UO ecologists secure $2 million to boost soil health of Oregon hazelnut farms

A mix of native wildflowers and volcanic rock dust can offer climate resilience for filbert orchards

University of Oregon

 

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Marissa Lane-Massee, a research assistant at the University of Oregon and a fourth-generation hazelnut farmer, is researching and developing a cover crop seed mix to bring climate resilience to hazelnut orchards. Photo by Nicolas Walcott, University of Oregon Communications.

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Credit: Photo by Nicolas Walcott, University of Oregon Communications.

Ecologists from the University of Oregon have designed a system to improve soil health and strengthen the long-term vitality of the state’s hazelnut industry — and now they’ve received $2 million to test it on 20 farms.

 

Oregon produces 99 percent of the nation’s hazelnuts, but the escalation of global extreme heat, which brings dry soil and scalded plants, threatens the agricultural productivity of the region.

 

A promising solution: carpeting the orchard floor with native wildflowers and basalt dust.

 

“To be resilient to climate change and to minimize climate change, you need healthy soil,” said Lauren Hallett, an associate professor of environmental studies and biology at the UO’s College of Arts and Sciences.

 

For the past five years, Hallett and her colleague Marissa Lane-Massee, a research assistant at the UO and fourth-generation hazelnut farmer, have worked together to create cover crop seed mixes that keep agricultural soils cooler during increasingly hotter seasons without interfering with the harvest. Compared to bare soil, a blanket of cover crops can better regulate soil temperature and enhance water retention and soil microbiology.

 

They’ve tested the approach on hazelnut farms managed by Lane-Massee and her family, with promising results. Now, to show how the system might scale up, the research team has received  $2 million from the U.S. Department of Agriculture. They’ll partner with 20 hazelnut farms across the Willamette Valley to demonstrate the ecological and economic benefits and provide incentives and technical support for adoption.

 

U.S. Sen. Ron Wyden of Oregon established the USDA program through his work on the Inflation Reduction Act. The effort built on his earlier work on the 2018 Farm Bill to promote soil health and climate resilience.

 

“Oregon is great at both growing things and adding value to them, but we need to make sure we do both in a sustainable way,” Wyden said. “I am always eager to support programs that tackle challenges farmers face while helping address the climate crisis.”

 

Despite its whispered potential, cover cropping historically has had a negative stigma among hazelnut farmers, Lane-Massee said. The orchard ground is conventionally left bare because of concerns that adding vegetation would tangle up fallen nuts and mess with harvesting, she said. A clean orchard floor is considered a good orchard floor.

 

“My grandma always said you got to keep the orchard like a golf course, so that when you go to harvest, the nuts roll right across the ground and into the machine,” Lane-Massee said. “But there's also cultural and aesthetic reasons, like wanting an aesthetically pleasing understory with smooth floors and rows that are neat and tidy."

 

Unlike tangle-prone grasses or weeds, the research team’s cover crop mix includes native wildflowers, including camas, checkermallow and phacelia, that harmoniously follow the hazelnut lifecycle. The cover crops sprout in late fall, grow to a small ground cover in winter and bloom taller during spring and summer. They improve soil integrity by acting as a shield from the sun, retaining soil moisture and preventing erosion.

 

By autumn when trees drop their hazelnuts, the wildflowers have already died back, allowing for easy, untangled picking. To validate this, Lane-Massee checks how many hazelnuts remain unpicked in each plot after the harvesting machines plow through as part of their data collection process. The ideal is two nuts or fewer to avoid profit losses, she said.

 

As perennials, the cover crops grow back once the fall rains begin.

 

“You never have to reseed. It’s a one-time input,” Lane-Massee said.

 

The cover crop mix will be used alongside basalt dust amendments, which can potentially help mitigate climate change. When basalt gets weathered down by rain or wind, a chemical reaction occurs that removes carbon dioxide from the atmosphere and converts it into stable minerals. These wash into local streams and rivers and eventually flow into oceans where they stay trapped on the seafloor for thousands of years — a tactic to address carbon pollution.

 

Spreading the dust can also increase the pH of the soil, serving as a carbon-sequestering alternative to conventional lime. The process has the potential to scale up quickly because basalt powder, a byproduct of mining, isn’t in short supply with Oregon’s Columbia Plateau as a local source, Hallett said.

 

The Lane-Massee Farm is the first hazelnut orchard to use basalt dust amendments and plans to investigate how it can be scaled up to commercial farming. Measuring how much carbon gets stored and the amount of powder to apply for the best results is hard to ascertain, Hallett added, but basalt dust is a very compelling source of permanent carbon removal.

 

“This is potentially one of the most promising natural climate solutions but also one with the biggest range of uncertainty,” she said.

 

Moreover, what works in one orchard may not work for another.

 

“Every farm you go to has a different story,” Lane-Masse said.

 

In response, the research team will customize the cover crop seed mix to each partnering orchard based on its canopy conditions, including the soil type and amount of sunlight available, and the farmers’ needs.

 

“I hope that, with the research we're doing, we can offer more of a tailored scientific perspective and experience to what each individual farmer is doing,” Lane-Massee said. “The blanket science will not work for every situation. It's really important that science learns from the people it's trying to help and that farmers learn from science.”

 

A barrier, however, is the expenses. Currently, native wildflower seeds and basalt dust amendments are not as cost effective or widely available as conventional methods, Hallett said. But in order to make them economically feasible, you need to demonstrate the practice as powerful, she said.

 

"It’s a bit of a chicken-and-egg problem,” Hallett said.

 

The research pair said it will likely take a generation, or more, until the soil health management system becomes standard practice. But they believe the new federal backing could be the tipping point in gaining industry and agricultural support.

 

“I hope someday when I drive down I-5, I just see fields of wildflowers and happy growers,” Hallett said.

Some regrowth of the cover crops a week after a rainy day in the fall on the Lane-Massee farm in Oregon. Photo by Nicolas Walcott, University of Oregon Communications.

Loaded totes of hazelnuts harvested on the Lane-Masse farm in Oregon. Photo by Nicolas Walcott, University of Oregon Communications.

Credit

Photo by Nicolas Walcott, University of Oregon Communications.

 

Producing liquid hydrogen using environmentally friendly technology

Peer-Reviewed Publication

University of Groningen

 

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Upper left: the magnetocaloric material starts in the magnetically disordered (paramagnetic) state at temperature T. Upper right: an external magnetic field is applied and causes the material to enter the magnetically ordered (ferromagnetic) state, with a consequent rise in temperature to T+ΔT. Lower right: heat is transferred away from the material to reduce its temperature back to T. Lower left: the magnetic field is removed and causes the material to lose its magnetic order, returning to the paramagnetic state and decreasing the temperature to T-ΔT. Heat can now be removed from the substance to be cooled (hydrogen), raising the temperature of the magnetocaloric material back to T (upper left

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Credit: University of Groningen / Blake lab

If we want to use hydrogen as fuel for cars or airplanes, or for chemical storage of excess renewable energy, it would be most efficient if it were liquid. However, this would require cooling it down to minus 253°C or 20°K, which is very energy-consuming when using a conventional cooling technology.

A team of scientists led by Graeme Blake, assistant professor of Inorganic Chemistry at the University of Groningen (the Netherlands), has been researching a more energy-efficient cooling method called magnetocaloric cooling. This method involves using materials that will heat up when they are exposed to a magnetic field. The heat is then transferred to a ‘heat sink’, which leaves the material - and its surroundings - colder once the magnetic field is removed. The method not only consumes less energy, but also eliminates the need for refrigerant gases, which have a strong greenhouse effect.

Blake used magnetocaloric cooling to reach 20°K, cold enough to liquify hydrogen. This has been done before, but only with materials containing rare-earth metals. The mining of these metals consumes a lot of energy and can lead to environmental problems. What’s revolutionary about Blake’s material is that it does not contain these metals: ‘Our material, or a future variant of it, could probably reduce the cost and improve the environmental friendliness of this cooling technology.’

Reference: J.J.B. Levinsky, B. Beckmann, T. Gottschall, D. Koch, M. Ahmadi, O. Gutfleisch & G.R. Blake: Giant magnetocaloric effect in a rare-earth-free layered coordination polymer at liquid hydrogen temperatures, Nature Communications.

This image shows the crystal structure of the new magnetocaloric material: cobalt hydroxide layers are shaded pink, sulfate ions are shaded yellow, oxygen atoms are red, carbon atoms are brown, nitrogen atoms are light blue, hydrogen atoms are white.

A photograph of some crystals of the magnetocaloric material

Credit

University of Groningen / Blake lab

Journal

Nature Communications

DOI

10.1038/s41467-024-52837-x 

Method of Research

Experimental study

Subject of Research

Not applicable

Article Title

Giant magnetocaloric effect in a rare-earth-free layered coordination polymer at liquid hydrogen temperatures

 

UChicago scientist develops paradigm to predict behavior of atmospheric rivers

Study by Asst. Prof. Da Yang provides powerful framework that sheds light on key processes driving extreme weather patterns

University of Chicago

When torrential rains and powerful winds hit densely populated coastal regions, whole cities can be destroyed—but governments and residents can take precautions with sufficient warning.

Many of these coastal deluges are caused by atmospheric rivers—regions of concentrated water vapor carried along on strong winds, sometimes called “rivers in the sky.” Meteorologists monitor them, but the ability to predict exactly how an atmospheric river might behave based on its underlying physics would offer more precise forecasts.

In a paper published today in Nature Communications, senior author Da Yang, assistant professor of geophysical sciences at the University of Chicago, and first author Hing Ong, a postdoctoral researcher formerly in Yang’s group and now at Argonne National Laboratory, describe a new equation they developed to better understand the processes that drive atmospheric rivers.

They hope the new framework will enhance the accuracy of atmospheric river predictions, especially for extreme weather events and in the context of a changing climate. This improved, process-level understanding also supports clearer communication of extreme weather forecast results.

A global phenomenon

Atmospheric rivers are long, narrow regions of concentrated water vapor accompanied by strong winds that carry moisture from the tropics toward the poles. They can transport as much as 15 times the amount of water that flows through the mouth of the Mississippi River, and they can bring heavy rain, snow, and strong winds. Up to half of California’s annual precipitation is brought by atmospheric rivers.

While the west coast of North America is particularly susceptible to extreme precipitation carried by atmospheric rivers—nicknamed a “Pineapple Express” when it originates around Hawaii—these rivers in the sky occur worldwide. On average, there are five in the northern midlatitudes and five in the southern midlatitudes at any given point, moving west to east. They aren’t all powerful enough to cause damaging floods and landslides; weaker systems can be beneficial, replenishing reservoirs and relieving droughts.

Atmospheric rivers are an essential element of the global climate, and understanding them will help improve the ability to forecast weather, manage water resources, and predict flood risk. Much of the existing research on atmospheric rivers involves characterization: monitoring, tracking, and rating them to help convey their hazard level. But what has been lacking is a way to determine an atmospheric river’s evolution.

“One stone, two birds”

Atmospheric rivers are monitored using a metric called integrated vapor transport (IVT), which describes the amount and velocity of water vapor moving through the atmosphere.

This metric is enough to develop tracking and monitoring algorithms, but to address fundamental questions about the evolution of atmospheric rivers, scientists need a governing equation. This is a mathematical expression that describes how a system changes based on specific rules or principles.

A governing equation would let scientists ask big-picture questions, Yang said, such as: “What provides energy to form and sustain atmospheric rivers? And why do they move eastward?”

Deriving the framework to answer these questions required the team to develop a quantity that combines the water vapor amount and the energy of strong winds into one variable: integrated vapor kinetic energy (IVKE).

The new equation is as effective and efficient as IVT at tracking and monitoring atmospheric rivers. But it has “the added benefit of being an intuitive first principle-based governing equation,” said Yang, “that can tell us what makes an atmospheric river stronger, what dissipates it, and what makes it propagate eastward—in real-time.”

The breakthrough adds physical process–level understanding to the statistical analysis of atmospheric rivers. The working title of the paper that describes this versatile framework was “One Stone, Two Birds.”

Using this new framework, Yang’s team found that atmospheric rivers mainly increase in strength because potential energy converts into kinetic energy. The rivers weaken due to condensation and turbulence and travel eastward due to the horizontal movement of kinetic energy and moisture by air currents.

Weather and a changing climate

The National Oceanic and Atmospheric Administration (NOAA), the primary center responsible for weather forecasting, researches, monitors, and publicizes information on atmospheric rivers. Yang suggested that his team’s new framework complements NOAA’s IVT-based analyses, offering real-time diagnostics that provide a stronger physical basis for forecast results. This approach boosts confidence in predictions, especially for extreme events, and aids in diagnosing model performance—ultimately guiding improvements in forecasting models.

The role of climate change in the evolution of atmospheric rivers is also a topic of interest. “We know that with climate change, the amount of water vapor is increasing,” said Yang. “Under the assumption that the circulation doesn’t change much, you may expect that the individual atmospheric river may get stronger.”

The study did not investigate that relationship, but it will be one of the team’s next steps. A new postdoctoral researcher in Yang’s lab, Aidi Zhang, will use the new framework to study how climate change impacts atmospheric rivers using vapor kinetic energy.

This research is a new area for Yang, although not so distant from his expertise, focusing on convective storms in the tropical atmosphere. Before joining UChicago, Yang lived in California for 15 years, which piqued his interest in atmospheric rivers. And “now that I live in higher latitudes,” he said, “I should pay more attention to these midlatitude storms.”

Ong, H. and Yang, D. Vapor Kinetic Energy for the Detection and Understanding of Atmospheric Rivers. Nat. Comm. (2024)

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Journal

Nature Communications

DOI

10.1038/s41467-024-53369-0 

Article Title

UChicago scientist develops paradigm to predict behavior of atmospheric rivers

Article Publication Date

4-Nov-2024

 

Defibrillation devices can save lives using 1,000 times less electricity

An optimized model could further reduce the energy needed, decrease pain and tissue damage

American Institute of Physics

 

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A computer model of the voltage field in a portion of the atria, with each box progressing in time up to 20 seconds after fibrillation begins. The authors discovered adjusting the duration and the smooth variation in time of the voltage supplied by defibrillation devices is a more efficient mechanism that reduces the energy needed to stop fibrillation by three orders of magnitude.

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Credit: Alejandro Garzon and Roman O. Grigoriev

WASHINGTON, Nov. 5, 2024 – In a paper published this week in Chaos, by AIP Publishing, researchers from Sergio Arboleda University in Bogotá, Colombia, and the Georgia Institute of Technology in Atlanta used an electrophysiological computer model of the heart’s electrical circuits to examine the effect of the applied voltage field in multiple fibrillation-defibrillation scenarios. They discovered far less energy is needed than is currently used in state-of-the-art defibrillation techniques.

“The results were not at all what we expected. We learned the mechanism for ultra-low-energy defibrillation is not related to synchronization of the excitation waves like we thought, but is instead related to whether the waves manage to propagate across regions of the tissue which have not had the time to fully recover from a previous excitation,” author Roman Grigoriev said. “Our focus was on finding the optimal variation in time of the applied electric field over an extended time interval. Since the length of the time interval is not known a priori, it was incremented until a defibrillating protocol was found.”

The authors applied an adjoint optimization method, which aims to achieve a desired result, defibrillation in this case, by solving the electrophysiologic model for a given voltage input and looping backward through time to determine the correction to the voltage profile that will successfully defibrillate irregular heart activity while reducing the energy the most.

Energy reduction in defibrillation devices is an active area of research. While defibrillators are often successful at ending dangerous arrhythmias in patients, they are painful and cause damage to the cardiac tissue.

“Existing low-energy defibrillation protocols yield only a moderate reduction in tissue damage and pain,” Grigoriev said. “Our study shows these can be completely eliminated. Conventional protocols require substantial power for implantable defibrillators-cardioverters (ICDs), and replacement surgeries carry substantial health risks.” 

In a normal rhythm, electrochemical waves triggered by pacemaker cells at the top of the atria propagate through the heart, causing synchronized contractions. During arrhythmias, such as fibrillation, the excitation waves start to quickly rotate instead of propagating through and leaving the tissue, as in normal rhythm.

“Under some conditions, an excitation wave may or may not be able to propagate through the tissue. This is called the ‘vulnerable window,’” Grigoriev said. “The outcome depends on very small changes in the timing of the excitation wave or very small external perturbations.

“The mechanism of ultra-low-energy defibrillation we uncovered exploits this sensitivity. Varying the electrical field profile over a relatively long time interval allows blocking the propagation of the rotating excitation waves through the ‘sensitive’ regions of tissue, successfully terminating the irregular electric activity in the heart.”

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The article “Ultra-low-energy defibrillation through adjoint optimization” is authored by Alejandro Garzon and Roman O. Grigoriev. It will appear in Chaos on Nov. 5, 2024 (DOI: 10.1063/5.0222247). After that date, it can be accessed at https://doi.org/10.1063/5.0222247.

ABOUT THE JOURNAL

Chaos is devoted to increasing the understanding of nonlinear phenomena in all areas of science and engineering and describing their manifestations in a manner comprehensible to researchers from a broad spectrum of disciplines. See https://pubs.aip.org/aip/cha.

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Journal

Chaos An Interdisciplinary Journal of Nonlinear Science

DOI

10.1063/5.0222247 

Article Title

Ultra-low-energy defibrillation through adjoint optimization

Article Publication Date

5-Nov-2024

 

Education modules build student and instructor skills

Undergraduate curriculum in ecological forecasting builds student and instructors’ quantitative literacy and data science skills

Virginia Tech

 

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Students in Cayelan Carey's class are working on the Macrosystems EDDIE modules with assistance from Mary Lofton (at far right).

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Credit: Photo courtesy of Cayelan Carey.

A series of hands-on teaching modules created and shared by Virginia Tech researchers has filled a gap in data science training opportunities for environmental science undergraduate students and instructors, reaching more than 35,000 students at more than 50 colleges and universities globally in the last seven years.

Researchers built the modules, called Macrosystems EDDIE, which stands for Environmental Data-Driven Inquiry and Exploration, in 2017. This fall, a comprehensive study of the program’s effectiveness was published in the journal BioScience, confirming what the researchers long suspected – the modules are working.

“These modules are novel because of a dearth of materials available for undergraduates, specifically on ecological forecasting, which is a rapidly emerging sub-discipline within ecology,” said Mary Lofton, postdoctoral associate with the Center for Ecosystem Forecasting and lead author of the report. “The goal is to introduce students to some of the core concepts of forecasting in this really user-friendly interface.” 

Designed by researchers in the Center for Ecosystem Forecasting, the modules were created to be easily integrated into existing coursework. They aim to complement educators’ work teaching ecological concepts and quantitative skills, such as data visualization, modeling, and analysis.

“I appreciated how the modules allowed students to better understand forecasting including the data requirements, integration of models, and uncertainty associated with forecasts,” said David Richardson, professor of biology at the State University of New York at New Paltz. “The students expressed the value of learning these concepts as they apply to their fields of interest (e.g., environmental science) or in understanding forecasting from a variety of uses like weather apps.”

Some of the distinct features of the modules include the following:

• Being unsequenced and available in an à la carte fashion
• Three activities for each module that can stand alone or build on each other, allowing for flexibility in the time required to finish
• The incorporation of large, high-frequency data sets from a range of publicly-available repositories that represent real-world data
• Hands-on activities that allow students to manipulate components within the model to develop an intuition for interpreting how the changes affect model forecasts
•  Flexibility with knowledge of coding and access to coding software; modules can be completed in interactive webpages or with versions that allow students and instructors to view and edit module code. The software is also free and publicly available
•  Availability on an open-source platform

"Our modules constitute one of the first formalized data science curricula on ecological forecasting for undergraduates,” Lofton said.

Macrosystems EDDIE was the brainchild of Cayelan Carey, professor in biological sciences and co-director of the Center for Ecosystem Forecasting. With the support of two National Science Foundation grants, Carey led the creation of the program while teaching the essential concepts of macrosystems ecology. 

“All our modules are carefully designed to teach both ecological concepts and quantitative skills using an established and tested pedagogical framework; they undergo rigorous assessment and peer review in partnership with the Science Education Research Center at Carleton College; and they are continuously revised in response to student and faculty feedback,”  said Carey, an affiliated faculty of the Fralin Life Sciences Institute's Global Change Center. 

According to Quinn Thomas, a co-investigator on the Macrosystems EDDIE program, the unprecedented rate of change of ecosystems around the globe have provided an impetus for researchers to apply macrosystems ecology to forecast ecosystem changes under alternate climate and land use scenarios. Producing this information, which will be critical to decision makers, takes training that integrates disparate concepts and skills with which many instructors lack familiarity.

“One of the findings of this analysis is that instructors who are using our modules in their classroom to teach their students are then more likely to use these approaches in their research. We’re essentially teaching the teachers and enabling them to do a different type of science, which is exciting,” said Thomas, professor of forest resources and environmental conservation and co-director of the Center for Ecosystem Forecasting. 

The researchers said the rapidly changing ecosystems are requiring increased complex skills from water managers and natural resource custodians. The modules aim to develop a training program that teaches students macrosystems ecology while also enriching their quantitative skills to build a diverse workforce. 

And so far, the modules are hitting their mark.

“Between Fall 2021 and Fall 2022, over 800 ecology lab students were introduced to ecological forecasting through the Macrosystems EDDIE module,” said Kaitlin J. Farrell, Ph.D., at the University of Georgia. “Working through the module made it easy to integrate these cutting-edge ecological concepts into our curriculum.” 

Researchers:

• Mary E. Lofton, postdoctoral research associate in biological sciences
• Tadhg N. Moore, postdoctoral research associate in biological sciences
• Whitney M. Woelmer, postdoctoral research associate in biological sciences
• R. Quinn Thomas, professor in forest resources and environmental conservation
• Cayelan C. Carey, professor in biological sciences

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Journal

BioScience

Article Title

A modular curriculum to teach undergraduates ecological forecasting improves student and instructor confidence in their data science skills Get access Arrow

 

Leveraging machine learning to find promising compositions for sodium-ion batteries

Researchers optimize the composition of a multi-element transition metal oxide to achieve exceptional energy density in sodium-ion batteries

Tokyo University of Science

 

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Researchers utilized a machine learning-based strategy to explore and optimize the ratio of transition metals within multi-element materials for sodium-ion batteries. The model analyzes various compositional combinations and predicts the most promising candidates, reducing the need for extensive experimental testing and making the search for high-performance battery materials more time- and cost-effective.

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Credit: Shinichi Komaba from TUS, Japan

Energy storage is an essential part of many rapidly growing sustainable technologies, including electric cars and renewable energy generation. Although lithium-ion batteries (LIBs) dominate the current market, lithium is a relatively scarce and expensive element, creating both economic and supply stability challenges. Accordingly, researchers all over the world are experimenting with new types of batteries made from more abundant materials.

Sodium-ion (Na-ion) batteries which use sodium ions as energy carriers present a promising alternative to LIBs owing to the abundance of sodium, their higher safety, and potentially lower cost. In particular, sodium-containing transition-metal layered oxides (NaMeO2) are powerful materials for the positive electrode of Na-ion batteries, offering exceptional energy density and capacity. However, for multi-element layered oxides composed of several transition metals, the sheer number of possible combinations makes finding the optimal composition both complex and time-consuming. Even minor changes in the selection and proportion of transition metals can bring about marked changes in crystal morphology and affect battery performance.

Now, in a recent study, a research team led by Professor Shinichi Komaba, along with Ms. Saaya Sekine and Dr. Tomooki Hosaka from Tokyo University of Science (TUS), Japan, and from Chalmers University of Technology, and Professor Masanobu Nakayama from Nagoya Institute of Technology, leveraged machine learning to streamline the search for promising compositions. The findings of their study were received on September 05, 2024, with uncorrected proofs and published online in the Journal of Materials Chemistry A on November 06, 2024, after proofreading. This research study is supported by funding agencies JST-CREST, DX-GEM, and JST-GteX.

The team sought to automate the screening of elemental compositions in various NaMeO2 O3-type materials. To this end, they first assembled a database of 100 samples from O3-type sodium half-cells with 68 different compositions, gathered over the course of 11 years by Komaba’s group. “The database included the composition of NaMeO2 samples, with Me being a transition metal like Mn, Ti, Zn, Ni, Zn, Fe, and Sn, among others, as well as the upper and lower voltage limits of charge-discharge tests, initial discharge capacity, average discharge voltage, and capacity retention after 20 cycles,” explains Komaba.

The researchers then used this database to train a model incorporating several machine learning algorithms, as well as Bayesian optimization, to perform an efficient search. The goal of this model was to learn how properties like operating voltage, capacity retention (lifetime), and energy density are related to the composition of NaMeO2 layered oxides, and to predict the optimal ratio of elements needed to achieve a desired balance between these properties.

After analyzing the results, the team found that the model predicted Na[Mn0.36Ni0.44Ti0.15Fe0.05]O2 to be the optimal composition to achieve the highest energy density, which is one of the most important characteristics in electrode materials. To verify the accuracy of the model’s prediction, they synthesized samples with this composition and assembled standard coin cells to run charge-discharge tests.

The measured values were, for the most part, consistent with the predicted ones, highlighting the accuracy of the model and its potential for exploring new battery materials. “The approach established in our study offers an efficient method to identify promising compositions from a wide range of potential candidates,” remarks Komaba, “Moreover, this methodology is extendable to more complex material systems, such as quinary transition metal oxides.”

Using machine learning to identify promising research avenues is a growing trend in materials science, as it can help scientists greatly reduce the number of experiments and time required for screening new materials. The strategy presented in this study could accelerate the development of next-generation batteries, which have the potential to revolutionize energy storage technologies across the board. This includes not only renewable energy generation and electric or hybrid vehicles but also consumer electronics such as laptops and smartphones. Moreover, successful applications of machine learning in battery research can serve as a template for material development in other fields, potentially accelerating innovation across the broader materials science landscape.

“The number of experiments can be reduced by using machine learning, which brings us one step closer to speeding up and lowering the cost of materials development. Furthermore, as the performance of electrode materials for Na-ion batteries continues to improve, it is expected that high-capacity and long-life batteries will become available at lower cost in the future,” concludes Komaba.

Let us hope commercially viable sodium-ion batteries become a reality soon!

***

Reference                     

Title of original paper: Na[Mn0.36Ni0.44Ti0.15Fe0.05]O2 predicted via machine learning for high energy Na-ion batteries

Journal: Journal of Materials Chemistry A

DOI: 10.1039/D4TA04809A

 

About The Tokyo University of Science

Tokyo University of Science (TUS) is a well-known and respected university, and the largest science-specialized private research university in Japan, with four campuses in central Tokyo and its suburbs and in Hokkaido. Established in 1881, the university has continually contributed to Japan's development in science through inculcating the love for science in researchers, technicians, and educators.

With a mission of “Creating science and technology for the harmonious development of nature, human beings, and society," TUS has undertaken a wide range of research from basic to applied science. TUS has embraced a multidisciplinary approach to research and undertaken intensive study in some of today's most vital fields. TUS is a meritocracy where the best in science is recognized and nurtured. It is the only private university in Japan that has produced a Nobel Prize winner, and the only private university in Asia to produce Nobel Prize winners within the natural sciences field.

Website: https://www.tus.ac.jp/en/mediarelations/

 

About Professor Shinichi Komaba from Tokyo University of Science

Professor Shinichi Komaba is a Professor at the Faculty of Science Division I, Department of Applied Chemistry, Tokyo University of Science (TUS). He obtained his Ph.D. from Waseda University, Japan, and then joined Iwate University as a research associate. After studying as a post-doctoral researcher at Bordeaux-CNRS, France, he joined TUS in 2005 to work on developing electrodes, electrolytes, and binding materials for several types of rechargeable batteries. His research group conducts cutting-edge research in the field of rechargeable batteries and their electrochemical applications. With more than 290 publications to his credit, Prof. Komaba has won numerous international awards, including the title of "Highly Cited Researcher" from 2019 to 2023.

 

Funding information

This study was partially funded by the Ministry of Education, Culture, Sports, Science and Technology (MEXT) Program: Data Creation and Utilization Type Materials Research and Development Project (JPMXP1122712807), the JST through CREST (Grant No. JPMJCR21O6), ASPIRE (JPMJAP2313), and GteX (JPMJGX23S4). The synchrotron X-ray diffraction experiments were performed at BL02B2 of SPring-8 with the approval of JASRI (Proposal No. 2023B1573).

 

The proposed machine learning-based approach to explore and optimize the ratio of transition metals offers an efficient method to identify promising compositions among a wide range of potential candidates, potentially speeding up the development of sodium-ion batteries.

Credit

Shinichi Komaba from TUS, Japan

Optimizing Sodium-Ion Battery [VIDEO] 

Scientists worldwide are exploring sodium-ion batteries (Na-ion batteries) as potential alternatives to the more expensive lithium-ion batteries, especially for sustainable applications such as electric vehicles and renewable energy generation. In a recent study published in the Journal of Materials Chemistry A, researchers from Japan utilized machine learning to predict the optimal composition of transition-metal layered oxides for the positive electrode of Na-ion batteries. Their approach demonstrated how artificial intelligence can significantly reduce the time, resources, and effort required for discovering new materials.

Credit

Professor Shinichi Komaba from Tokyo University of Science (TUS), Japan

Journal

Journal of Materials Chemistry A

DOI

10.1039/D4TA04809A 

Method of Research

Experimental study

Subject of Research

Not applicable

Article Title

Na[Mn0.36Ni0.44Ti0.15Fe0.05]O2 predicted via machine learning for high energy Na-ion batteries

Study of Arab tribes should not be abandoned, study says

University of Exeter

The study of Arab tribes should not be abandoned because Middle East and North African citizens continue to insist on the relevance of the term in their daily lives, a new study says.

The use of “tribe” has been discredited and is now rarely used by researchers, who are concerned it is too vague, evocative of primitive and backwards connotations, and has been inappropriately applied.

The study says there should be more specific use of the term, treating it as a dynamic not static description and researchers should ensure it is used based on on-the-ground reality and not theoretical biases.

Dr Eleanor Gao, from the University of Exeter, who conducted the research, said: “The term tribe has at times been indiscriminately applied even when societies were not organized as such to serve Western intellectual and organizational interests. Nonetheless it is a term that still holds resonance for many citizens of the Arab world and one that they independently use to describe their own societies.

“By reducing the tribe to its most essential characteristics, distinguishing the term from various, often negative attributes it has been assigned, and not presupposing the absence or salience of tribes, we can resurrect a badly maligned but still very useful concept.”

The Arab world includes a wide array of diverse ethnicities. Some groups such as the Kurds, Berbers, Persians, Turkmen, and Circassians organize themselves tribally. For some this is about kinship and for others it is based around a common territory, language and culture or defence capacity. The study says this explains why the term has lost clarity and has become opaque.

For some groups the concept of a tribe was invented by Western colonial powers to impose boundaries on official documents and maps.

Dr Gao said: “It is no wonder some social scientists have demanded that we move away from the study of tribes altogether.

“In future, rather than a thick, complex conception of a tribe that is bound to a number of characteristics applicable only in specific contexts, we should adopt a thin, parsimonious conception of a tribe focused on the essential characteristics that differentiate a tribe from other social units.

“We must recognize tribes are not static but dynamic and modern entities. They shift and change according to social circumstances and regime agendas.”

The study outlines how some tribes have become more politically powerful because of the actions of the Jordanian and Kuwaiti monarchies, which have purposely selected electoral rules that encourage voting for one’s own tribe over political parties or blocs.

 

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Journal

Frontiers in Political Science

DOI

10.3389/fpos.2024.1454752 

Method of Research

Meta-analysis

Subject of Research

People

Article Title

Bringing back the tribe: why we should not abandon the study of tribes in the Arab world