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Making zinc-air batteries rechargeable using developed cobalt(II) oxide as a catalyst

Zinc-air batteries are a promising alternative to expensive lithium-ion batteries. Compared with lithium-ion technology, zinc-air batteries have a greater energy density, very low production cost, and superior safety. However, their fundamental inability to recharge has lowered their wide-scale adoption.

Zinc-air batteries use charged zinc particles to store large amounts of electricity at a time. When electricity is required, the charged zinc is combined with oxygen from the air (and water), releasing the stored electricity and producing zincate. This process is known as oxygen reduction reaction (ORR).

Theoretically, this zincate can again be broken down into oxygen and zinc ions by passing electricity through it. This process, in turn, is called oxygen evolution reaction (OER). Using these reactions, zinc-air batteries can be made rechargeable, competing with lithium-ion batteries.

The major challenge of the recharging process is the sluggish kinetics of the reactions which lead to poor cycle life. These batteries require a catalyst that could potentially enhance the ORR and the OER reactions, making their kinetics fast. Hence, the development of highly efficient catalysts is of paramount importance for rechargeable zinc-air batteries.

Previous studies have suggested transition-metal oxides as great bifunctional ORR / OER catalysts because of their ability to provide sites for the reversible adsorption of oxygen. But the methods involved in creating well-defined defects for reversible adsorption of oxygen in such oxides are challenging.

To investigate the use of cobalt(II) oxide nanosheets deposited on stainless steel or carbon cloth as a bifunctional catalyst, a group of researchers from different universities of China and Canada collaborated and conducted several experiments. Their research findings were published in the journal Nano Energy .

Research approach

Preparation of catalyst

Different nano-structures were prepared using simple heat treatment and electrodeposition to test them as bifunctional electrocatalysts. The type of nano-structures prepared were:

      • Cobalt hydroxide  nanosheets on steel and carbon cloth
      • Layered cobalt (II) oxide nanosheet on steel and carbon cloth
      • Cobalt (II) oxide on steel
      • Layered cobalt tetroxide nanosheet on steel

Material Characterization

To understand the characteristics of the prepared samples, various analyticaland tests were carried out:

Charging and discharging tests

Later discharge and charge cycling tests of single cells were operated by the battery testing system.


The simple heat treatment strategy created oxygen vacancy sites. According to the authors, layered cobalt-oxide nano-sheets exhibited excellent bifunctional ORR / OER performance. Investigations suggested abundant oxygen vacancies and cobalt sites be the reason for enhanced ORR / OER performance. Later, the developed layered cobalt-oxide nanosheets on steel were used as an electrode in a rechargeable zinc-air flow battery and a record-breaking cycle life of over 1,000 hours with nearly unchanged voltage was observed. Galvanostatic discharging-charging cycles also demonstrated long life and high energy efficiency.

This research carried out provides a new method to design highly efficient bifunctional ORR / OER catalysts that could be used to enhance the cycle life of rechargeable zinc-air flow batteries. At Frontis Energy we are looking forward to industrial applications.

(Photo: Engineersforum)

Reference: Wu et al., Cobalt (II) oxide nanosheets with rich oxygen vacancies as highly efficient bifunctional catalysts for ultra-stable rechargeable Zn-air flow battery, 2021

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Machine learning makes smarter batteries

Renewable energies, such as wind and solar energy are naturally intermittent. To balance their demand and supply, batteries of, for example, electric vehicles can be charged and act as an energy buffer for the power grid. Cars spend most of their time idle and could, at the same time, feed their electricity back into the grid. While this is still a dream of the future, commercialization of electric and hybrid vehicles is already creating a growing demand for long-lasting batteries, both for driving as well as grid buffering. Consequently, methods for evaluating the state of the battery will become increasingly important.

The long duration of battery health tests is a problem, hindering the rapid development of new batteries. Better battery life forcasting methods are therefore urgently needed but are extremely difficult to develop. Now, Severson and her colleagues report in the journal Nature Energy that machine learning can help to predict computer battery life by creating computer models. The published algorithms use data from early-stage charge and discharge cycles.

Normally, a figure of merit describes the health of a battery. It quantifies the ability of the battery to store energy relative to its original state. The health status is 100% when the battery is new and decreases with time. This is similar to the state of charge of a battery. Estimating the state of charge of a battery is, in turn, important to ensure safe and correct use. However, there is no consensus in the industry and science as to what exactly a battery’s health status is or how it should be determined.

The state of health of a battery reflects two signs of aging: progressive capacity decline and impedance increase (another measure of electrical resistance). Estimates of the state of charge of a battery must therefore take into account both the drop in capacity and the increase in impedance.

Lithium ion batteries, however, are complex systems in which both capacity fade and impedance increase are caused by multiple interacting processes. Most of these processes cannot be studied independently since they often occur in simultaneously. The state of health can therefore not be determined from a single direct measurement. Conventional health assessment methods include examining the interactions between the electrodes of a battery. Since such methods often intervene directly in the system “battery”, they make the battery useless, which is hardly desired.

A battery’s health status can also be determined in less invasive ways, for example using adaptive models and experimental techniques. Adaptive models learn from recorded battery performance data and adjust themselves. They are useful if system-specific battery information are not available. Such models are suitable for the diagnosis of aging processes. The main problem, however, is that they must be trained with experimental data before they can be used to determine the current capacity of a battery.

Experimental techniques are used to evaluate certain physical processes and failure mechanisms. This allows the rate of future capacity loss to be estimated. Unfortunately, these methods can not detect any intermittent errors. Alternative techniques use the rate of voltage or capacitance change (rather than raw voltage and current data). In order to accelerate the development of battery technology, further methods need to be found which can accurately predict the life of the batteries.

Severson and her colleagues have created a comprehensive data set that includes the performance data of 124 commercial lithium-ion batteries during their charge and discharge cycles. The authors used a variety of rapid charging conditions with identical discharge conditions. This method caused a change of the battery lives. The data covered a wide range of 150 to 2,300 cycles.

The researchers then used machine learning algorithms to analyze the data, creating models that can reliably predict battery life. After the first 100 cycles of each experimentally characterized battery their model already showed clear signs of a capacity fade. The best model could predict the lifetime of about 91% data sets studied in the study. Using the first five cycles, batteries could be classified into categories with short (<550 cycles) or long lifetimes.

The researchers’ work shows that data-driven modeling using machine learning allows forecasting the state of health of lithium-ion batteries. The models can identify aging processes that do not otherwise apparent in capacity data during early cycles. Accordingly, the new approach complements the previous predictive models. But at Frontis Energy, we also see the ability to combine generated data with models that predict the behavior of other complex dynamic systems.

(Photo: Wikipedia)

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Cobalt Nanocrystals Make Lithium-Ion Batteries Age More Slowly

In todays Li-ion batteries, cobalt oxide cathodes improve performance and durability. While, such cobalt cathodes show the same performance as nickel oxide cathodes, they come at a higher price. Nickel cathodes, in turn, crack and dissolve quickly, which reduces their lifespan. Nevertheless, nickel cathodes are very popular because they are so cheap.

Now, the research team led by Jaephil Cho of the Ulsan National Institute of Science and Technology in South Korea has developed a cathode made of more than 80% nickel. The researchers reported in the journal Energy & Environmental Science that a cathode coated with nanocrystals of cobalt aged more slowly than conventional nickel cathodes. After recharging 400 times at room temperature, the battery was able to retain 86% of its original capacity.

The novel nickel cathodes could help meet the growing demand for rechargeable batteries in electric vehicles if cobalt prices rise in the future.

(Photo: Wikipedia)