Accelerating Solvent Selection for Type II Porous Liquids.

Type II porous liquids, comprising intrinsically porous molecules dissolved in a liquid solvent, potentially combine the adsorption properties of porous adsorbents with the handling advantages of liquids. Previously, discovery of appropriate solvents to make porous liquids had been limited to direct experimental tests. We demonstrate an efficient screening approach for this task that uses COSMO-RS calculations, predictions of solvent pKa values from a machine-learning model, and several other features and apply this approach to select solvents from a library of more than 11,000 compounds. This method is shown to give qualitative agreement with experimental observations for two molecular cages, CC13 and TG-TFB-CHEDA, identifying solvents with higher solubility for these molecules than had previously been known. Ultimately, the algorithm streamlines the downselection of suitable solvents for porous organic cages to enable more rapid discovery of Type II porous liquids.

DFT-Machine Learning Approach for Accurate Prediction of pKa.

In this study, we propose a novel method of pKa 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 pKa, yielding a mean absolute error of 1.85 pKa units. Subsequently, such pKa 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 pKa prediction at an MAE of 0.60 log units. This algorithm was then utilized to predict the pKa 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 pKa values were documented for future reference.

Distribution and Transport of CO2 in Hydrated Hyperbranched Poly(ethylenimine) Membranes: A Molecular Dynamics Simulation Approach.

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.

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

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.

Integrated, DC voltage-driven nucleic acid diagnostic platform for real sample analysis: Detection of oral cancer.

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.