Alkaline fuel cell technology - A review

Ferriday, T.B.; Middleton, Hugh

doi:10.1016/j.ijhydene.2021.02.203

Cited by 222 publications

(153 citation statements)

References 127 publications

Supporting

Mentioning

121

Contrasting

Order By: Relevance

“…In addition to excellent chemical stability, resolving the tradeoff between ion conductivity and dimensional stability in membranes is prevalent research point. − Generally, the simplest method to realize high ion conductivity is to increase the ion exchange capacity (IEC), which leads to high water uptake (WU) in AEMs. Moderate water absorption in AEMs will facilitate the transportation of ions; however, excessive water may cause more swelling, which is detrimental to the application of AEMFCs.…”

Section: Introductionmentioning

confidence: 99%

Multication Cross-Linked Poly(p-terphenyl isatin) Anion Exchange Membranes for Fuel Cells: Effect of Cross-Linker Length on Membrane Performance

Zhao

Long

Wang

et al. 2021

ACS Appl. Energy Mater.

View full text Add to dashboard Cite

As a key component of anion exchange membrane fuel cells (AEMFCs), anion exchange membranes (AEMs) have been investigated in the last decades. Herein, a series of multication cross-linkers were introduced into side-chain-type poly(p-terphenyl isatin) to develop high-performance and long-term stable AEMs. Additionally, the effects of the hydrophilic cross-linker length on the membrane performance were systematically investigated. The resulting cross-linked membranes possess a low swelling ratio (<18% at 80 °C) and high tensile strength (51.1−58.3 MPa). Notably, the cross-linker length influences the AEM internal morphology. With hexyl as the spacer between backbones and cation groups in the cross-linker, 0.9q-PTI-6C exhibits the highest hydroxide ion conductivity of 118.5 mS/cm at 80 °C, which is ascribed to well-developed ion channels. Furthermore, alkyl spacer chains and cross-linked networks contribute to the excellent alkali stability of membranes. After immersion in 2 M NaOH for 1200 h at 80 °C, 0.9q-PTI-8C only shows 11 and 12.7% losses in ion conductivity and ion exchange capacity (IEC), respectively. The fuel cell fabricated using 0.9q-PTI-6C can achieve the maximum power density of 310 mW/cm 2 at 80 °C.

show abstract

Section: Introductionmentioning

confidence: 99%

Multication Cross-Linked Poly(p-terphenyl isatin) Anion Exchange Membranes for Fuel Cells: Effect of Cross-Linker Length on Membrane Performance

Zhao

Long

Wang

et al. 2021

ACS Appl. Energy Mater.

View full text Add to dashboard Cite

show abstract

“…LSM = Strontia-dopped lanthanum manganite [22]. One of the oldest FC types is the AFC [23][24][25]. The AFC was originally created for the Apollo missions [26].…”

Section: Types Of Fcsmentioning

confidence: 99%

A Critical Review on Artificial Intelligence for Fuel Cell Diagnosis

et al. 2022

View full text Add to dashboard Cite

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.

show abstract

“…A fuel cell is an electrochemical cell or device that enables the conversion of chemical energy to electrical energy [ 4 ]. There are various types of fuel cells, including alkaline fuel cells (AFCs)—mostly related to anionic exchange membranes (AEMs) [ 5 , 6 , 7 , 8 ], proton-exchange membrane fuel cells (PEMFC), also known as polymer electrolyte membrane (PEM) fuel cells [ 5 , 9 , 10 , 11 ], phosphoric acid fuel cells (PAFCs) [ 12 ], molten carbonate fuel cells (MCFCs) [ 13 ], solid oxide fuel cells (SOFCs) [ 14 , 15 ], enzymatic (bio)fuel cells (EFCs) [ 16 ], direct methanol fuel cells (DMFCs) [ 17 ], and others.…”

Section: Introductionmentioning

confidence: 99%

Molecular Modeling in Anion Exchange Membrane Research: A Brief Review of Recent Applications

et al. 2022

View full text Add to dashboard Cite

Anion Exchange Membrane (AEM) fuel cells have attracted growing interest, due to their encouraging advantages, including high power density and relatively low cost. AEM is a polymer matrix, which conducts hydroxide (OH−) ions, prevents physical contact of electrodes, and has positively charged head groups (mainly quaternary ammonium (QA) groups), covalently bound to the polymer backbone. The chemical instability of the quaternary ammonium (QA)-based head groups, at alkaline pH and elevated temperature, is a significant threshold in AEMFC technology. This review work aims to introduce recent studies on the chemical stability of various QA-based head groups and transportation of OH− ions in AEMFC, via modeling and simulation techniques, at different scales. It starts by introducing the fundamental theories behind AEM-based fuel-cell technology. In the main body of this review, we present selected computational studies that deal with the effects of various parameters on AEMs, via a variety of multi-length and multi-time-scale modeling and simulation methods. Such methods include electronic structure calculations via the quantum Density Functional Theory (DFT), ab initio, classical all-atom Molecular Dynamics (MD) simulations, and coarse-grained MD simulations. The explored processing and structural parameters include temperature, hydration levels, several QA-based head groups, various types of QA-based head groups and backbones, etc. Nowadays, many methods and software packages for molecular and materials modeling are available. Applications of such methods may help to understand the transportation mechanisms of OH− ions, the chemical stability of functional head groups, and many other relevant properties, leading to a performance-based molecular and structure design as well as, ultimately, improved AEM-based fuel cell performances. This contribution aims to introduce those molecular modeling methods and their recent applications to the AEM-based fuel cells research community.

show abstract

Alkaline fuel cell technology - A review

Cited by 222 publications

References 127 publications

Multication Cross-Linked Poly(p-terphenyl isatin) Anion Exchange Membranes for Fuel Cells: Effect of Cross-Linker Length on Membrane Performance

Multication Cross-Linked Poly(p-terphenyl isatin) Anion Exchange Membranes for Fuel Cells: Effect of Cross-Linker Length on Membrane Performance

A Critical Review on Artificial Intelligence for Fuel Cell Diagnosis

Molecular Modeling in Anion Exchange Membrane Research: A Brief Review of Recent Applications

Contact Info

Product

Resources

About