A statistical study of proton conduction in Nafion®-based composite membranes: Prediction, filler selection and fabrication methods

On the evaluating membrane flux of forward osmosis systems: Data assessment and advanced intelligent modeling

As an emerging desalination technology, forward osmosis (FO) can potentially become a reliable method to help remedy the current water crisis. Introducing uncomplicated and precise models could help FO systems' optimization. This paper presents the prediction and evaluation of FO systems' membrane flux using various artificial intelligence-based models. Detailed data gathering and cleaning were emphasized because appropriate modeling requires precise inputs. Accumulating data from the original sources, followed by duplicate removal, outlier detection, and feature selection, paved the way to begin modeling. Six models were executed for the prediction task, among which two are tree-based models, two are deep learning models, and two are miscellaneous models. The calculated coefficient of determination (R2 ) of our best model (XGBoost) was 0.992. In conclusion, tree-based models (XGBoost and CatBoost) show more accurate performance than neural networks. Furthermore, in the sensitivity analysis, feed solution (FS) and draw solution (DS) concentrations showed a strong correlation with membrane flux. PRACTITIONER POINTS: The FO membrane flux was predicted using a variety of machine-learning models. Thorough data preprocessing was executed. The XGBoost model showed the best performance, with an R2 of 0.992. Tree-based models outperformed neural networks and other models.

Electrochemical Properties and Structure of Membranes from Perfluorinated Copolymers Modified with Nanodiamonds

In this study, we aimed to design and research proton-conducting membranes based on Aquivion®-type material that had been modified with detonation nanodiamonds (particle size 4-5 nm, 0.25-5.0 wt. %). These nanodiamonds carried different functional groups (H, OH, COOH, F) that provided the hydrophilicity of the diamond surface with positive or negative potential, or that strengthened the hydrophobicity of the diamonds. These variations in diamond properties allowed us to find ways to improve the composite structure so as to achieve better ion conductivity. For this purpose, we prepared three series of membrane films by first casting solutions of perfluorinated Aquivion®-type copolymers with short side chains mixed with diamonds dispersed on solid substrates. Then, we removed the solvent and the membranes were structurally stabilized during thermal treatment and transformed into their final form with -SO3H ionic groups. We found that the diamonds with a hydrogen-saturated surface, with a positive charge in aqueous media, contributed to the increase in proton conductivity of membranes to a greater rate. Meanwhile, a more developed conducting diamond-copolymer interface was formed due to electrostatic attraction to the sulfonic acid groups of the copolymer than in the case of diamonds grafted with negatively charged carboxyls, similar to sulfonic groups of the copolymer. The modification of membranes with fluorinated diamonds led to a 5-fold decrease in the conductivity of the composite, even when only a fraction of diamonds of 1 wt. % were used, which was explained by the disruption in the connectivity of ion channels during the interaction of such diamonds mainly with fluorocarbon chains of the copolymer. We discussed the specifics of the mechanism of conductivity in composites with various diamonds in connection with structural data obtained in neutron scattering experiments on dry membranes, as well as ideas about the formation of cylindrical micelles with central ion channels and shells composed of hydrophobic copolymer chains. Finally, the characteristics of the network of ion channels in the composites were found depending on the type and amount of introduced diamonds, and correlations between the structure and conductivity of the membranes were established.

Materials discovery of ion-selective membranes using artificial intelligence

Significant attempts have been made to improve the production of ion-selective membranes (ISMs) with higher efficiency and lower prices, while the traditional methods have drawbacks of limitations, high cost of experiments, and time-consuming computations. One of the best approaches to remove the experimental limitations is artificial intelligence (AI). This review discusses the role of AI in materials discovery and ISMs engineering. The AI can minimize the need for experimental tests by data analysis to accelerate computational methods based on models using the results of ISMs simulations. The coupling with computational chemistry makes it possible for the AI to consider atomic features in the output models since AI acts as a bridge between the experimental data and computational chemistry to develop models that can use experimental data and atomic properties. This hybrid method can be used in materials discovery of the membranes for ion extraction to investigate capabilities, challenges, and future perspectives of the AI-based materials discovery, which can pave the path for ISMs engineering.

Exploration of the Polarization Curve for Proton-Exchange Membrane Fuel Cells

The polarization curve is the most important profile to evaluate the performance of proton-exchange membrane fuel cells (PEMFCs). To explore the important thermodynamic parameters and their correlation with the composition, fabrication, and operational settings, a comprehensive data set consisting of 446 polarization curves from 191 perfluorosulfonate and 255 sulfonated hydrocarbon-based PEMs is collected. Then, a Markov chain Monte Carlo simulation within the Bayesian frame provides higher than 93% confidence to extract six important thermodynamic parameters including open-circuit potential, the transfer coefficient, the current loss, the reference exchange current density, the internal resistance, and the limiting current density. An extreme gradient boosting algorithm affords a mean determinative coefficient of 0.89 to predict the whole polarization curve and a confidence of 94% to predict the peak power density (PPD). Both approaches to explore the polarization curve for PEMFCs show good robustness in the blind test. Overall, the PPD is positively correlated with the ion-exchange capacity of the polymer, operational temperature, and humidity and is negatively affected by internal resistance, membrane thickness, and the loading of the catalyst. The flow rate of fuels can effectively enhance them, while the increase of catalyst loading or fuel concentration shows deleterious impacts.

Membrane materials for energy production and storage

Ion exchange membranes are widely used in chemical power sources, including fuel cells, redox batteries, reverse electrodialysis devices and lithium-ion batteries. The general requirements for them are high ionic conductivity and selectivity of transport processes. Heterogeneous membranes are much cheaper but less selective due to the secondary porosity with large pore size. The composition of grafted membranes is almost identical to heterogeneous ones. But they are more selective due to the lack of secondary porosity. The conductivity of ion exchange membranes can be improved by their modification via nanoparticle incorporation. Hybrid membranes exhibit suppressed transport of co-ions and fuel gases. Highly selective composite membranes can be synthesized by incorporating nanoparticles with modified surface. Furthermore, the increase in the conductivity of hybrid membranes at low humidity is a significant advantage for fuel cell application. Proton-conducting membranes in the lithium form intercalated with aprotic solvents can be used in lithium-ion batteries and make them more safe. In this review, we summarize recent progress in the synthesis, and modification and transport properties of ion exchange membranes, their transport properties, methods of preparation and modification. Their application in fuel cells, reverse electrodialysis devices and lithium-ion batteries is also reviewed.