KoBra: A rate constant method for prediction of the diffusion of sorbates inside nanoporous materials at different loadings

Kolokathis, Panagiotis D.; Braun, O. M.

doi:10.1002/jcc.25857

Cited by 3 publications

(4 citation statements)

References 48 publications

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“…One of the most common in silico approaches for the risk assessment of NMs is the development of QSAR-type models, that quantitatively correlate the bioactivity and toxicity of NMs with descriptors encoding their structural characteristics. [170] For the classic QSAR approach, there are several methods for the calculation of theoretical-structural descriptors for GBMs including the valency-based topological indices of chemical networks proposed by Hayat et al (2018), [178] the distance-based topological descriptors presented by Arockiaraj et al (2019), [179] or other properties such as diffusion inside the GBM calculated by rates as proposed by Kolokathis et al (2019). [180] Based on the QSAR approaches, different ML approaches have been developed that make use, apart from the classic molecular descriptors, of other nano-related properties (e.g., physicochemical characterization data, quantum-mechanical descriptors, energy data calculated by MD simulations, omics data etc.)…”

Section: Qsar-type Methodologiesmentioning

confidence: 99%

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Surface Functionalization of Graphene‐Based Materials: Biological Behavior, Toxicology, and Safe‐By‐Design Aspects

et al. 2021

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drug delivery, bioimaging probes, antibacterial agents, and tissue engineering. [2] The advantage of GBMs over other nanomaterials (NMs) in biomedical application is their high specific surface area which allows high density functionalization and drug loading on both sides of the planar structure. [3] The π-conjugated system offers high capacity for GBMs to interact with various organic compounds with aromatic structures through π-π stacking during fabrication of graphene composites or for immobilization of biomolecules such as nucleic acids, peptides, antibodies, or other therapeutic substances. [4] The same properties may also raise concerns regarding the potential risk that GBMs may cause to human health through binding of key signaling molecules for example. GBMs may interact with biomolecules, changing the structure of protein or DNA or induce oxidative damage, and so on. [5] Therefore, prior to the widespread use of GBMs, it is imperative to evaluate their potential risk to environmental and human health and to establish a proactive approach for the safe design of these materials.In a recent review article, Fadeel et al. comprehensively examined the literature on the effects of GBMs on human health and the environment and gave an overview of the state-of-the-art of safety assessment of GBMs, highlighting the importance of The increasing exploitation of graphene-based materials (GBMs) is driven by their unique properties and structures, which ignite the imagination of scientists and engineers. At the same time, the very properties that make them so useful for applications lead to growing concerns regarding their potential impacts on human health and the environment. Since GBMs are inert to reaction, various attempts of surface functionalization are made to make them reactive. Herein, surface functionalization of GBMs, including those intentionally designed for specific applications, as well as those unintentionally acquired (e.g., protein corona formation) from the environment and biota, are reviewed through the lenses of nanotoxicity and design of safe materials (safe-by-design). Uptake and toxicity of functionalized GBMs and the underlying mechanisms are discussed and linked with the surface functionalization. Computational tools that can predict the interaction of GBMs behavior with their toxicity are discussed. A concise framing of current knowledge and key features of GBMs to be controlled for safe and sustainable applications are provided for the community.

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Section: Qsar-type Methodologiesmentioning

confidence: 99%

“…(2019), [ 179 ] or other properties such as diffusion inside the GBM calculated by rates as proposed by Kolokathis et al (2019). [ 180 ]…”

Section: Computational Approaches For Sbd Of Gbmsmentioning

confidence: 99%

Surface Functionalization of Graphene‐Based Materials: Biological Behavior, Toxicology, and Safe‐By‐Design Aspects

et al. 2021

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“…TST methods can be used to estimate the diffusion coefficients in porous materials at slow diffusion time scales − using the free-energy landscape . Such methods have been used for the estimation of diffusion coefficients of aromatics in MFI-type zeolites. ,,− Caro-Ortiz et al showed that force fields for framework flexibility produce a zeolite structure that vibrates around a new equilibrium configuration with limited capacity to accommodate bulky guest molecules. To the best of our knowledge, molecular simulation studies where the effect of framework flexibility on the adsorption and diffusion of aromatics in zeolites is systematically studied are not available.…”

Section: Introductionmentioning

confidence: 99%

Effects of Framework Flexibility on the Adsorption and Diffusion of Aromatics in MFI-Type Zeolites

et al. 2020

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We systematically study how the degree of framework flexibility affects the adsorption and diffusion of aromatics in MFI-type zeolites as computed by Monte Carlo simulations. It is observed that as the framework is more flexible, the zeolite structure is inherently changed. We have found that framework flexibility has a significant effect on the adsorption of aromatics in MFI-type zeolites, especially at high pressure. Framework flexibility allows the zeolite framework to accommodate to the presence of guest aromatic molecules. For very flexible zeolite frameworks, loadings up to two times larger than that in a rigid zeolite framework are obtained at a given pressure. We assessed the “flexible snapshot” method, which captures framework flexibility using independent snapshots of the framework. We have found that this method only works well when the loadings are low. This suggests that the effect of the guest molecules on the zeolite framework is important. Framework flexibility lowers the free-energy barriers between low energy states, increasing the rate of diffusion of aromatics in the straight channel of MFI-type zeolites for many orders of magnitude compared to a rigid zeolite framework. The simulations show that framework flexibility should not be neglected and that it significantly affects the diffusion and adsorption properties of aromatics in an MFI-type zeolite.

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“…This allows for the simultaneous, exact treatment of spatial correlations on the surface that may be caused by adsorbate confinement or slow diffusion, [26,27] island formation, [28][29][30] lateral interactions, [1,14,18,19,[31][32][33][34][35] or substrate heterogeneity. [30,36,37] The numerical solution of the CME by KMC can be quite costly and, for some problems, there may be alternative approaches, [2,[38][39][40] such as solving the CME in Fourier space [41] or the tensor-train approximation. [42] In the limit of fast diffusion, the lattice structure becomes irrelevant, which simplifies the problem considerably.…”

Section: Introductionmentioning

confidence: 99%

Efficient Implementation of Cluster Expansion Models in Surface Kinetic Monte Carlo Simulations with Lateral Interactions: Subtraction Schemes, Supersites, and the Supercluster Contraction

Heß

2019

J Comput Chem

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While lateral interaction models for reactions at surfaces have steadily gained popularity and grown in terms of complexity, their use in chemical kinetics has been impeded by the low performance of current kinetic Monte Carlo (KMC) algorithms. The origins of the additional computational cost in KMC simulations with lateral interactions are traced back to the more elaborate cluster expansion Hamiltonian, the more extensive rate updating, and to the impracticality of rate‐catalog‐based algorithms for interacting adsorbate systems. Favoring instead site‐based algorithms, we propose three ways to reduce the cost of KMC simulations: (1) representing the lattice energy by a smaller Supercluster Hamiltonian without loss of accuracy, (2) employing the subtraction schemes for updating key quantities in the simulation that undergo only small, local changes during a reaction event, and (3) applying efficient search algorithms from a set of established methods (supersite approach). The cost of the resulting algorithm is fixed with respect to the number of lattice sites for practical lattice sizes and scales with the square of the range of lateral interactions. The overall added cost of including a complex lateral interaction model amounts to less than a factor 3. Practical issues in implementation due to finite numerical accuracy are discussed in detail, and further suggestions for treating long‐range lateral interactions are made. We conclude that, while KMC simulations with complex lateral interaction models are challenging, these challenges can be overcome by modifying the established variable step‐size method by employing the supercluster, subtraction, and supersite algorithms (SSS‐VSSM). © 2019 Wiley Periodicals, Inc.

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KoBra: A rate constant method for prediction of the diffusion of sorbates inside nanoporous materials at different loadings

Cited by 3 publications

References 48 publications

Surface Functionalization of Graphene‐Based Materials: Biological Behavior, Toxicology, and Safe‐By‐Design Aspects

Surface Functionalization of Graphene‐Based Materials: Biological Behavior, Toxicology, and Safe‐By‐Design Aspects

Effects of Framework Flexibility on the Adsorption and Diffusion of Aromatics in MFI-Type Zeolites

Efficient Implementation of Cluster Expansion Models in Surface Kinetic Monte Carlo Simulations with Lateral Interactions: Subtraction Schemes, Supersites, and the Supercluster Contraction

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