In this study, hydrated Nafion film in the catalyst layer of the cathode for a polymer electrolyte membrane fuel cell is investigated using the molecular dynamics simulation method, exhibiting different structural characteristics on Pt and carbon surfaces. First, it is found that water molecules, hydronium ions, and sulfonate groups are highly concentrated at the interfacial region between the Nafion phase and the Pt surface, whereas Nafion backbone chains are present in a high concentration at the interface between the Nafion phase and the carbon surface. Second, it is also found from pair correlation function analysis that the water molecules and sulfonate groups in the hydrated Nafion phase are more associated with the Pt surface compared to the carbon surface, which is due to their strong attractive interactions with the Pt surface that makes the dimension of the hydrated Nafion phase 4–7% thinner on the Pt surface. Third, it is observed from water-occupied volume analysis that water molecules on the carbon surface can form large-size water phase between the Nafion phase and the carbon surface because the Nafion–carbon interface is not tightly integrated due to their weak interaction. In these structural characteristics, it is demonstrated that the water diffusion and proton vehicular diffusion are suppressed in the interfacial region of the Pt surface due to the highly packed structures in the water phase as well as the polymer phase in addition to the strong molecular interaction with the Pt surface, whereas the proton hopping diffusion is enhanced due to the well-developed organized water phase via the hydrogen bonding network.

We present an integrated and low-cost microfluidic platform capable of extraction of nucleic acids from real biological samples. We demonstrate the application of this platform in pathogen detection and cancer screening. The integrated platform consists of three units including a pretreatment unit for separation of nucleic acids from lysates, a preconcentration unit for concentration of isolated nucleic acids and a sensing unit localized at a designated position on the chip for specific detection of the target nucleic acid. The platform is based on various electrokinetic phenomena exhibited by ion exchange membranes in a DC electrical field that allow them to serve as molecular filters, analyte preconcentrators and sensors. In this manuscript, we describe each unit of the integrated chip separately and show specific detection of a microRNA (miRNA 146a) biomarker associated with oral cancer as a proof-of-concept experiment. This platform technology can easily be extended to other targets of interest by optimizing the properties of the ion exchange membranes and the specific probes functionalized onto the sensors.

Hyperbranched poly­(ethylenimine) (HB-PEI) has been distinguished as a promising candidate for carbon dioxide (CO2) capture. In this study, we investigate the distribution and transport of CO2 molecules in a HB-PEI membrane at various hydration levels using molecular dynamics (MD) simulations. For this, model structures consisting of amorphous HB-PEI membranes with CO2 molecules are equilibrated at various hydration levels. Under dry conditions, the primary and secondary amines are highly associated with CO2, indicating that they would participate in CO2 capture via the carbamate formation mechanism. Under hydrated conditions, the pair correlations of CO2 with the primary and secondary amines are reduced. This result suggests that the carbamate formation mechanism is less prevalent compared to dry conditions, which is also supported by CO2 residence time analysis. However, in the presence of water molecules, it is found that the CO2 molecules can be associated with both amine groups and water molecules, which would enable the tertiary amine as well as the primary and secondary amines to capture CO2 molecules via the bicarbonate formation mechanism. Through our MD simulation results, the feasibilities of different CO2 capture pathways in HB-PEI membranes are demonstrated at the molecular level.

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.

The effect of side-chain length on the nanophase-segregated structure and transport in perfluorinated sulfonic acid (PFSA)-based and perfluorinated phosphoric acid (PFPA)-based membranes is investigated at 20 and 5 wt % water content conditions using a molecular dynamics simulation method. It is found using the pair correlation analysis that the longer side chain leads to more developed local water structures in the water phase at 20 wt % water content, observable in both membrane chemistries albeit more distinct in PFPA-based membranes. It is also confirmed from the structure factor analysis that large-scale nanophase segregation is enhanced with increasing side-chain length for PFPA membranes, whereas no significant change is observed at these scales for PFSA membranes. Next, it is revealed that the proton transport is increased by 0.004 S/cm in PFSA-based membranes with increasing side-chain length due to the enhanced vehicular and hopping mechanisms, whereas the proton transport in PFPA-based membranes is decreased by 0.002 S/cm despite improved nanophase segregation. As confirmed by the pair correlation function analysis, the diminished proton transport in PFPA-based membranes is attributed to the molecular association of phosphate groups with hydronium ions via hydrogen bond in the longer side-chain case, which is namely a hydronium-mediated bridge configuration. Such bridge configurations and correspondingly similar trends in proton transport are also observed at 5 wt % water content condition to a lesser extent. Our simulation study demonstrates that the proton transport is affected by the hydrogen-bonding network as well as by the nanophase segregation.