Effect of the Side-Chain Length in Perfluorinated Sulfonic and Phosphoric Acid-Based Membranes on Nanophase Segregation and Transport: A Molecular Dynamics Simulation Approach

Platinum-Loaded Cerium Oxide Capable of Repairing Neuronal Homeostasis for Cerebral Ischemia-Reperfusion Injury Therapy

Effective neuroprotective agents are required to prevent neurological damage caused by reactive oxygen species (ROS) generated by cerebral ischemia-reperfusion injury (CIRI) following an acute ischemic stroke. Herein, it is aimed to develop the neuroprotective agents of cerium oxide loaded with platinum clusters engineered modifications (Ptn-CeO2). The density functional theory calculations show that Ptn-CeO2 could effectively scavenge ROS, including hydroxyl radicals (·OH) and superoxide anions (·O2 -). In addition, Ptn-CeO2 exhibits the superoxide dismutase- and catalase-like enzyme activities, which is capable of scavenging hydrogen peroxide (H2O2). The in vitro studies show that Ptn-CeO2 could adjust the restoration of the mitochondrial metabolism to ROS homeostasis, rebalance cytokines, and feature high biocompatibility. The studies in mice CIRI demonstrate that Ptn-CeO2 could also restore cytokine levels, reduce cysteine aspartate-specific protease (cleaved Caspase 3) levels, and induce the polarization of microglia to M2-type macrophages, thus inhibiting the inflammatory responses. As a result, Ptn-CeO2 inhibits the reperfusion-induced neuronal apoptosis, relieves the infarct volume, reduces the neurological severity score, and improves cognitive function. Overall, these findings suggest that the prominent neuroprotective effect of the engineered Ptn-CeO2 has a significant neuroprotective effect and provides a potential therapeutic alternative for CIRI.

Effect of ionic groups on the morphology and transport properties in a novel perfluorinated ionomer containing sulfonic and phosphonic acid groups: a molecular dynamics study

Combining the phosphonic acid group with the sulfonic acid group in PEMs has been shown to be an effective strategy for improving the fuel cell performance. However, the interplay of two different ionic groups and the resulting effect on the membrane properties have not been fully elucidated. Here, we used classical molecular dynamics simulation to investigate the morphologies, transport properties and effects of ionic groups in a novel perfluorinated PEM containing two ionic groups (PFSA-PFPA) in comparison to the corresponding homopolymers. Phase separations between hydrophilic and hydrophobic domains are confirmed in these PEMs and result from the evolution of water clusters formed around the ionic groups. The combination of both ionic groups brings a complicated morphological feature in PFSA-PFPA, with near-cylindrical aqueous domains of large length scales interconnected by tortuous domains of small sizes. And we found that the self-diffusion coefficients of water molecules are strongly related to morphologies, with the water transport in PFSA-PFPA lying between two analogous homopolymers. At the molecular level, we found that the sulfonic and phosphonic acid groups have distinct effects on the coordination behaviors and the dynamics of water molecules and hydronium ions. Strong electrostatic interactions lead to compact coordination structures and sluggish dynamics of hydronium ions around phosphonic acid groups, which determine the morphological evolution and transport properties in PFSA-PFPA. Our study affords insights into the relationship between molecular characteristics and transport properties bridged by phase-separated morphologies in a novel PEM containing both sulfonic acid and phosphonic acid groups, which deepens the understanding of the interplay between two ionic groups and may inspire the rational design of high-performance PEMs.

The selective heating effect of microwave irradiation on a binary mixture of water and polyethylene oxide: a molecular dynamics simulation approach

In this study, we investigate the molecular mechanisms of a microwave-driven selective heating process by performing molecular dynamics simulations for three different systems including pure water, pure polyethylene oxide (PEO), and water-PEO mixed systems in the presence of a microwave with two different intensities of electric field such as 0.001 V Å^-1 and 0.01 V Å^-1 at a frequency of 100 GHz. First, from performing molecular dynamics simulations of CO and CO₂ in the presence of the microwave, it is confirmed that the molecular dipole moment is responsible for the rotational motion induced by the oscillating electric field. Second, by analyzing the MD simulations of the pure water system, we discover that the dipole moment of water exhibits a time lag with respect to the microwave. During the heating process, however, the temperature, kinetic, and potential energies increase synchronously with the oscillating electric field of the microwave, showing that the heating of the water system is caused by the molecular reaction of water to the microwave. Comparing the water-PEO mixed system to the pure water and pure PEO systems, the water-PEO mixed system has a higher heating rate than the pure PEO system but a lower heating rate than the pure water system. Therefore, we conclude that heating the water-PEO mixed system is driven by water molecules selectively activated by microwave irradiation. We also calculate the diffusion coefficients of water molecules and PEO chains by describing their mean square displacements, demonstrating that the diffusion coefficients are increased in the presence of microwaves for both water and PEO in pure and mixed systems. Lastly, during the microwave heating process, the structures of the water-PEO mixed system are altered as a function of the intensity of electric field, which is mainly driven by the response of water molecules.

Thermoresponsive Conductivity of Graphene-Based Fibers

Smart materials are versatile material systems which exhibit a measurable response to external stimuli. Recently, smart material systems have been developed which incorporate graphene in order to share on its various advantageous properties, such as mechanical strength, electrical conductivity, and thermal conductivity as well as to achieve unique stimuli-dependent responses. Here, a graphene fiber-based smart material that exhibits reversible electrical conductivity switching at a relatively low temperature (60 °C), is reported. Using molecular dynamics (MD) simulation and density functional theory-based non-equilibrium Green's function (DFT-NEGF) approach, it is revealed that this thermo-response behavior is due to the change in configuration of amphiphilic triblock dispersant molecules occurring in the graphene fiber during heating or cooling. These conformational changes alter the total number of graphene-graphene contacts within the composite material system, and thus the electrical conductivity as well. Additionally, this graphene fiber fabrication approach uses a scalable, facile, water-based method, that makes it easy to modify material composition ratios. In all, this work represents an important step forward to enable complete functional tuning of graphene-based smart materials at the nanoscale while increasing commercialization viability.

DFT-Machine Learning Approach for Accurate Prediction of pKa

In this study, we propose a novel method of pK_a prediction in a diverse set of acids, which combines density functional theory (DFT) method with machine learning (ML) methods. First, the DFT method with B3LYP/6-31++G**/SM8 is used to predict pK_a, yielding a mean absolute error of 1.85 pK_a units. Subsequently, such pK_a values predicted from the DFT method are employed as one of 10 molecular descriptors for developing ML models trained on experimental data. Kernel Ridge Regression (KRR), Gaussian Process Regression, and Artificial Neural Network are optimized using three Pipelines: Pipeline 1 involving only hyperparameter optimization (HPO), Pipeline 2 involving HPO followed by a relative contribution analysis (RCA) and recursive feature elimination (RFE), and Pipeline 3 involving HPO followed by RCA and RFE on an expanded set of composite features. Finally, it is demonstrated that KRR with Pipeline 3 yields optimal pK_a prediction at an MAE of 0.60 log units. This algorithm was then utilized to predict the pK_a of 37 novel acids. The two most important features were determined to be the number of hydrogen atoms in the molecule and the degree of oxidation of the acid. The predicted pK_a values were documented for future reference.