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Rapid imaging of ion dynamics in battery materials

Particles in lithium-ion batteries are crucial for releasing positively and negatively charged lithium ions. The migration of these ions is a limiting factor for the batteries’ charge and discharge cycles. To develop fast charging batteries, engineers and scientists need to understand how ions in batteries travel. Now, researchers at the University of Cambridge published in the prestigious journal Nature an imaging approach that follows ion movement in functional battery materials in real-time. This technology helps to better understand how lithium-ion batteries work at sub-micrometer sizes and ultimately to construct batteries that charge in only a few minutes.

Scientists need to understand the ion dynamics of active particles to build better batteries but also other galvanic cells such as fuel cells or electrolyzers. Hitherto, traditional approaches for studying lithium-ion dynamics could not trace the rapid changes that occur in batteries that charge in minutes at sub-micrometer precision.

The problem

In lithium-ion batteries, two porous electrodes (positive and negative) are comprised of active particles: carbon, a metal oxide and a binder. The carbon and metal oxides act as electron conductors, while the binder glues the particles to hold the materials together. An electrolyte separates the two electrodes of the battery and acts as a conduit for ions to travel from one electrode to the other.

Engineers need to image the relevant physical and chemical interactions at least ten times faster than the operation time to track the internal ion dynamics of batteries for each of these processes. This is similar to choosing a camera shutter speed appropriate for filming sports – if the shutter speed is too slow, the camera will generate hazy images. The geometry of the active particles and the structure of the porous electrodes are of particular interest for battery development.

Each battery imaging technique has a unique image capture time, defining which battery functions can be accurately recorded. Previously existing approaches take a few minutes to collect an image; therefore, they can only catch processes that take many hours to complete.

Which is the new concept?

Notably, the researchers’ novel technique takes less than a second to acquire a picture, allowing for considerably faster processes to be studied than previously feasible. As an imaging tool, it is also capable of studying batteries while in use and has a sufficient spatial resolution. This sub micrometer resolution is required to track what happens in an active particle. Furthermore, by comparing the evolution of ion concentration in active particles spatially separated in the electrode, the approach can map ion dynamics at the electrode scale.

Methodology

The research team adapted an optical microscopy approach previously used in biology to follow lithium-ion mobility in active battery materials. This method involves passing a laser beam at electrochemically active battery particles storing or releasing lithium ions and then analyzing the scattered light. As additional lithium is stored, the local concentration of electrons in these particles varies. This changes the scattering pattern. As a result, the local change in lithium concentration correlates with the time development of the scattering signals and can be used to locate the particles.

During charge-discharge cycles, ‘active’ materials in battery electrodes store and release ions. The researchers describe in their publication a real-time imaging approach that uses light scattered from active particles to follow ion concentration changes. The intensity of scattering fluctuates with local ion concentration. In their approach, the evolution of scattering patterns over time indicated the system’s ion dynamics. As additional ions are stored in a particle, the colors of the contours show the change in scattering intensity over the previous 5-second period: red denotes an increase in intensity, while blue suggests a reduction. The shifting patterns correspond to the material’s passage from one phase to the next. When a central domain of one phase shrinks and surrounding domains of another phase grows, broken black lines show phase borders.

Conclusion

The new imaging technique can be used for almost all active materials that store lithium or other ions, suffering electronic changes as the ion concentration changes. Because standard approaches cannot directly track changes in local concentration throughout a particle during fast operation, the time variation of ion concentration in active particles remains poorly understood. The new solution will enable electrochemical engineers to test proposed mechanisms of ion transport in these materials by overcoming the imaging problem.

Limitations to this approach

It should be emphasized that the spatial resolution of the authors’ imaging technique is limited by a basic restriction imposed by the wavelength of the light. Shorter wavelengths are required to resolve finer details. In the presented work, the resolution was around 300 nanometers. Another point to consider is that laser scattering is the result of light interacting with just one object. Another drawback is that scattering results from light interacting with the particle’s first couple of atomic levels. As a result, this method only catches the ion movements in the 2D plane related to these atomic layers. Slower approaches, such as X-ray tomography, can be used to gather 3D information.

Way forward

It will be fascinating to follow up on the authors’ findings for individual particles and investigate porous electrodes under the far-from-equilibrium conditions of fast charging.

This approach could also investigate solid electrolytes, which are intriguing but poorly understood battery materials. Suppose light scattering from solid electrolytes varies with local ion concentration, as it does in active materials. In that case, the approach could be used to map how the ion distribution changes in such electrolytes as electric current travel through them. Other systems involving coupled ion and electron transport, such as catalyst layers in fuel cells and electrochemical gas sensors, could benefit from the optical scattering method as well.

In the future, thorough scattering tests using homogeneous particles could help to quantify the link between the scattering response and lithium-ion concentration. The scattering signals might then be converted to local concentrations using this correlation. However, the link between different materials will not always be the same. Machine-learning approaches could accelerate finding these links and automate light scattering analysis.

The authors’ imaging method also opens up the possibility of measuring chemical and physical (geometric) changes in active particles during battery operation at the same time. The difference between the scattering from a particle and that from other materials in a battery (such as the binder or electrolyte) could be used to determine the particle shape and how it evolves. The time required for light scattering a particle would reveal local changes in lithium concentration. These materials store much more energy than current active materials, and their adoption could reduce battery weight. This would be especially advantageous in electric vehicles, as it would allow for longer driving ranges.

The research provides previously unavailable insights into battery materials working in non-equilibrium situations. Their method for directly monitoring changes in active particles during operation will complement previous approaches that rely on destructive battery tests to infer internal alterations. As a result, it has the potential to transform the battery-design process.

Reference details

Merryweather, et al., 2021 “Operando optical tracking of single-particle ion dynamics in batteries”, Nature, 594, 522–528, doi:10.1038/s41586-021-03584-2

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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.

Results

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: https://doi.org/10.1016/j.nanoen.2020.105409 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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Turbocharged lithium batteries at high temperatures

One of the biggest hurdles for the electrification of road traffic is the long charging time for lithium batteries in electric vehicles. A recent research report has now shown that charging time can be reduced to 10 minutes while the battery is being heated.

A lithium battery can power a 320-kilometer trip after only 10 minutes of charging − provided that its temperature is higher than 60 °C while charging.

Lithium batteries that use lithium ions to generate electricity are slowly charged at room temperature. It takes more than three hours to charge, as opposed to three minutes to tank a car.

A critical barrier to rapid charging is the lithium plating, which normally occurs at high charging rates and drastically affects the life and safety of the batteries. Researchers at Pennsylvania State University in University Park are introducing an asymmetrical temperature modulation method that charges a lithium battery at an elevated temperature of 60 °C.

High-speed charging typically encourages lithium to coat one of the battery electrodes (lithium plating). This will block the flow of energy and eventually make the battery unusable. To prevent lithium deposits on the anodes, the researchers limited the exposure time at 60 °C to only ~10 minutes per cycle.

The researchers used industrially available materials and minimized the capacity loss at 500 cycles to 20%. A battery charged at room temperature could only be charged quickly for 60 cycles before its electrode was plated.

The asymmetrical temperature between charging and discharging opens up a new way to improve the ion transport during charging and at the same time achieve a long service life.

For many decades it was generally believed that lithium batteries should not be operated at high temperatures due to accelerated material degradation. Contrary to this conventional wisdom, the researchers introduced a rapid charging process that charges a cell at 60 °C and discharges the cell at a cool temperature. In addition, charging at 60 °C reduces the battery cooling requirement by more than 12 times.

In battery applications, the discharge profiles depend on the end user, while the charging protocol is determined by the manufacturer and can therefore be specially designed and controlled. The quick-charging process presented here opens up a new way of designing electrochemical energy systems that can achieve high performance and a long service life at the same time.

At Frontis Energy we also think that the new simple charging process is a promising method. We are looking forward to the market launch of this new rapid charging method.

(Photo: iStock)

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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)