Molecular dynamics simulation of self- and mutual diffusion coefficients for confined mixtures

Separation of hydrocarbon mixture of neopentane and n-hexane using NaY zeolite: Large distinct diffusivity

A recently proposed method based on Levitation and Blow torch effects, is employed here to see if it can separate a mixture of neopentane and n-hexane. The results show that the mixture can be separated with a hot zone temperature of just 40 K above the ambient temperature, 300 K. The two components are found to accumulate at the two extreme ends of the zeolite column. The computed separation factor is in the range of 10¹⁵ -10²⁰ (as compared to 10⁴ for existing separation methods). The energy expense for the separation is significantly smaller than for existing separation methods by several orders of magnitude. Transport (D₁₁ ), self (D_s ), and distinct diffusivities (D_d ) of the mixture were computed. The contribution of distinct diffusivity to the transport diffusivity is 70% as compared to 10%-30% seen in other separation methods and is larger by 2.3 times as compared to the self-diffusivity.

Using Computationally-Determined Properties for Machine Learning Prediction of Self-Diffusion Coefficients in Pure Liquids

The ability to predict transport properties of liquids quickly and accurately will greatly improve our understanding of fluid properties both in bulk and complex mixtures, as well as in confined environments. Such information could then be used in the design of materials and processes for applications ranging from energy production and storage to manufacturing processes. As a first step, we consider the use of machine learning (ML) methods to predict the diffusion properties of pure liquids. Recent results have shown that Artificial Neural Networks (ANNs) can effectively predict the diffusion of pure compounds based on the use of experimental properties as the model inputs. In the current study, a similar ANN approach is applied to modeling diffusion of pure liquids using fluid properties obtained exclusively from molecular simulations. A diverse set of 102 pure liquids is considered, ranging from small polar molecules (e.g., water) to large nonpolar molecules (e.g., octane). Self-diffusion coefficients were obtained from classical molecular dynamics (MD) simulations. Since nearly all the molecules are organic compounds, a general set of force field parameters for organic molecules was used. The MD methods are validated by comparing physical and thermodynamic properties with experiment. Computational input features for the ANN include physical properties obtained from the MD simulations as well as molecular properties from quantum calculations of individual molecules. Fluid properties describing the local liquid structure were obtained from center of mass radial distribution functions (COM-RDFs). Feature sensitivity analysis revealed that isothermal compressibility, heat of vaporization, and the thermal expansion coefficient were the most impactful properties used as input for the ANN model to predict the MD simulated self-diffusion coefficients. The MD-based ANN successfully predicts the MD self-diffusion coefficients with only a subset (2 to 3) of the available computationally determined input features required. A separate ANN model was developed using literature experimental self-diffusion coefficients as model targets. Although this second ML model was not as successful due to a limited number of data points, a good correlation is still observed between experimental and ML predicted self-diffusion coefficients.

Engineering the pH-Sensitivity of the Graphene and Carbon Nanotube Based Nanomedicines in Smart Cancer Therapy by Grafting Trimetyl Chitosan

PURPOSE

The aim of this study was to introduce a smart and responsive drug carrier for Doxorubicin (DOX) and Paclitaxel (PAX) for desirable therapeutic application.

METHOD

Loading and releasing of DOX and PAX from smart and pH-sensitive functionalized single-walled carbon nanotube (SWCNTs) and graphene carriers have been simulated by molecular dynamics. The influences of chitosan polymer on proposed carriers have been studied, and both carriers were functionalized with carboxyl groups to improve the loading and releasing properties of the drugs.

RESULTS

The results showed that DOX could be well adsorbed on both functionalized SWCNTs and graphene. In contrast, there was a weak electrostatic and Van der Waals interaction between both these drugs and carriers at cancerous tissues, which is highly favorable for cancer therapy. Adding trimethyl chitosan (TMC) polymer to carriers facilitated DOX release at acidic tissues. Furthermore, at blood pH, the PAX loaded on the functionalized SWCNTs carrier represented the highest dispersion of the drug while the DOX-graphene showed the highest concentration of the drug at a point. In addition, the mean-square displacement (MSD) results of PAX-graphene indicated that the PAX could be adsorbed quickly and be released slowly. Finally, functionalized graphene-TMC-PAX is a smart drug system with responsive behavior and controllable drug release, which are essential in cancer therapy.

CONCLUSION

Simultaneous application of the carboxyl group and TMC can optimize the pH sensitivity of the SWCNTs and graphene to prepare a novel and smart drug carrier for cancer therapy.

Molecular insight into the smart functionalized TMC-Fullerene nanocarrier in the pH-responsive adsorption and release of anti-cancer drugs

The Doxorubicin (DOX) and Paclitaxel (PAX) are widely used for cancer-therapy. Herein, in the efforts devoted to developing smart drug carriers, the loading and releasing of the DOX and PAX on the pH sensitive functionalized Fullerene carrier was investigated by molecular dynamics (MD) simulations. The effects of chitosan polymer as a functionalizing agent of the Fullerene carrier was also studied. In addition, the Fullerene carrier was functionalized with carboxyl groups in order to improve the loading and releasing properties of the DOX and PAX. The results showed the DOX is well adsorbed on Fullerene which was functionalized with carboxyl group and it was released controllably in cancerous tissues. According to the results of the electrostatic and Van der Waals interactions, it was found that the functionalized Fullerene can be a proper carrier for DOX in comparison with PAX. Adding the trimethyl chitosan (TMC) polymer to the carrier could improve the Van der Waals attractions of the PAX and Fullerene which indicates that by passing the time at acidic pH, the Van der Waals energy reaches zero that leads to promote the release of the PAX in cancerous tissues. The carboxyl group which was employed as a functionalizing agent could also increase the number of hydrogen bonds for the PAX and DOX at acidic and neutral pH, respectively. Moreover, a significant rise in the number of hydrogen bonds between the PAX and Fullerene at neutral pH was achieved by adding the TMC to the carrier. A more decrease of gyration radius was obtained for the DOX at acidic pH which confirms that the DOX with TMC-Fullerene is a more stable carrier. So, this smart nanomedicine system is introduced as an promising composition for smart cancer therapy.

pH-Sensitive Co-Adsorption/Release of Doxorubicin and Paclitaxel by Carbon Nanotube, Fullerene, and Graphene Oxide in Combination with N-isopropylacrylamide: A Molecular Dynamics Study

Nanotechnology based drug delivery systems for cancer therapy have been the topic of interest for many researchers and scientists. In this research, we have studied the pH sensitive co-adsorption and release of doxorubicin (DOX) and paclitaxel (PAX) by carbon nanotube (CNT), fullerene, and graphene oxide (GO) in combination with N-isopropylacrylamide (PIN). This simulation study has been performed by use of molecular dynamics. Interaction energies, hydrogen bond, and gyration radius were investigated. Results reveal that, compared with fullerene and GO, CNT is a better carrier for the co-adsorption and co-release of DOX and PAX. It can adsorb the drugs in plasma pH and release it in vicinity of cancerous tissues which have acidic pH. Investigating the number of hydrogen bonds revealed that PIN created many hydrogen bonds with water resulting in high hydrophilicity of PIN, hence making it more stable in the bloodstream while preventing from its accumulation. It is also concluded from this study that CNT and PIN would make a suitable combination for the delivery of DOX and PAX, because PIN makes abundant hydrogen bonds and CNT makes stable interactions with these drugs.

On the mutual diffusion properties of ethanol-water mixtures

The structural organization, the number of hydrogen bonds (H bond), and the self- and mutual diffusion coefficients of ethanol-water mixtures were studied by molecular dynamics simulation. It was found that both the numbers of H bonds per water and per ethanol decrease as the mole fraction of ethanol increases. The composition dependences and the relationships between the self- and the mutual diffusion coefficients were further discussed. The self-diffusion coefficient of water has a large drop as the concentration of ethanol increases from 0 to 0.3 and then it nearly keeps constant, while that of ethanol has a minimum around ethanol mole fraction of 0.5. The mutual diffusion coefficient could be divided into two parts, the kinematic factor and the thermodynamic factor. Both the kinematic and thermodynamic factors for ethanol-water mixtures were calculated. It was found that the change trend of mutual diffusion coefficients with the composition is mainly dependent on the thermodynamic factors.