Newswise — Green hydrogen production technology, which utilizes renewable energy to produce eco-friendly hydrogen without carbon emissions, is gaining attention as a core technology for addressing global warming. Green hydrogen is produced through electrolysis, a process that separates hydrogen and oxygen by applying electrical energy to water, requiring low-cost, high-efficiency, high-performance catalysts.

The Korea Institute of Science and Technology (KIST, President Oh Sang-rok) announced that a research team led by Dr. Na Jongbeom and Dr. Kim Jong Min from the Center for Extreme Materials Research has developed next-generation water electrolysis catalyst technology. This technology integrates a single-atom ‘All-in-one’ catalyst precisely controlled down to the atomic level with binder-free electrode technology. A key feature of this technology is its ability to stably perform both hydrogen evolution and oxygen evolution reactions simultaneously on a single electrode.

Existing electrolysis systems had limitations requiring different catalysts and electrode structures for the hydrogen evolution reaction (HER) and oxygen evolution reaction (OER), necessitating the use of large quantities of expensive precious metals. Additionally, the binder used to fix the catalyst to the electrode posed problems, including reduced electrical conductivity and catalyst detachment during long-term operation.

KIST researchers utilized atomic-level precision control technology to uniformly disperse iridium (Ir) atoms across the surface of a manganese (Mn)-nickel (Ni)-based layered double hydroxide (LDH) support incorporating phytic acid. This strategy replaced the conventional use of bulk iridium precious metal. By maximizing the number of active sites for water-splitting reactions with minimal iridium, this approach is analogous to evenly spreading fine grains of sand over a large surface rather than relying on a single large rock.

In particular, the iridium single atom acts as a direct active site for the hydrogen evolution reaction through its strong interaction with the support, while simultaneously enhancing the catalytic performance of the nickel-based active site where the oxygen evolution reaction occurs. Thus, a single-atom catalyst has realized bifunctional catalytic characteristics, exhibiting suitable reactivity for both reactions. Furthermore, the research team applied a method of directly growing the catalyst on the electrode surface, achieving an electrode structure that does not require a separate binder. This significantly improved electrical conductivity and ensured excellent durability even during long-term operation.

This technology significantly reduces precious metal usage to within 1.5% compared to existing precious metal catalysts while achieving outstanding performance in both hydrogen and oxygen evolution reactions. In addition, it demonstrates high stability with minimal performance degradation even after continuous operation for over 300 hours in an anion exchange membrane (AEM) water electrolysis system. This research outcome demonstrates the technical feasibility of simultaneously enhancing the economic viability and durability of electrolysis systems by minimizing precious metal usage and simplifying electrode structures. It is expected to significantly contribute to the commercialization of green hydrogen production and the reduction of hydrogen production costs in the future.

Dr. Na Jongbeom of KIST stated, “This work is highly significant as it resolves the two essential reactions for hydrogen production using a single catalyst while reducing precious metal consumption.” He added, “This technology will accelerate the commercialization of water electrolysis devices and provide substantial support for expanding hydrogen energy.”

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KIST was established in 1966 as the first government-funded research institute in Korea. KIST now strives to solve national and social challenges and secure growth engines through leading and innovative research. For more information, please visit KIST’s website at https://kist.re.kr//eng/index.do

This research was conducted with support from the Ministry of Science and ICT (Minister Bae Kyung-hoon) through KIST’s Institutional Program and Excellent New Researcher Program (RS-2024-00350423), the DACU Core Technology Development Project (RS-2023-00259920), and the Korea-US-Japan International Joint Research Project (Global-24-003). The research results were published in the latest issue of the international journal Advanced Energy Materials (IF: 26.0, JCR (%): 2.5%).

Newswise — Kieyarrah Dennis can wear a lot of hats. In fact, versatility has shaped her personal and academic pursuits.

Her adaptability blossomed during her elementary years at a community-focused bilingual school in Milwaukee. Later, it drove her to earn a bachelor’s degree in biochemistry and history as an undergraduate student at the College of Saint Benedict in Minnesota.

“I knew that biochemistry was a broad enough scientific track that I could use it as a foundation to do anything,” she said. “And I want to do it all.”

In 2021, Dennis joined the University of Wisconsin-Milwaukee’s School of Freshwater Sciences as a PhD student — propelled by a love of water and bugs.

She now specializes in expanding our understanding of antibiotic-resistant organisms so that the field of medicine can better equip people to survive bacterial infections. Her research advocates for more diverse treatments against the pathogens we are exposed to in our water systems and other public spaces.

“I’ve taken antibiotics,” Dennis said, “but I didn’t think about the fact that treatments could or could not work based on what organism you’re sick with and whatever resistance mechanisms they pick up.”

Following ‘creepy crawlers’

Dennis’ biochemistry studies for her bachelor’s degree planted the seeds for her work as a grad student today. “I was just thinking about parasites,” she said. “I’ve always been interested in creepy crawlers.”

Charged with writing a mock proposal for research, her capstone explored the development of a vaccine against a disease spread by freshwater parasites. The process introduced Dennis to disease transmission routes, dynamic food chains and freshwater environments, including public parks and green spaces.

Dennis was fascinated and hooked, and she started as a freshwater sciences grad student at UWM less than a month after graduation. “I drove home, rested for maybe eight days, then started here,” she said.

Probing antibiotic resistance

Over the past four years, Dennis has plunged into the complexities of how certain pathogens — such as E. coli, which is prevalent in bodies of freshwater and beyond — evolve and adapt to resist antibiotic treatment.

The issues of antibiotic resistance and multidrug-resistant organisms have grown significantly since the 1980s, which has prompted concern and significant funding to prevent a future where antibiotics no longer work.

For Dennis, some days her research looks like microscopic sequencing of gene families in the lab. Other days, it requires donning her history hat, while contemplating anthropology, sociology and other disciplines.

“You can’t solve this issue when you only look at a slice of where it occurs,” she said. “It’s out in the community. It’s in the hospitals. It’s in our food chain. It’s in the water.”

Bridging science and neighborhoods

With her lab hat on, Dennis immerses herself in the detailed genetics and mutation patterns of these microorganisms, as well as the freshwater environments that drive the evolution of the pathogens. Her findings will help develop new solutions to protect us from them.

Recently, though, she also discovered a love for public health. She hopes to educate communities about these issues in our world, bringing the science to everyday people.

“There’s usually a disconnect between the people doing the actual research and the people doing advocacy or the application of research,” she said. “I would like to do both.”

Newswise — Around 56 million years ago, Europe and North America began pulling apart to form what became the ever-expanding North Atlantic Ocean. Vast amounts of molten rock from Earth’s mantle reached the ocean floor as the crust stretched and thinned, creating a volcanic rifted margin between Norway and Greenland, a marine feature that has intrigued scientists for decades.

They have long argued over why so much magma surfaced here in what was among the biggest volcanic events in Earth’s history, one that is implicated in a period of intense global warming during the Eocene Epoch. Was a deep, superhot mantle plume responsible, or did crustal thinning play the bigger role?

Newswise — Illinois Grainger engineers have identified a fundamental deformation mechanism that can be leveraged to greatly enhance the fatigue properties of metals, opening the door to a new strategy for designing fatigue-resistant alloys.

Metal alloys crack and fail through a mechanism called “fatigue” when repeatedly loaded and strained. While it is well known how to design alloys to withstand static loads and pressures, it is very difficult to design resistance to fatigue because it is difficult to predict how the underlying cause manifests at the atomic scale.

Researchers in The Grainger College of Engineering at the University of Illinois Urbana-Champaign have demonstrated that fatigue resistance can be greatly enhanced by controlling how metal plasticity, or irreversible deformation, localizes at small scales. It represents a new design strategy for engineering metallic alloys that are resistant to fatigue by leveraging unique deformation processes at the atomic scale.

“Transportation, space and energy all create environments where there is risk for fatigue, presenting a challenge to both safety and sustainability,” said materials science and engineering professor and project lead Jean-Charles Stinville. “Structural applications that involve high temperatures or radiation need materials resistant to fatigue, and our work shows how to design metal alloys that achieve this.”

These results were recently published in the journal Nature Communications.

Fatigue is governed by how a material accommodates plastic deformation, the irreversible rearrangement of its internal structure under repeated loading. As a material is cyclically loaded and unloaded, localized plastic deformation accumulates eventually leading to crack initiation. Paradoxically, materials engineered to withstand very high static loads often suffer from reduced fatigue resistance because their microstructure promotes strong localization of plastic deformation, accelerating damage accumulation.

“In alloys, plastic deformation tends to localize into discrete regions, which ultimately become preferential sites for fatigue crack initiation,” Stinville explained. “Because this localization emerges from complex microstructural and deformation processes interactions, it is difficult to predict where and how it will occur, making it challenging to account for during the engineering design stage.” 

Stinville and his collaborators examined whether fatigue resistance can be drastically improved by designing alloys in which plastic deformation is engineered to remain small and uniformly distributed rather than intense and highly localized.

“It makes sense intuitively, that spreading out the plastic deformation homogeneously makes reduces the impact of localized deformation, but experimentally demonstrating it was another matter,” Stinville said. “It required new technology capable of scanning large regions at very high resolution combined with theoretical support from density functional theory and ab-initio molecular dynamics simulations.”

The researchers used high-throughput automated high-resolution digital image correlation, a technique developed in Stinville’s laboratory, to map plastic deformation with unprecedented spatial resolution across large material regions. Unlike conventional methods, which must trade field of view for resolution, this approach captures fine-scale deformation over wide areas. These measurements revealed a delocalized mode of plastic deformation involving deformation processes called “dynamic plastic delocalization.” Mechanical testing showed to be directly associated with greatly enhanced fatigue resistance.

To make sense of the observed structural features, Stinville’s group collaborated with mechanical science and engineering researchers within the group of mechanical science and engineering professor Huseyin Sehitoglu, an expert in the theory and modeling of metal deformation. Computational modeling clarified the roles of chemistry and ordering on the observed delocalized plasticity in the tested materials.

Now that it has been confirmed that metal chemistry and structure can be used to generate homogeneous plasticity during deformation and therefore greatly improved fatigue resistance, the next step is exploring the potential of this result in material design strategies.

“Now that the fundamental mechanism has been identified, we can design new alloys chemistry that activates it to produce fatigue resistant alloys,” Stinville said. 

This study’s other contributors are Dhruv Anjaria, Mathieu Calvat, Shuchi Sanandiya, and Daegun You of Illinois Grainger Engineering; Milan Heczko of the Czech Academy of Arts and Sciences; and Maik Rajkowski, Aditya Srinivasan Tirunilai and Guillaume Laplanche of Ruhr Universität Bochum.

Newswise — Woods Hole, Mass. (February 4, 2026) – Alan Seltzer, an affiliated scientist at Woods Hole Oceanographic Institution (WHOI), assistant professor at University College Dublin, and former WHOI postdoctoral scholar, has been named the 2026 recipient of the F.G. Houtermans Award by the European Association of Geochemistry (EAG). The award is among the highest international honors recognizing early-career scientists in geochemistry.

Seltzer is being recognized for pioneering the use of dissolved gas isotopes to quantify physical and biogeochemical processes across the Earth system, including exploring the sensitivity of groundwater systems to climate and the dynamics of atmosphere-ocean gas exchange. Much of the work cited by the award committee was conducted at WHOI, where Seltzer was a postdoctoral scholar from 2019 to 2021 and later a member of the scientific staff in the Marine Chemistry and Geochemistry Department, where he established a gas isotope tracer laboratory and developed several new analytical techniques.

His research helped open new pathways for using noble gas and nitrogen isotopes to investigate groundwater, seawater, air, and volcanic gases. Seltzer also helped extend high-precision noble gas isotope techniques to volcanic systems in collaboration with WHOI associate scientist Peter Barry to better understand the origins and transport pathways of volatiles from Earth’s deep interior. He also expanded oceanic applications of noble gas tracers for air-sea interaction and glacial meltwater circulation with WHOI scientists Bill Jenkins and Roo Nicholson, and more recently advanced high-precision tools for quantifying nitrogen cycling in aquatic environments in collaboration with MIT-WHOI Joint Program student Katelyn McPaul and WHOI associate scientist Scott Wankel.

“It is an honor to be recognized with the F.G. Houtermans Award,” Seltzer said. “WHOI has a special culture in which collaboration and high-risk science are celebrated, and without the encouragement and freedom at WHOI to take risks, push analytical limits, and fail a lot along the way, much of my work would not have been possible. I’m deeply grateful for all the support I’ve received from the WHOI community over my career so far.”

Seltzer’s selection continues a notable streak for WHOI’s Marine Chemistry and Geochemistry Department. Former WHOI postdoctoral scholar David Bekaert received the Houtermans Award in 2025, marking back-to-back years in which the honor has gone to WHOI-trained scientists—a rare distinction that highlights the strength and impact of the Institution’s postdoctoral program.

The 2026 F.G. Houtermans Award will be formally presented at the Goldschmidt Conference in July.

About Woods Hole Oceanographic Institution

The Woods Hole Oceanographic Institution is a private, non-profit organization on Cape Cod, Massachusetts, dedicated to marine research, engineering, and higher education. Established in 1930, its primary mission is to understand the ocean and its interaction with the Earth as a whole, and to communicate an understanding of the ocean’s role in the changing global environment. Top scientists, engineers, and students collaborate on more than 800 concurrent projects worldwide—both above and below the waves—pushing the boundaries of knowledge and possibility. 

Newswise — New work from Georgia Tech is showing how a simple glass of wine can serve as a powerful gateway for understanding advanced research and technologies.

The project, inspired by an Atlanta Science Festival event hosted by School of Chemistry and Biochemistry Assistant Professor Andrew McShan, develops an innovative outreach and teaching module around nuclear magnetic resonance (NMR) techniques, and is designed for easy adoption in introductory chemistry and biochemistry courses. 

Published earlier this year in the Journal of Chemical Education, the study, “Automated Chemical Profiling of Wine by Solution NMR Spectroscopy: A Demonstration for Outreach and Education” was led by a team from the School of Chemistry and Biochemistry including lead author McShan, Ph.D. students Lily Capeci, Elizabeth A. Corbin, Ruoqing Jia, Miriam K. Simma, and F. N. U. Vidya, Academic Professional Mary E. Peek, and Georgia Tech NMR Center Co-Directors Johannes E. Leisen and Hongwei Wu.

“NMR is one of the most widely used analytical tools in chemistry and the life sciences, and Georgia Tech hosts one of the most cutting-edge NMR centers in the world,” McShan says. “Our study shows that you don’t need advanced training to appreciate how powerful tools like NMR work and how those tools are used in research.”

All materials, tutorials, and data are freely available via online tutorials and a YouTube video, enabling educators to replicate or adapt the activity even in settings with limited access to NMR facilities.

Wine sleuthing at the Atlanta Science Festival

From families with K-12 students to undergraduates to adults with no prior chemistry experience, nearly 130 visitors explored wine chemistry at the Georgia Tech NMR Center during the Atlanta Science Festival event. With McShan’s guidance, they identified and quantified more than 70 chemical components that influence wine taste, aroma, and quality by analyzing the chemical composition, structure, and dynamics of molecules.

Taking on the role of wine investigators (a real-world application of NMR), the group investigated examples of wine fraud, learning to identify harmful additives like methanol, antifreeze, and lead acetate – additives that played roles in both historical and modern wine scandals.

“By connecting the science to something familiar like wine, we were able to spark curiosity and excitement across age groups,” says McShan. “This a framework for how complex analytical techniques can be made inclusive, interactive, and inspiring whether in the classroom or at a science festival.”

Science for all

The study underscores the potential of NMR and other powerful technologies as outreach opportunities – from engaging the public to better teaching undergraduate students.

“After the event, adults said they learned how chemical composition affects wine characteristics and how NMR is used in research and industry,” McShan says. “Younger participants learned key concepts about wine composition and found benefits from the sensory elements, like watching the spectrometer in action.”

They aim to use these takeaways to continue developing outreach tools. “My end goal is to develop NMR into a practical teaching tool by grounding the technique in real-world examples,” adds McShan. “Using this approach is a clear avenue to introducing the general public to the world-class instruments used by researchers at Georgia Tech and exposing undergraduate students to the powerful analytical techniques they are likely to encounter throughout their careers.”

Funding: National Science Foundation

Newswise — New forms of fentanyl are created every day. For law enforcement, that poses a challenge: how do you identify a chemical you’ve never seen before?

Researchers at Lawrence Livermore National Laboratory (LLNL) aim to answer that question with a machine learning model that can distinguish opioids from other chemicals with an accuracy over 95% in a laboratory setting. The foundation for this new technique was published in Analytical Methods.

To identify synthetic opioids like fentanyl now, chemists try to match their signature to a library of a few hundred known samples. But studies suggest there could be thousands of unknown forms, some more dangerous than others. Recognizing those new versions requires a reference-free identification system: a way to catch an opioid even if it does not exist in a chemical database yet.

“When law enforcement finds a new clandestine drug operation, those labs often produce never-before-seen fentanyl derivatives. We can’t just go check a database, and we can’t just go back to who made it and ask how they did it,” said LLNL computational mathematician and author Colin Ponce. “And law enforcement needs to identify the samples they find quickly because there’s going to be another sample tomorrow. I think that’s a little bit of a unique situation.”

Machine learning might seem like a natural fit to identify novel or unknown opioids. And it is — to an extent. The method works best with large data sets, which are difficult to generate for toxic substances like synthetic opioids. 

To even get a machine learning algorithm off the ground, the team had to create the chemical data. They did so with LLNL’s mass spectrometry capabilities coupled to an autosampler, which enabled them to measure hundreds of samples under the same experimental conditions. This minimized variables for the machine learning algorithms. 

“In the world of AI, data is gold, and if you don’t have good data, then you’re not going to generate accurate machine learning models,” said LLNL chemist and author Carolyn Fisher. “Good data is something that we can control and generate at LLNL.” 

With that data in hand, they tried different machine learning techniques as they homed in on the best method: a random forest model. 

“When a model like this eventually gets into the hands of a user, the output has to be interpretable and trustworthy,” said LLNL scientist and author Kourosh Arasteh. “We explored machine learning methods ranging from simple regression and random forests to more complex neural network approaches to balance interpretability with performance.” 

The random forest approach runs through a collection of decision trees. Each tree asks a series of questions about the data and, based on each answer, lands on a prediction: opioid or not. Together, they vote on the final classification.

“Our 650 samples are not the same as having 300,000 samples. On the machine learning side, we needed to make sure that we were designing techniques that that were appropriate for that kind of scale,” said Ponce.

This study trained and tested the algorithm with analytically pure samples. These ideal chemicals contain no contaminants or impurities.

“The challenge is that nothing is analytically pure in the real world,” said Fisher. “The next step is to add in background noise and have the AI understand what it should care about during a classification task.”

Fisher and Ponce emphasized that this work would have been impossible without collaboration across the disciplines of data science and chemistry. The two are friends outside of work, and this study, a Laboratory Directed Research and Development project, emerged from a series of organic conversations between them.

“To me, this project really captures what LLNL does best,” said fellow author and LLNL software engineer Steven Magana-Zook. “When you get chemists and data scientists working side by side, you end up with results that neither group could get on their own. That kind of cross-disciplinary work is exactly what makes this place so strong.”

That approach, while essential to the work, initially proved to be an obstacle. The team faced rejection of this manuscript from two journals — reviewers in chemistry didn’t fully grasp the machine learning aspects and experts on the computational side felt uncertain about the chemistry.

“I don’t think people talk about failure enough. It’s so common in science. We fail so much more than we succeed,” said Fisher. “But we keep iterating and improving. I’m proud of our resilience.” 

The team’s persistence paid off. Looking ahead, they aim to further develop their algorithm using real-world samples with higher background signals. 

Other LLNL coauthors include Roald Leif, Alex Vu, Mark Dreyer, Brian Mayer and Audrey Williams.

Newswise — After high-profile water crises like the one in Flint, Michigan, some Americans distrust the safety of tap water, choosing to purchase drinking water from freestanding water vending machines or kiosks. Yet this more expensive water may contain different pollutants than local tap water, according to a study in ACS’ Environmental Science & Technology. Researchers report that water sampled from 20 kiosks in six states sometimes contained lead at levels above public health recommendations.

“Currently, water kiosks are not regulated the same as tap water; their water is not tested for lead or other metals,” says Samantha Zuhlke, a corresponding author of this study. “Updating water kiosk regulations can improve their quality and help consumers make informed decisions about the water they are drinking.”

Water kiosks are privately owned vending machines that are often marketed as being safer than tap water, commanding prices of $0.25-$0.35 per gallon (compared to less than 2 cents per gallon for tap water in most U.S. cities). Kiosk operators generally treat local tap water with purification techniques such as filtration, ultraviolet light or reverse osmosis (RO) to remove potentially harmful contaminants such as lead, microbes, residual disinfectants, and per- and polyfluoroalkyl substances (PFAS). But water vending machines in the U.S. are poorly regulated. So, a team of researchers led by Zuhlke and David Cwiertny conducted a comprehensive comparison of the chemical and microbial characteristics of kiosk water and tap water from municipalities close to the monitored kiosks.

The team collected water samples from 20 kiosks operated by four different manufacturers across Iowa and in the surrounding states of Illinois, Kansas, Missouri, Arkansas and Oklahoma. Most of the kiosks advertised treatment of their water by RO, a process that uses pressure to force water through a semipermeable membrane, purifying the water and leaving most contaminants caught behind the membrane. For comparison, the researchers collected tap water samples from community sources within a mile of each kiosk.

They analyzed all samples and found no evidence of microbial contamination in any sample. They also found that RO treatment in kiosks effectively removed most PFAS from the sourced tap water. However, this benefit was offset by concerning levels of lead in some RO-purified kiosk water samples — nearly twice the concentration recommended by the U.S. Environmental Protection Agency.

The researchers traced the lead to the corrosion of brass plumbing in the kiosks following RO treatment. Although the plumbing components are marketed as “lead-free,” small amounts of the metal can leach under the low-pH and low-alkalinity conditions of RO-treated water, they say. Replacing the internal metal pieces with other materials could eliminate lead in dispensed water.

“This work adds to growing evidence that allowable levels of lead in ‘lead-free’ plumbing can still be problematic sources of lead in drinking water when such plumbing is exposed to certain types of water, like that generated after RO treatment,” Cwiertny says.

The authors acknowledge funding from the University of Iowa’s Center for Social Science Innovation and the Office of Undergraduate Research. This work was conducted through the University of Iowa Center for Health Effects of Environmental Contamination, which receives support through the Iowa Department of Natural Resources.

The paper’s abstract will be available on Feb. 11 at 8 a.m. Eastern time here:   

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The American Chemical Society (ACS) is a nonprofit organization founded in 1876 and chartered by the U.S. Congress. ACS is committed to improving all lives through the transforming power of chemistry. Its mission is to advance scientific knowledge, empower a global community and champion scientific integrity, and its vision is a world built on science. The Society is a global leader in promoting excellence in science education and providing access to chemistry-related information and research through its multiple research solutions, peer-reviewed journals, scientific conferences, e-books and weekly news periodical Chemical & Engineering News. ACS journals are among the most cited, most trusted and most read within the scientific literature; however, ACS itself does not conduct chemical research. As a leader in scientific information solutions, its CAS division partners with global innovators to accelerate breakthroughs by curating, connecting and analyzing the world’s scientific knowledge. ACS’ main offices are in Washington, D.C., and Columbus, Ohio.

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Newswise — Lithium-ion batteries serve as the core energy storage devices in various industries and everyday products, including smartphones, electric vehicles, and ESS (energy storage systems). However, conventional lithium-ion batteries use liquid electrolytes, posing a risk of fire or explosion when subjected to external impact or overheating. Recent electric vehicle fire incidents have heightened concerns about their safety. As an alternative to overcome these limitations, ‘all-solid-state batteries’-which use non-flammable solid materials as electrolytes-are gaining attention as next-generation battery technology.

However, amorphous solid electrolytes-the core material for all-solid-state batteries-have faced limitations in analyzing lithium-ion transport mechanisms due to the irregularity of their internal structure. Consequently, performance improvements have been achieved empirically by altering electrolyte composition or compression conditions, making it difficult to systematically explain the causes of performance differences.

A research team led by Dr. Byungju, Lee at the Computational Science Research Center of the Korea Institute of Science and Technology (KIST, President Sang-Rok Oh) has identified key factors governing lithium ion movement in amorphous solid electrolytes through AI-based atomic simulations. The team analyzed lithium-ion movement by distinguishing it into ‘ease of movement between sites’ and ‘connectivity of movement paths’. They confirmed that overall performance is more significantly influenced by the difficulty of ions moving from one site to the next than by path connectivity.

In fact, while ion conductivity performance varied by up to fivefold depending on lithium ion mobility, the effect of pathway connectivity was limited to approximately a twofold difference. This provides a quantitative basis for interpreting performance variations that were previously difficult to explain due to the amorphous structure. Furthermore, the research team identified specific structural conditions that enhance lithium ion mobility. The higher the proportion of structures where four sulfur atoms surrounded a lithium ion, the faster the ion migration became. Optimal performance was achieved when the size of the internal void space fell within an appropriate range. Notably, excessively large voids actually hindered ion migration and degraded performance. This finding overturns the conventional wisdom that ‘lower density leads to higher conductivity’.

The results of this study can be directly applied to the design and manufacturing process of solid electrolytes for all-solid-state batteries. Simply controlling the internal structure by adjusting the electrolyte composition ratio or compression/molding conditions can improve ionic conductivity performance without requiring additional material changes, making it highly applicable in industrial settings. Furthermore, the analytical method proposed in this study can be extended to the development of various solid electrolyte materials. By pre-selecting high-performance candidate materials, it can dramatically enhance performance prediction and accelerate material development speed. This is expected to advance the commercialization of all-solid-state batteries in fields where safety and energy density are critical, such as electric vehicles and energy storage devices.

Dr. Byungju, Lee of KIST stated, “This research is significant in that it clearly identifies the key factors determining the performance of amorphous solid electrolytes.” He added, “As it presents design criteria enabling systematic improvement of material performance, we expect it to contribute to accelerating the commercialization of all-solid-state batteries.”

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KIST was established in 1966 as the first government-funded research institute in Korea. KIST now strives to solve national and social challenges and secure growth engines through leading and innovative research. For more information, please visit KIST’s website at https://kist.re.kr//eng/index.do

This research was conducted as part of KIST’s major projects and the Materials Global Young Connect Project (RS-2024-00407995), supported by the Ministry of Science and ICT (Minister Bae Kyung-hoon). The research findings were published in the latest issue of the international journal Advanced Energy Materials (IF 26.0, JCR field 2.5%).

Newswise — Agricultural pest management has traditionally relied on chemical insecticides, but their overuse has led to environmental contamination, health risks, and rapidly evolving pesticide resistance. Meanwhile, natural variation in pest resistance exists within crops and their wild relatives, offering valuable resources for breeding. However, resistance traits are difficult to measure accurately, as they are often scored visually using coarse categories that fail to capture continuous variation. This limits the effectiveness of genome-wide association studies and genomic selection. Advances in deep learning provide new opportunities to extract detailed phenotypic information directly from images, overcoming subjectivity and labor constraints. Based on these challenges, there is a pressing need to conduct in-depth research on AI-enabled phenotyping and genomic breeding for pest resistance.

Researchers from the Chinese Academy of Agricultural Sciences and collaborating institutions report (DOI: 10.1093/hr/uhaf128) on 7 May 2025 in Horticulture Research that deep learning can substantially improve genomic selection for pest-resistant grapevine. The team developed convolutional neural networks to automatically assess insect damage on grape leaves and combined these data with genome resequencing, genome-wide association studies, and transcriptomic analyses. By linking AI-derived phenotypes with genetic markers, the study identifies key resistance genes and demonstrates highly accurate machine-learning-based prediction of pest resistance, offering a new framework for precision breeding.

The study analyzed 231 grapevine accessions subjected to natural infestations of the tobacco cutworm, a major leaf-feeding pest. Deep convolutional neural networks were trained to classify pest damage as mild or severe, achieving over 95% accuracy, while a custom regression model generated continuous damage scores strongly correlated with human assessments. These AI-derived phenotypes enabled more precise genetic analyses than traditional categorical scoring. Genome-wide association studies identified 69 quantitative trait loci and 139 candidate genes linked to pest resistance, many involved in jasmonic acid, salicylic acid, ethylene, and calcium-mediated signaling pathways. By integrating transcriptomic data, the researchers pinpointed key defense genes, including calcium-transporting ATPase ACA12 and the protein kinase CRK3, both strongly induced during herbivore attack. Machine-learning-based genomic selection models further demonstrated high predictive power, reaching 95.7% accuracy for binary traits and strong correlations for continuous traits. Together, these results show that combining deep learning phenotyping with genomics reveals subtle resistance mechanisms and enables reliable prediction of complex, polygenic pest-resistance traits.

“This work highlights how artificial intelligence can fundamentally change plant breeding,” said the study’s senior authors. “By replacing subjective visual scoring with fast, objective deep-learning-based phenotyping, we can capture continuous variation in pest damage that was previously overlooked. When these high-quality phenotypes are integrated with genomics and transcriptomics, they reveal the true polygenic architecture of pest resistance. This approach not only improves prediction accuracy, but also allows breeders to make informed selections much earlier in the breeding cycle.”

The findings have broad implications for sustainable agriculture and crop improvement. AI-driven phenomics enables rapid, large-scale assessment of pest resistance without increasing labor costs, making it suitable for breeding programs worldwide. By identifying resistance genes and accurately predicting pest tolerance, breeders can reduce reliance on chemical pesticides while improving crop resilience. The framework established in grapevine can be readily adapted to other crops and stress traits, supporting the development of automated, data-driven breeding platforms. Ultimately, integrating deep learning, genomics, and machine learning could accelerate the creation of pest-resistant varieties essential for food security under increasing environmental pressure.

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References

DOI

10.1093/hr/uhaf128

Original Source URL

https://doi.org/10.1093/hr/uhaf128

Funding information

This work was supported by the National Key Research and Development Program of China (No. 2023YFD2200702), the project of National Key Laboratory for Tropical Crop Breeding (No. NKLTCB202325), the National Natural Science Foundation of China (No. 32372662), and the Science Fund Program for Distinguished Young Scholars of the National Natural Science Foundation of China (Overseas) to Yongfeng Zhou.

About Horticulture Research

Horticulture Research is an open access journal of Nanjing Agricultural University and ranked number one in the Horticulture category of the Journal Citation Reports ™ from Clarivate, 2023. The journal is committed to publishing original research articles, reviews, perspectives, comments, correspondence articles and letters to the editor related to all major horticultural plants and disciplines, including biotechnology, breeding, cellular and molecular biology, evolution, genetics, inter-species interactions, physiology, and the origination and domestication of crops.