Novel Filler-Filled-Type Polymer Electrolyte Membrane for PEFC Employing Poly(vinylphosphonic acid)-b-polystyrene-Coated Cellulose Nanocrystals as a Filler

Recent Developments on Bioinspired Cellulose Containing Polymer Nanocomposite Cation and Anion Exchange Membranes for Fuel Cells (PEMFC and AFC)

Hydrogen fuel cell (FC) technologies are being worked on as a possible replacement for fossil fuels because they produce a lot of energy and do not pollute the air. In FC, ion-exchange membranes (IEMs) are the vital components for ion transport between two porous electrodes. However, the high production cost of commercialized membranes limits their benefits. Various research has focused on cellulose-based membranes such as IEM with high proton conductivity, and mechanical, chemical, and thermal stabilities to replace the high cost of synthetic polymer materials. In this review, we focus on and explain the recent progress (from 2018 to 2022) of cellulose-containing hybrid membranes as cation exchange membranes (CEM) and anion exchange membranes (AEM) for proton exchange membrane fuel cells (PEMFC) and alkaline fuel cells (AFC). In this account, we focused primarily on the effect of cellulose materials in various membranes on the functional properties of various polymer membranes. The development of hybrid membranes with cellulose for PEMFC and AFC has been classified based on the combination of other polymers and materials. For PEMFC, the sections are associated with cellulose with Nafion, polyaryletherketone, various polymeric materials, ionic liquid, inorganic fillers, and natural materials. Moreover, the cellulose-containing AEM for AFC has been summarized in detail. Furthermore, this review explains the significance of cellulose and cellulose derivative-modified membranes during fuel cell performance. Notably, this review shows the vital information needed to improve the ion exchange membrane in PEMFC and AFC technologies.

Proton conductivity dependence on the surface polymer thickness of core-shell type nanoparticles in a proton exchange membrane

The proton exchange membrane (PEM) is the main component that determines the performance of polymer electrolyte fuel cells. The construction of proton-conduction channels capable of fast proton conduction is an important topic in PEM research. In this study, we have developed poly(vinylphosphonic acid)-block-polystyrene (PVPA-b-PS)-coated core-shell type silica nanoparticles prepared by in situ polymerization and a core-shell type nanoparticle-filled PEM. In this system, two-dimensional (2D) proton-conduction channels have been constructed between PVPA and the surface of silica nanoparticles, and three-dimensional proton-conduction channels were constructed by connecting these 2D channels by filling with the core-shell type nanoparticles. The proton conductivities and activation energies of pelletized PVPA-coated core-shell type nanoparticles increased depending on the coated PVPA thickness. Additionally, pelletized PVPA-b-PS-coated silica nanoparticles showed a good proton conductivity of 1.3 × 10^-2 S cm^-1 at 80 °C and 95% RH. Also, the membrane state achieved 1.8 × 10^-4 S cm^-1 in a similar temperature and humidity environment. Although these proton conductivities were lower than those of PVPA, they have advantages such as low activation energy for proton conduction, suppression of swelling due to water absorption, and the ability to handle samples in powder form. Moreover, by using PS simultaneously, we succeeded in improving the stability of proton conductivity against changes in the temperature and humidity environment. Therefore, we have demonstrated a highly durable, tough but still enough high proton conductive material by polymer coating onto the surface of nanoparticles and also succeeded in constructing proton-conduction channels through the easy integration of core-shell type nanoparticles.

A Critical Review on Artificial Intelligence for Fuel Cell Diagnosis

In recent years, fuel cell (FC) technology has seen a promising increase in its proportion in stationary power production. Several pilot projects are in operation across the world, with the number of running hours steadily rising, either as stand-alone units or as part of integrated gas turbine–electric energy plants. FCs are a potential energy source with great efficiency and zero emissions. To ensure the best performance, they normally function within a confined temperature and humidity range; nevertheless, this makes the system difficult to regulate, resulting in defects and hastened deterioration. For diagnosis, there are two primary approaches: restricted input information, which gives an unobtrusive, rapid yet restricted examination, and advanced characterization, which provides a more accurate diagnosis but frequently necessitates invasive or delayed tests. Artificial Intelligence (AI) algorithms have shown considerable promise in providing accurate diagnoses with quick data collecting. This work focuses on software models that allow the user to evaluate many different possibilities in the shortest amount of time and is a vital method for proper and dynamic analysis of such entities. The artificial neural network, genetic algorithm, particle swarm optimization, random forest, support vector machine, and extreme learning machine are common AI approaches discussed in this review. This article examines the modern practice and provides recommendations for future machine learning methodologies in fuel cell diagnostic applications. In this study, these six AI tools are specifically explained with results for a better understanding of the fuel cell diagnosis. The conclusion suggests that these approaches are not only a popular and beneficial tool for simulating the nature of an FC system, but they are also appropriate for optimizing the operational parameters necessary for an ideal FC device. Finally, observations and ideas for future research, enhancements, and investigations are offered.