Newswise — The aviation industry accounts for a significant share of global carbon emissions. In response, the international community is expanding mandatory use of Sustainable Aviation Fuel (SAF), which is produced from organic waste or biomass and is expected to significantly reduce greenhouse gas emissions compared to conventional fossil-based jet fuel. However, high production costs remain a major challenge, leading some airlines in Europe and Japan to pass SAF-related costs on to consumers.

Against this backdrop, a research team led by Dr. Yun-Jo Lee at the Korea Research Institute of Chemical Technology (KRICT), in collaboration with EN2CORE Technology Co., Ltd., has successfully demonstrated an integrated process that converts landfill gas generated from organic waste—such as food waste—into aviation fuel.

Currently, the refining industry mainly produces SAF from used cooking oil. However, used cooking oil is limited in supply and is also used for other applications such as biodiesel, making it relatively expensive and difficult to secure in large quantities. In contrast, landfill gas generated from food waste and livestock manure is abundant and inexpensive. This study represents the first domestic demonstration of aviation fuel production using landfill gas as the primary feedstock.

Producing aviation fuel from landfill gas requires overcoming two major challenges: purifying the gas to obtain suitable intermediates and improving the efficiency of converting gaseous intermediates into liquid fuels. The research team addressed these challenges by developing an integrated process encompassing landfill gas pretreatment, syngas production, and catalytic conversion of syngas into liquid fuels.

EN2CORE Technology was responsible for the upstream processes. Landfill gas collected from waste disposal sites is desulfurized and treated using membrane-based separation to reduce excess carbon dioxide. The purified gas is then converted into synthesis gas—containing carbon monoxide and hydrogen—using a proprietary plasma reforming reactor, and subsequently supplied to KRICT.

KRICT applied the Fischer–Tropsch process to convert the gaseous syngas into liquid fuels. In this process, hydrogen and carbon react on a catalyst surface to form hydrocarbon chains. Hydrocarbons of appropriate chain length become liquid fuels, while longer chains form solid byproducts such as wax. By employing zeolite- and cobalt-based catalysts, KRICT significantly improved selectivity toward liquid fuels rather than solid byproducts.

A key innovation of this work is the application of a microchannel reactor. Excessive heat generation during aviation fuel synthesis can damage catalysts and reduce process stability. The microchannel reactor developed by the team features alternating layers of catalyst and coolant channels, enabling rapid heat removal and suppression of thermal runaway. Through integrated and modular design, the reactor volume was reduced by up to one-tenth compared to conventional systems. Production capacity can be expanded simply by adding modules.

For demonstration purposes, the team constructed an integrated pilot facility on a landfill site in Dalseong-gun, Daegu. The facility, approximately 100 square meters in size and comparable to a two-story detached house, successfully produced 100 kg of sustainable aviation fuel per day, achieving a liquid fuel selectivity exceeding 75 percent. The team is currently optimizing long-term operation conditions and further enhancing catalyst and reactor performance.

This achievement demonstrates the potential to convert everyday waste-derived gases from food waste and sewage sludge into high-value aviation fuel. Moreover, it shows that aviation fuel production—previously limited to large-scale centralized plants—can be realized at local landfills or small waste treatment facilities. The technology is therefore expected to contribute to the establishment of decentralized SAF production systems and strengthen the competitiveness of Korea’s SAF industry.

The research team noted that the work is significant in securing an integrated process technology that converts organic waste into high-value fuels. KRICT President Young-Kuk Lee stated that the technology has strong potential to become a representative solution capable of achieving both carbon neutrality and a circular economy.

The development of two catalysts enabling selective production of liquid fuels was published as an inside cover article in ACS Catalysis (November 2025) and in Fuel (January 2026).

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KRICT is a non-profit research institute funded by the Korean government. Since its foundation in 1976, KRICT has played a leading role in advancing national chemical technologies in the fields of chemistry, material science, environmental science, and chemical engineering. Now, KRICT is moving forward to become a globally leading research institute tackling the most challenging issues in the field of Chemistry and Engineering and will continue to fulfill its role in developing chemical technologies that benefit the entire world and contribute to maintaining a healthy planet. More detailed information on KRICT can be found at https://www.krict.re.kr/eng/

This research was supported by “Development of integrated demonstration process for the production of bio naphtha/lubricant oil from organic waste-derived biogas” (Project No. RS-2022-NR068680) through the National Research Foundation (NRF) funded by the Ministry of Science and ICT (MSIT), Republic of Korea.

BYLINE: Melody Warnick

Newswise — Racing through the air at Olympic speeds, athletes at the Winter Olympics in Milan will need more than strength and skill—they’ll need science. In sports like ski jumping, skeleton, and speed skating, aerodynamics can make the difference between getting the gold or going home empty-handed.

And athletes know it. A scandal erupted at the Nordic World Ski Championships recently when Norwegian team coaches illegally enlarged ski jumpers’ suits to enhance aerodynamics, in the hopes the skiers would fly a few extra meters. One former champion called it “doping, just with a different needle.”

Virginia Tech aerodynamics expert Chris Roy explained what athletes are doing to take advantage of the science of aerodynamics. 

Why did Norwegian coaches alter ski jumpers’ suits?

“When trying to fly without propulsion, it comes down to maximizing your lift while minimizing your drag,” Roy said. “One way to do that is by increasing your surface area, which is what the Norwegian coaches were trying to do.”

But that’s not the only way, Roy said. “You can also get higher lift by curving your shape, called camber, or by changing your angle relative to the oncoming wind. Increasing camber or angle both increase lift, but there’s a limit. Too much camber or angle can lead to stall, where lift drops dramatically and drag increases. You don’t want to hit stall during a ski jump.”

For Olympic athletes, how can aerodynamics shave off time?

“Shape is one of the key aspects of aerodynamics,” Roy said. “Low drag requires an aerodynamic shape.”

“That’s why ski jumpers form a V with their skis, turning their body into efficient lift-generating surfaces. A streamlined wing shape can have 10 times less drag than a circular shape of the same thickness,” Roy said.

Aerodynamics shows up in speed skating too, when skaters “draft” behind others. “By skating behind others, you can drastically reduce your aerodynamic drag, in some cases by up to 40 percent, allowing the skaters in the back to significantly reduce their effort.”

How do athletes use engineering research to train for the Winter Olympics? 

“Lots of Winter Olympic sports use wind tunnel testing to improve aerodynamics, equipment, and apparel, including ski jumping, speed skating, bobsled, skeleton, and luge,” Roy explained. “These sports also use computational fluid dynamics to model these effects on the computer.”  

About Roy

Chris Roy is a professor in the Kevin T. Crofton Department of Aerospace and Ocean Engineering at Virginia Tech, where he’s affiliated with the Center for Research and Engineering in Aero/Hydrodynamic Technologies (CREATe). His research expertise centers around computational fluid dynamics, aerodynamics, and the reliability of computer simulations. Read more about him here.

Schedule an interview

To schedule an interview with Chris Roy, contact Mike Allen at mike.allen@vt.edu or 540-400-1700. 

Newswise — Georgia Institute of Technology has named Michael “Mike” Gazarik as the new director of the Georgia Tech Research Institute (GTRI) and a Georgia Tech senior vice president, effective February 16. 

A nationally respected aerospace and research leader, Gazarik has led large, complex research organizations across government, industry, and academia, shaping strategy, driving growth, and building institutions that deliver mission-critical innovation. With more than three decades of experience, his career reflects a deep ability to align technology with national priorities and guide organizations through periods of change and opportunity. 

A Georgia Tech alumnus, Gazarik currently serves as faculty director of the Engineering Management Program at the University of Colorado Boulder and as a part‑time staff member at the Johns Hopkins Applied Physics Laboratory. He previously held senior leadership roles at NASA, including director of engineering at NASA Langley Research Center and inaugural associate administrator for the Space Technology Mission Directorate (STMD). In industry, he spent eight years as vice president of engineering at Ball Aerospace, leading its strategic growth from an elite science contractor into a strategic national security asset that doubled in size.

“Mike Gazarik brings a rare combination of technical depth, executive leadership, and deep government experience,” said Tim Lieuwen, Georgia Tech’s executive vice president for Research. “He knows large research enterprises operate within the realities of policy and budget and has a proven ability to align technology with mission priorities while earning trust across stakeholders. We are excited to welcome Mike back to Georgia Tech to lead GTRI at a pivotal moment for research and innovation.”

GTRI employs more than 3,000 employees, conducting nearly $1 billion in annual research in areas such as autonomous systems, cybersecurity, electromagnetics, electronic warfare, modeling and simulation, sensors, systems engineering, and threat systems. GTRI’s renowned researchers combine science, engineering, economics, and policy to address challenges facing national security, industry, and society.

For nearly a century, GTRI has partnered with government and industry to deliver solutions to the most mission-critical challenges facing our nation,” said Georgia Tech President Ángel Cabrera. “We are proud to welcome Mike Gazarik to lead a crown jewel of our research enterprise and a crucial component of our nation’s science and technology fabric. His experience and leadership will strengthen GTRI’s ability to deliver on its mission and help make our nation safer, healthier, and more competitive.”

Gazarik is widely recognized for leading complex research enterprises with a focus on stability, strategic alignment, and mission impact. At NASA, he helped shape the agency’s science and technology enterprise during periods of fiscal constraint and technical risk, maintaining balance across broad mission areas and forming STMD to consolidate technology development. At Ball Aerospace, he guided significant growth and aligned strategy with evolving national security and civil space needs. His academic work has focused on preparing engineering leaders for mission-driven organizations — experience that aligns closely with GTRI’s role as a trusted partner to government and industry.

He earned a B.S. in electrical engineering from the University of Pittsburgh and an M.S. and Ph.D. in electrical engineering from Georgia Tech. Gazarik is a fellow of the American Institute of Aeronautics and Astronautics (AIAA), a former chair of AIAA’s Corporate Strategic Committee, and was elected to the AIAA Board of Trustees in 2025. His honors include NASA’s Outstanding Leadership Medal, the Silver Snoopy Award, the 2023 AIAA Rocky Mountain Section Educator of the Year, and recognition as Engineering Manager of the Year by the American Society of Engineering Management.

“GTRI has a remarkable legacy of delivering solutions that matter for the nation,” said Gazarik. “I’m honored to return to Georgia Tech and lead an organization that combines deep technical expertise with a mission-driven culture. My focus will be on listening, building on GTRI’s strengths, and ensuring we continue to advance research that makes a real difference for our partners and society.”

As director, Gazarik will lead GTRI’s multidisciplinary research enterprise, advancing its mission to deliver high‑impact science and technology solutions in support of national security, space systems, and critical societal needs.

BYLINE: Will Ferguson

Newswise — When it comes to powering aircraft, jet engines need dense, energy-packed fuels. Right now, nearly all of that fuel comes from petroleum, as batteries don’t yet deliver enough punch for most flights. Scientists have long dreamed of a synthetic alternative: teaching microbes to ferment plant material into high-performance jet fuels. But designing these microbial “mini-factories” has traditionally been slow and expensive because of the unpredictability of biological systems.

In a pair of recent studies, two teams at the Joint BioEnergy Institute (JBEI), which is managed by Lawrence Berkeley National Laboratory (Berkeley Lab), have demonstrated complementary ways to dramatically speed up this process. One combines artificial intelligence and lab automation to rapidly test and refine the genetic designs of biofuel-producing microbes. The other turns a microbe’s “bad habit” into a powerful sensing tool, uncovering hidden pathways that boost production.

Their shared target is isoprenol — a clear, volatile alcohol that can be converted into DMCO, a next-generation jet fuel with higher energy density than today’s conventional aviation fuels. Producing isoprenol efficiently has been a long-standing challenge in synthetic biology.

The two studies — one published in Nature Communications, the other in Science Advances — tackle different sides of this challenge. The first uses automation and machine learning to engineer Pseudomonas putida strains that produce five times more isoprenol than before. The second approach turns the bacterium’s natural fuel-sensing ability into an advantage. By rewiring that system into a biosensor, the team could rapidly screen millions of variants and identify strains that make up to 36 times more isoprenol.

“These are two powerful complementary strategies,” said senior author of the biosensor study Thomas Eng, JBEI deputy director of Host Engineering and a research scientist in Berkeley Lab’s Biological Systems and Engineering (BSE) Division. “One is data-driven optimization; the other is discovery. Together, they give us a way to move much faster than traditional trial-and-error.”

A new engine for strain design

The AI and automation study was led by Taek Soon Lee, director of Pathway and Metabolic Engineering at JBEI, and Héctor García Martín, director of Data Science and Modeling at JBEI, both staff scientists in Berkeley Lab’s BSE Division. They set out to accelerate one of synthetic biology’s most time-consuming steps: improving microbial production through a series of genetic tweaks to different combinations of genes. Traditionally, scientists alter a few genes at a time and test the results — a painstaking, intuition-driven process that can take months or even years to yield meaningful gains.

By contrast, the Berkeley Lab researchers built an automated pipeline that uses robotics to create and test hundreds of genetic designs in parallel. After each round, machine learning algorithms analyze the results to systematically suggest the next set of strain genetic designs. The result is a system that moves 10 to 100 times faster than conventional methods.

“Standard metabolic engineering is slow because you’re relying on human intuition and biological knowledge,” said García Martín. “Our goal was to make strain improvement systematic and fast.”

Lead author David Carruthers, a scientific engineering associate with JBEI and BSE, developed a robotic workflow that connects key lab steps into one automated system. Working with collaborators at Lawrence Livermore National Laboratory, the team introduced a custom microfluidic electroporation device that can insert genetic material into 384 Pseudomonas putida strains in under a minute — a task that typically takes hours by hand.

At the core of the system is CRISPR interference (CRISPRi), a tool that lets researchers “turn down” gene activity rather than switching genes off completely. This fine-tuning makes it possible to test subtle gene combinations that shape the cell’s metabolism and track the effects through detailed protein measurements. After each round, the machine learning model analyzes the results and recommends the next set of genes that are most likely to boost performance when dialed down.

“Traditionally, optimizing production is a kind of guess-and-check process,” Carruthers said. “You make one change, test it, and hope you’re climbing toward a higher peak. By combining automation and machine learning, we were able to climb that landscape systematically — in weeks, not years.”

Lee, who led the metabolic engineering work, emphasized why this level of automation is so transformative for biology.

“We have been engineering Pseudomonas by hand for years, but biological experiments always come with small variations that are hard to control,” he said. “Automation gives us the ability to generate the same high-quality data every time, which is essential for machine learning to work well.”

Patrick Kinnunen, a former Berkeley Lab JBEI postdoctoral researcher who co-developed the data strategy, highlighted how crucial that reproducibility was for the algorithms. “Automation didn’t just make the experiments faster — it made the data cleaner,” he said. “That clarity is what lets it uncover non-intuitive genetic combinations that we probably would have missed by hand.”

Using their automated learning loop, the team completed six engineering cycles, each lasting just a few weeks instead of the months typical of manual workflows. They boosted isoprenol titers (the concentration of product in the culture) five-fold compared to their starting strain.

Turning a bug into a feature

Meanwhile, a second team led by Eng tackled a different but equally stubborn hurdle: how to select target genes that, when dialed down, improve isoprenol production significantly. The team’s microbe, Pseudomonas putida, posed a peculiar problem. It didn’t just make isoprenol, it also consumed the fuel molecule almost as soon as it produced it, undermining production efforts. Initially, this looked like a flaw. But during the COVID-19 pandemic, Eng and colleagues realized it might be a clue: if the microbe could sense and eat isoprenol, it likely had a built-in molecular sensor.

“There was a real ‘Aha!’ moment,” Eng said. “We had spent more than a year trying to figure out why the cells were consuming the product. One day we thought, ‘Wait, if they can sense it, there has to be a protein that detects it. Maybe we can turn that from a problem into a tool.’”

The team discovered the molecular system the microbe uses to sense isoprenol: two proteins that work together to detect the fuel and send signals inside the cell. They then rewired this system into a biosensor — a kind of biological “engine light” that turns on in proportion to how much fuel the cell produces.

Then came the clever twist: They linked the sensor to genes essential for survival, creating a system where only the microbes that make the most fuel can grow. Instead of measuring thousands of samples by hand, they let natural selection do the screening. This approach rapidly surfaced “champion” strains, including variants that produced up to 36 times more isoprenol than the original.

“What started as a frustrating bug became our biggest asset,” Eng said. “We turned the microbe’s fuel-eating behavior into a sensor that reports and selects for the best producers automatically.”

The approach also revealed surprising biology; high-producing strains switched to feed on their own amino acids once glucose ran out, sustaining production by rewiring their metabolism in unexpected ways. Just as importantly, the workflow can be applied to other molecules, offering a flexible new tool for rapidly engineering microbes — not just for isoprenol, but for a wide range of bio-based products.

Scaling up to industry-ready

Although developed independently, the two approaches fit together well. The AI-driven pipeline excels at rapidly optimizing combinations of a known set of gene targets, while the biosensor method is best for discovering novel gene targets, revealing genetic levers that would be difficult to predict.

“One is depth-first; the other is breadth-first,” Eng said. “Machine learning systematically optimizes combinations of annotated targets, while the biosensor approach starts fresh and lets the cells tell us which gene targets matter.”

Both teams are now working to scale their methods from lab experiments to industrially relevant fermentation systems — a critical step for producing synthetic aviation fuel at commercial levels. They’re also adapting their approaches to other microbes and target molecules, aiming to make them broadly applicable in biomanufacturing.

“If widely adopted, these approaches could reshape the industry,” García Martín said. “Instead of taking a decade and hundreds of people to develop one new bioproduct, small teams could do it in a year or less.”

Aindrila Mukhopadhyay, BSE deputy director for science, director of Host Engineering at JBEI, and a coauthor on the biosensor study, said these kinds of tools are changing how biological research gets done.

“Engineering biology is challenging due to the inherent unpredictability of metabolism and that makes the engineering slow,” Mukhopadhyay said. “By streamlining key steps — as we did through selections — and leveraging automation and AI, we’re making it a faster, more systematic process that is easier to adopt.”

JBEI is a Bioenergy Research Center funded by the Department of Energy Office of Science.

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Lawrence Berkeley National Laboratory (Berkeley Lab) is committed to groundbreaking research focused on discovery science and solutions for abundant and reliable energy supplies. The lab’s expertise spans materials, chemistry, physics, biology, earth and environmental science, mathematics, and computing. Researchers from around the world rely on the lab’s world-class scientific facilities for their own pioneering research. Founded in 1931 on the belief that the biggest problems are best addressed by teams, Berkeley Lab and its scientists have been recognized with 17 Nobel Prizes. Berkeley Lab is a multiprogram national laboratory managed by the University of California for the U.S. Department of Energy’s Office of Science.

DOE’s Office of Science is the single largest supporter of basic research in the physical sciences in the United States, and is working to address some of the most pressing challenges of our time. For more information, please visit energy.gov/science.