasible, providing active hydroxyl species for a truly selective CH 4 to CH 3 OH conversion.

03 NATURAL GAS↗
DOE OSTI2021

Cesium-Induced Active Sites for C–C Coupling and Ethanol Synthesis from CO 2 Hydrogenation on Cu/ZnO(000$\bar{1}$) Surfaces

The efficient conversion of carbon dioxide, a major air pollutant, into ethanol or higher alcohols is a big challenge in heterogeneous catalysis, generating great interest in both basic scientific research and commercial applications. Here, we report the facilitated methanol synthesis and the enabled ethanol synthesis from carbon dioxide hydrogenation on a catalyst generated by codepositing Cs and Cu on a ZnO(000$\bar{1}$) substrate. A combination of catalytic testing, X-ray photoelectron spectroscopy (XPS) measurements, and calculations based on density functional theory (DFT) and kinetic Monte Carlo (KMC) simulation was used. The results of XPS showed a clear change in the reaction mechanism when going from Cs/Cu(111) to a Cs/Cu/ZnO(000$\bar{1}$) catalyst. The Cs-promoting effect on C–C coupling is a result of a synergy among Cs, Cu, and ZnO components that leads to the presence of CH x and CH y O species on the surface. Furthermore, according to the DFT-based KMC simulations, the deposition of Cs introduces multifunctional sites with a unique structure at the Cu–Cs–ZnO interface, particularly being able to promote the interaction with CO 2 and thus the methanol synthesis predominantly via the formate pathway. More importantly, it tunes the CHO binding strongly enough to facilitate the HCOOH decomposition to CHO via the formate pathway, but weakly enough to allow further hydrogenation to methanol. The fine-tuning of CHO binding also enables a close alignment of a CHO pair to facilitate the C–C coupling and eventually ethanol synthesis. Our study opens new possibilities to allow the highly active and selective conversion of carbon dioxide to higher alcohols on widely used and low-cost Cu-based catalysts.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Multiscale modeling of solute diffusion in triblock copolymer membranes

We develop a multiscale simulation model for diffusion of solutes through porous triblock copolymer membranes. The approach combines two techniques: self-consistent field theory (SCFT) to predict the structure of the self-assembled, solvated membrane and on-lattice kinetic Monte Carlo (kMC) simulations to model diffusion of solutes. Solvation is simulated in SCFT by constraining the glassy membrane matrix while relaxing the brush-like membrane pore coating against the solvent. The kMC simulations capture the resulting solute spatial distribution and concentration-dependent local diffusivity in the polymer-coated pores; we parameterize the latter using particle-based simulations. We apply our approach to simulate solute diffusion through nonequilibrium morphologies of a model triblock copolymer, and we correlate diffusivity with structural descriptors of the morphologies. We also compare the model’s predictions to alternative approaches based on simple lattice random walks and find our multiscale model to be more robust and systematic to parameterize. Furthermore, our multiscale modeling approach is general and can be readily extended in the future to other chemistries, morphologies, and models for the local solute diffusivity and interactions with the membrane.

36 MATERIALS SCIENCE↗