Scientists use inorganic zinc-based compounds to vastly improve the capacity of sodium- and potassium-ion batteries.

Lithium-ion batteries (LIBs) are, by far, the most widely used type of rechargeable batteries, spanning numerous applications. These include consumer electronics, electric vehicles (e.g., Tesla cars), renewable energy systems, and spacecraft.

Although LIBs deliver the best performance in many aspects when compared to other rechargeable batteries, they have their fair share of disadvantages. Lithium is a rather scarce resource, and its price will rise quickly with its availability decreasing.

Moreover, lithium extraction and improperly discarded LIBs pose huge environmental challenges as the liquid electrolytes commonly used are toxic and flammable.

The shortcomings of LIBs have motivated researchers worldwide to look for alternative energy storage technologies. Sodium (Na)-ion batteries (NIBs) and potassium-ion batteries (KIBs) are two rapidly emerging options that are cost-efficient as well as sustainable. Both NIBs and KIBs are projected to be billion-dollar industries by the end of the decade.

Governments across the world, including that of the US, Austria, Hong Kong, Germany, and Australia, are promoting research and innovation in this field. Moreover, companies such as Faradion Limited, TIAMAT SAS, and HiNa Battery Technology Co. Ltd., are investing heavily in this technology. Both Contemporary Amperex Technology Co. Limited and Build Your Dreams are expected to introduce electric vehicle battery packs with NIBs soon.

Unfortunately, however, the capacity of the electrode materials used in NIBs and KIBs still lags behind that of LIBs. Against this backdrop, a research team led by Professor Shinichi Komaba from Tokyo University Science (TUS), Japan, has been working to develop groundbreaking high-capacity electrode materials for NIBs and KIBs.

In their latest study, published in Advanced Energy Materials on November 9, 2023, they report a new synthesis strategy for nanostructured “hard carbon” (HC) electrodes that deliver unprecedented performance. The study was co-authored by Mr. Daisuke Igarashi, Ms. Yoko Tanaka, and Junior Associate Professor Ryoichi Tatara from TUS, and Dr. Kei Kubota from the National Institute for Materials Science (NIMS), Japan.

But what is HC and why is it useful for NIBs and KIBs? Unlike other forms of carbon, such as graphene or diamond, HC is amorphous; it lacks a well-defined crystalline structure. Additionally, it is strong and resistant. In an earlier 2021 study, Prof. Komaba and his colleagues had found a way to use magnesium oxide (MgO) as a template during the synthesis of HC electrodes for NIBs, altering their final nanostructure.

The process had led to the formation of nanopores within the electrodes upon MgO removal, which, in turn, had vastly increased their capacity to store Na⁺ ions.

Motivated by their previous findings, the researchers explored whether compounds made from zinc (Zn) and calcium (Ca) could also be useful as nano-templates for HC electrodes. To this end, they systematically investigated different HC samples made using zinc oxide (ZnO) and calcium carbonate (CaCO₃) and compared their performance with the ones synthesized using magnesium oxide (MgO).

Preliminary experiments showed that ZnO was particularly promising for the negative electrode of NIBs. Accordingly, the researchers optimized the concentration of ZnO embedded in the HC matrix during synthesis, demonstrating a reversible capacity of 464 mAh g^–1 (corresponding to NaC_4.8) with a high initial Coulombic efficiency of 91.7% and a low average potential of 0.18 V vs. Na⁺/Na.

The team achieved remarkable results by incorporating this powerful electrode material into an actual battery. “The NIB fabricated using the optimized ZnO-templated HC as the negative electrode exhibited an energy density of 312 Wh kg^–1,” highlights Prof. Komaba. 

“This value is equivalent to the energy density of certain types of currently commercialized LIBs with LiFePO₄ and graphite and is more than 1.6 times the energy density of the first NIBs (192 Wh kg^–1), which our laboratory reported back in 2011.” Notably, the ZnO-templated HC also exhibited a significant capacity of 381 mAh g^–1 when incorporated into a KIB, further showcasing its potential.

Taken together, the results of this study show that using inorganic nanoparticles as a template to control the pore structure may provide an effective guideline for the development of HC electrodes. “Our findings prove that HCs are promising candidates for negative electrodes as an alternative to graphite,” concludes Prof. Komaba.

Shaping hard carbon to obtain exceptional large-capacity electrodes for sodium-ion batteries: It is possible to incorporate nanopores in hard carbon by using zinc oxide as a template during its synthesis. These pores enable the material to store many more charge carriers, making it a promising electrode candidate for sodium-ion batteries that can reach an energy density comparable to that of LiFePO4-type lithium-ion batteries. Image credit: Shinichi Komaba from TUS Japan

In turn, this could make NIBs viable for practical applications, such as the development of sustainable consumer electronics and electric vehicles as well as low carbon footprint energy storage systems for storing energy from solar and wind farms.

Source: Tokyo University of Science

“When you only have a few samples, know very little about a disease or might not have access to a high-level biosafety lab, our pipeline can be really valuable,” says Guckelsberger, adding that it can be used anywhere regardless of location, sample preparation equipment or type of microscope. In fact, the pipeline is versatile to test on any germ.

“We designed the test to be used for any emerging pathogen, increasing our readiness for future pandemics,” says Pietiäinen. “Certain components should be optimized for each new virus, but the beauty of the assay is that it can be used for different purposes. It’s already being used to study zoonotic viruses like the Puumala virus.”

Other automated cell-based assays, followed by AI-guided image analysis methods, are being used in the research group to study the drug responses to SARS-CoV-2 as well as to identify drugs that can kill patient-derived cancer cells ex vivo.

Beyond publishing their work and contributing to a better understanding of the pandemic, Guckelsberger and Pietiäinen share a common insight that this project taught them.

“When big questions come up in the world, we as scientists can’t work alone in silos. Experts from different fields, different universities and countries need to come together with a shared aim—in our case, data scientists, clinicians, computer scientists, biochemists,” says Pietiäinen.

“Working in a big team, which is not something we do often in computer science, was fascinating,” echoes Guckelsberger.

“One big challenge was communicating from different perspectives of expertise, for example making sense of what is happening at both ends of the pipeline from wet lab procedures to parameters to data and images. At the same time, this was a fantastic learning experience, and one that I wish to have more of in the future.”

While they employed well-established machine learning for each component of the pipeline, Guckelsberger says making the connection between biologists and computer scientists was one of the real advances. Using technology to resolve biological questions was a big takeaway for Pietiäinen, too.

“Combining microscopy with machine learning, not just for SARS-CoV-2, but to see personalized responses to drugs or to see the cellular phenotypes of rare genetic diseases, is powerful. A picture is worth a thousand words, that is also the case here.”

Source: Aalto University

For reasons of food security and economic incentive, farmers continuously seek to maximize their marketable crop yields. As plants grow inconsistently, at the time of harvesting, there will inevitably be variations in quality and size of individual crops. Therefore, Finding the optimal harvest time is a priority for farmers.

A new approach of using drones and artificial intelligence demonstrably improves this estimation by carefully and accurately analyzing individual crops to assess their likely growth characteristics.

Drone-based AI pipeline. A visual overview of the system to capture and analyze image data about crops, which then informs a model to help farmers know the best time to harvest their fields. Image credit: Guo et al. CC-BY

Some optimistic science fiction stories talk about a post-scarcity future, where human needs are catered for and hard labor is provided by machines. There are some ways in which this vision appears to predict some elements of current technological progress. One such area is in agricultural research, where automation has been making an impact.

For the first time, researchers, including those from the University of Tokyo, have demonstrated a largely automated system to improve crop yields, which can benefit many and may help pave the way for future systems that could one day harvest crops directly.

“The idea is relatively simple, but the design, implementation and execution is extraordinarily complex,” said Associate Professor Wei Guo from the Laboratory of Field Phenomics.

“If farmers know the ideal time to harvest crop fields, they can reduce waste, which is good for them, for consumers and the environment. But optimum harvest times are not an easy thing to predict and ideally require detailed knowledge of each plant; such data would be cost and time prohibitive if people were employed to collect it. This is where the drones come in.”

Guo has a background in both computer science and agricultural science, so is ideally suited to finding ways cutting-edge hardware and software could aid agriculture. He and his team have demonstrated that some low-cost drones with specialized software can image and analyze young plants — broccoli in the case of this study — and accurately predict their expected growth characteristics.

The drones carry out the imaging process multiple times and do so without human interaction, meaning the system requires little in terms of labor costs.

Data visualization on aerial photos. The cost of human labor and time involved prohibits manual cataloging of individual plants in a field. Here, the catalog data collected by the drones and produced by a deep learning system is superimposed onto photos of the fields. Image credit: Guo et al. CC-BY

“It might surprise some to know that by harvesting a field as little as a day before or after the optimal time could reduce the potential income of that field for the farmer by 3.7% to as much as 20.4%,” said Guo.

“But with our system, drones identify and catalog every plant in the field, and their imaging data feeds a model that uses deep learning to produce easy-to-understand visual data for farmers. Given the current relative low costs of drones and computers, a commercial version of this system should be within reach to many farmers.”

The team’s main challenge was in the image analysis and deep learning aspects. Collecting the image data itself is relatively trivial, but given the way plants move in the wind and how the light changes with time and the seasons, the image data contains a lot of variation that machines often find hard to compensate for.

So, when training their system, the team had to invest a huge amount of time labeling various aspects of images the drones might see, in order to help the system learn to identify what it was seeing correctly. The vast data throughput was also challenging — image data was often of the order of trillions of pixels, tens of thousands of times larger than even a high-end smartphone camera.

“I’m inspired to find more ways that plant phenotyping (measuring of plant growth traits) can go from the lab to the field in order to help solve the major problems we face,” said Guo.

Source:University of Tokyo

You can offer your link to a page which is relevant to the topic of this post.