Chonnam National University researchers investigate how biodiesel feedstocks and production pathways affect their life cycle sustainability

Study provides biofuel producers with a practical, multi-dimensional framework for choosing feedstocks by weighing both climate impact and air pollution together

Chonnam National University, The Research Information Management Team, Office of Research Promotion

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Researchers reveal that waste-derived pathways exhibit the lowest greenhouse gas emissions while plant-based pathways dominate by Scope 3 contributions from farming and indirect land-use change.

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Credit: Professor Boreum Lee from Chonnam National University, Republic of Korea

For mitigating climate change and limiting global warming to within 1.5 °C, scaling up global renewable energy capacity is an important goal. However, this effort towards transition to clean energy faces significant resource constraints. To address this challenge, diverse approaches are required like biodiesel, which can serve as a crucial bridge technology for decarbonization of the transportation sector while renewable energy is scaled up.

Biodiesel is compatible with already existing diesel infrastructure and can be deployed quickly without extensive modifications, making it a particularly promising solution. However, not all biodiesel is equally sustainable. Its environmental impact depends strongly on the feedstock used and the way it is produced.  This creates a complex decision-making landscape for policymakers and stakeholders, necessitating comprehensive life-cycle assessments (LCAs) of different biodiesel pathways. Although previous LCAs have compared biodiesel pathways, most have focused primarily on total greenhouse gas (GHG) emissions and a limited range of feedstocks. They have also rarely examined feedstock-specific mitigation strategies or air-pollution impacts.

To address this gap, a research team led by Professor Boreum Lee along with Mr. Sanghyuk Koh, both from the Department of Environment and Energy Engineering, Chonnam National University, Republic of Korea, presents a comprehensive Greenhouse gases, Regulated Emissions, and Energy use in Technologies (GREET)-based framework for LCA across five different biodiesel feedstocks. Their study was made available online on July 01, 2026 and was published in Volume 422 of Applied Energy on November 01, 2026.

The study considered three plant-based feedstocks, namely soy oil, carinata oil, and palm oil, representing diverse agricultural systems, and two waste-derived feedstocks, namely used cooking oil (UCO) and beef tallow. A Well-to-Tank approach, based on the GREET model, was adopted aiming to capture emissions across the full life cycle. The full life cycle was divided into three categories: Scope 1 that covers direct emissions during biodiesel production, Scope 2 that encompasses indirect emissions from energy use, and Scope 3, which integrates emissions from major upstream and downstream processes.

For plant-based feedstocks, the upstream processes included farming, oil extraction, refining, transportation, and emissions from indirect land use change (ILUC). In contrast, waste-derived feedstocks included rendering, oil extraction, and refining as upstream processes, while overseas import and long-distance international transport were excluded.

Emissions analysis showed that waste-derived feedstocks consistently demonstrated lower GHG emissions than plant-based oils. This is mainly due to the absence of emissions from upstream agriculture and land use. Among plant-based options, carinata oil showed the lowest emissions as it avoids ILUC emissions. For plant-based feedstocks, Scope 3 farming processes had the greatest contribution to GHG emissions, while for waste-derived feedstocks Scope 1 and the Scope 3 refining stage were dominant.

“Waste-derived pathways, when paired with renewable energy inputs in processing, can achieve net-negative emissions, meaning they could actually remove more carbon from the atmosphere than they emit with a reduction of up to 346–352% relative to their own baseline (conventional-input) production,” says Prof. Lee.

Interestingly, air-pollution analysis showed that low GHG intensity did not necessarily mean uniformly low pollution. For example, carinata oil, despite its lowest plant-based GHG emissions, had the highest volatile organic compound (VOC) and carbon monoxide (CO) levels across all pathways. These findings highlight the importance of considering air-pollution impacts alongside GHG emissions when evaluating biodiesel sustainability.

Furthermore, Monte Carlo simulation-based uncertainty analysis, aimed at capturing the effect of data variability, showed wider emission ranges for plant-based pathways, compared to much narrower distributions for waste-derived feedstocks.

Importantly, best-case GHG mitigation scenarios revealed emission reduction potentials ranging from 66% to 352%. Waste-derived feedstocks offered the greatest mitigation potential, achieving net-negative GHG emissions through the use of renewable energy in rendering and refining processes.

“Our findings argue against one-size-fits-all biofuel mandates: regions with strong waste-collection infrastructure should prioritize used-oil-based biodiesel, while agricultural regions may benefit more from dedicated energy crops. In the near future, this feedstock-specific, region-aware analysis approach will help policymakers design smarter policies and foster the broader biofuel industry, including aviation and marine fuels, paving the way towards a more sustainable future,” concludes Prof. Lee.

 

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Reference

Title of original paper: Decarbonizing biodiesel supply chains: a GREET-based life cycle assessment with Scope 1–3 emissions and best-case mitigation

Journal: Applied Energy

DOI: https://doi.org/10.1016/j.apenergy.2026.128326

                                

About the institute
Chonnam National University (CNU), established in 1952, is one of South Korea's leading national universities located in Gwangju. Building on its founding commitment to cultivating leaders of integrity and professional excellence, CNU contributes to national development and global progress through the pursuit of knowledge, ethical responsibility, and inclusive excellence. Guided by the core motto “Truth, Creativity, and Service,” the university advances research, education, and public engagement that strengthen resilient societies, foster sustainable development, and promote the well-being of future generations. As a trusted partner in the global community, CNU remains dedicated to addressing complex challenges in an increasingly interconnected world.

Website: https://global.jnu.ac.kr/jnumain_en.aspx

 

About Professor Boreum Lee
Dr. Boreum Lee is a Professor at the Department of Environment and Energy Engineering, Chonnam National University, Republic of Korea. His research focuses on the design and evaluation of carbon-neutral process systems, including hydrogen production, CO₂ capture, utilization and storage, ammonia synthesis and cracking, Power-to-X technologies, and water treatment. By integrating Aspen Plus process simulation with techno-economic and environmental assessments, he works to develop and optimize sustainable energy and chemical processes.

Website: https://boreumlee.com/

 

About Mr. Sanghyuk Koh
Mr. Sanghyuk Koh is a master’s candidate at the Department of Environment and Energy Engineering, Chonnam National University, Republic of Korea. His research focuses on the life cycle assessment and techno-economic analysis of biodiesel supply chains and green hydrogen production to evaluate their environmental and economic sustainability.

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Journal

Applied Energy

DOI

10.1016/j.apenergy.2026.128326 

Method of Research

Experimental study

Subject of Research

Not applicable

Article Title

Decarbonizing biodiesel supply chains: a GREET-based life cycle assessment with Scope 1–3 emissions and best-case mitigation

Article Publication Date

1-Nov-2026

 

High-entropy electrode improves efficiency of green hydrogen production

Hefei Institutes of Physical Science, Chinese Academy of Sciences

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High-Entropy Electrode Improves Efficiency of Green Hydrogen Production

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Credit: CHEN Bin

A research team led by Prof. MENG Guowen and Prof. CHEN Bin from the Institute of Solid State Physics, Hefei Institutes of Physical Science, Chinese Academy of Sciences, has developed a high-entropy antiperovskite electrode (InN(NiCoFeCrV)₃) supported by nickel foam for efficient anion-exchange membrane (AEM) water electrolysis, improving the efficiency and durability of hydrogen production.

The integrated electrode enabled an AEM electrolyzer to operate efficiently at high current densities, demonstrating its potential for large-scale green hydrogen production.

The study was published in ACS Nano.

Water electrolysis uses renewable electricity from sources such as solar and wind power to produce clean hydrogen. Among various technologies, AEM water electrolysis combines the advantages of traditional alkaline systems and proton-exchange membrane systems. However, its large-scale application is still limited by the slow oxygen evolution reaction (OER) and the need for catalysts with high activity and long-term stability.

To address this challenge, the researchers designed a high-entropy antiperovskite electrode containing five metal elements — nickel, cobalt, iron, chromium, and vanadium — grown directly on nickel foam. The unique structure helps maintain catalytic activity while promoting the formation of active sites during operation.

"Our goal was to develop a durable and efficient electrode for large-scale hydrogen production," said Prof. MENG Guowen, "This electrode showed excellent oxygen evolution performance and long-term stability in AEM water electrolysis, demonstrating its potential for practical hydrogen production."

Tests showed that the electrode required an overpotential of only 279 mV to reach a current density of 100 mA cm-2 and remained stable for more than 500 hours in alkaline electrolyte. When used in an AEM electrolyzer, it delivered 500 mA cm-2 at a cell voltage of 1.662 V and maintained stable operation for over 400 hours with little performance loss.

Further analysis showed that some metal elements gradually dissolved during operation, leading to the formation of an active surface layer. The interaction between the surface layer and the underlying material improved charge transfer and enhanced catalytic performance.

The study provides a new strategy for designing durable, low-cost electrodes for AEM water electrolysis and supports the development of more efficient green hydrogen technologies.

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Journal

ACS Nano

DOI

10.1021/acsnano.6c02462 

Article Title

Defect-Driven Surface Reconstruction in High-Entropy Antiperovskite to Generate Mott–Schottky Interface for Boosting Oxygen Evolution

 

Green hydrogen production and storage using alcohols, iron, and baker's yeast

A new recyclable system uses baker's yeast to store H₂ in organic molecules and iron ions to release it when needed - offering a promising route toward more sustainable hydrogen production and storage.

Tohoku University

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H₂ storage performance of polyhydric alcohols (LOHC) and the green H₂ production and storage cycle.

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Credit: Kouki Oka et al.

Hydrogen gas (H₂) is widely regarded as a promising clean energy source. By weight, it stores nearly three times more energy than gasoline, and its use does not directly release carbon dioxide (CO₂). Yet most H₂ is still produced from fossil fuels through CO₂-intensive processes. Storing and transporting this energy-rich gas are also costly and technically challenging. Therefore, developing sustainable methods for its production, storage, and safe transportation could lead to a cleaner energy future.

A research team from Tohoku University, in collaboration with Hokkaido University and Kyushu University, has taken a major step toward making this a reality by creating a new method for producing and storing H₂.

The method is based on a liquid organic hydrogen carrier (LOHC) system that can store and release H₂ through reversible chemical reactions. Their concept uses polyhydric alcohols and polyketones as LOHCs, with baker's yeast helping to store H₂ produced from sustainable resources and iron ions releasing it.

In the proposed cycle, a polyketone, with the help of baker's yeast, water, and NADH - a biological molecule (coenzyme) involved in fermentation - transforms into a H₂-rich polyhydric alcohol.

Unlike conventional methods, this approach does not require H₂ to be produced, purified, compressed, and then introduced into the LOHC. Instead, H₂ produced from sustainable resources is stored directly in the organic molecule during the yeast-assisted reaction. When H₂ is needed, the polyhydric alcohol is converted back into the polyketone.

The team also demonstrated that light irradiation in the presence of iron ions can trigger H₂ release. Iron is inexpensive and abundant in the Earth's crust, making it a potentially more sustainable alternative to the precious-metal catalysts commonly used for this process.

The work demonstrates a recyclable green H₂ production and storage cycle using earth-abundant materials and biocatalysts. Ongoing work will test the method with more suitable alcohols, such as ethylene glycol. The researchers will also work to improve H₂ storage and release for practical use.

The research was published online in Journal of Materials Chemistry A on August 24, 2026.

Comparison of the alcoholic fermentation-based direct H₂ storage process with conventional technology.

Photocatalytic H₂ release method using low-cost, Earth-abundant iron ions.

Credit

Kouki Oka et al.

Journal

Journal of Materials Chemistry A

DOI

10.1039/d6ta03349k 

Article Title

Hydrogen Gas Production and Storage Cycle of Polyhydric Alcohols/Polyketones

Article Publication Date

24-Aug-2026

Freezing fire: UT San Antonio scientists discover breakthrough for green energy

University of Texas at San Antonio

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As the global push for cleaner energy intensifies, solid oxide fuel cells have emerged as a promising option. They turn hydrogen or other renewable fuels directly into electricity and heat without combustion, eliminating virtually all pollution and boosting efficiency to over 60%. But a major downside has kept them out of everyday commercial use: they require extremely high temperatures to operate.

A team of researchers at UT San Antonio and partnering institutions have found a way around that barrier. Instead of relying on perfectly ordered materials, they applied a thermal shock technique to create tiny clusters of disordered atoms that move oxygen ions much more easily at lower temperatures. The result could help make fuel cells cheaper, more durable and easier to use outside the lab.

“The golden rule has been that you need a perfect crystal lattice for fast ion movement,” said Chonglin Chen, PhD, a professor in the Department of Physics and Astronomy in the College of Sciences. “What we have done here challenges that assumption.”

The team recently published their findings in Science Advances, with Shengli Pang, a Jiangsu University researcher and member of Chen’s team, serving as the lead author.

The sub-400°C breakthrough

Conventional solid oxide fuel cells (SOFCs) operate “inadequately” at temperatures lower than 400 degrees Celsius, making them incredibly difficult to use in commercial settings, Chen explained.

Conventional SOFCs need extreme temperatures, often exceeding 700°C, to operate. These volcanic temperatures accelerate material breakdown, demand expensive heat-resistant components to contain the heat, and cause long startup delays.

To solve this problem, the team turned to molecular nanotechnology, using extreme temperature swings to create materials with helpful imperfections at the atomic level.  First, they baked a standard ceramic fuel cell material at a scorching 1,300°C. They then rapidly froze it through a technique called quenching — plunging it into liquid nitrogen at nearly –196°C. This violent temperature shock shatters the material’s rigid, glass-like structure into ultra-thin, microscopic clusters of atoms measuring just 0.63 nanometers thick — so small that thousands of them could stack across the width of a single human hair.

Chen said the idea came from steelmaking, where quenching is used to change the material’s structure. The team adapted that approach for ceramics and found that the resulting clusters conducted oxygen ions far better than expected.

When tested at 400°C, the material achieved record oxygen-ion conductivity, about 1,400 times higher than a conventional ceramic material.

The chaos advantage

The breakthrough challenges a longstanding assumption in materials science that the best conductors must have a perfectly ordered, rigid crystal structure, with the neat rows of atoms acting like well-defined lanes on a highway.

But when the team examined their new material with electron microscopes and X-ray analysis, they found that the atoms inside these tiny fragments were disordered and chaotic. Yet they were still performing well.

This is because in traditional materials, the atomic-scale paths eventually become blocked as atoms clump together under stress, creating bottlenecks that interrupt the energy flow. But inside the new, disordered nanoclusters were oxygen vacancies that stayed separate and active, while interacting to create a network that allowed ions to travel more freely.

“With these vacancy-isolated clusters, we created a chaotic, highly dynamic network where the oxygen vacancies remain independent,” Chen explained. “Instead of fighting the disorder, we are using it to create a kind of superhighway for the ions.”

What this means for clean energy

To demonstrate commercial viability, the team tested whether the material could improve real fuel cell performance. By blending a trace amount of the clusters — just 0.5% by weight — with a conventional cobalt-based fuel cell cathode, they tripled the fuel cell’s peak power output.

In addition to boosting power, the clusters also boost durability. Standard fuel cells degrade quickly, losing over 13% of their power every 100 hours of use under the intense stress of high temperatures. But fuel cells enhanced with the disordered nanoclusters became 3.4% more stable and efficient with continued use.

“It acts as an atomic shield, boosting power while actively stopping the degradation that normally kills these devices,” Chen said.

Chen believes that the method could be easy for manufacturers to adopt because it requires only a small amount of the new material rather than a complete redesign of existing systems. They are now working on ways to scale up production.

“This will bring us one step closer to practical, next-generation green energy,” Chen added.

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Journal

Science Advances

Article Title

Disordered vacancy-isolated Ce-Gd-O clusters achieve exceptional low-temperature oxygen-ion conductivity for fuel cells

 

A novel framework to enhance high-resolution images taken in poor lighting conditions

Researchers devise a multistage approach that uses coarse enhancement to guide the recovery of fine details, outperforming state-of-the-art techniques

Chinese Association of Automation

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Systems that can restore proper lighting in a low-light, high-resolution photograph often struggle to preserve finer details and structures, calling for new techniques.

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Credit: salmanghaffar15 from Flickr Image source link: https://www.flickr.com/photos/50717189@N05/22664940938

Modern cameras can capture an extraordinary amount of detail, producing images made up of millions of pixels that reveal textures, edges, and colors invisible to earlier generations of digital photography. In poor lighting conditions, however, much of that information becomes difficult to recover, with pictures coming out grainy and details getting lost in dark patches of the frame. Now that cameras are being increasingly used as inputs for computer-vision systems in domains such as surveillance, healthcare, and autonomous driving, the issues introduced by poor lighting extend beyond aesthetics.  

Interestingly, fixing this problem only gets harder as image resolution goes up. An ultra-high-definition (UHD) image contains both broad, scene-level information and extremely fine details, so machine learning-based enhancement systems must handle these scales carefully; they need to preserve overall illumination, color, and scene structure while properly recovering small features. On top of this, the huge number of pixels in a UHD image makes it difficult to use sophisticated neural networks on consumer-grade hardware. How can we use machine learning to enhance UHD low-light images, preserving global appearance and fine details, without excessive computational demands? 

To address this problem, a research team led by Professor Jiayi Ma and Dr. Hao Zhang from Wuhan University, China, has developed a new image enhancement method called LL-Refiner. Their study, published in Volume 13, Issue 6, of the IEEE/CAA Journal of Automatica Sinica on July 3, 2026, presents a framework designed specifically for the efficient enhancement of low-light UHD images.  

Rather than relying on a direct, one-step enhancement of the heavy high-resolution image, LL-Refiner operates in two coordinated stages. First, an enhanced coarse version of the image is produced at a lower resolution using a Transformer-based neural network, which efficiently handles global lighting, color distribution, and overall scene structure. This coarse result is then injected into an adaptive refinement network via cross-attention modules, progressively guiding the network to sharpen edges, textures, and fine text across hierarchical scales up to full resolution. 

The team tested LL-Refiner against several leading enhancement methods using real-world low-light datasets, including images captured with a smartphone camera under conditions different from those used in training. The results consistently favored the new approach, as Prof. Ma remarks: “Our method successfully preserves both the clarity of textual regions and the fine structure of patterns, demonstrating a balanced enhancement in both global consistency and local detail.” 

Beyond visual quality, the team also tested whether their enhanced images could improve performance in a separate computer-vision task, namely depth estimation, which is used in applications like robotics and autonomous navigation. Images enhanced with LL-Refiner led to more accurate depth predictions than images processed with other methods, indicating the improvements are not just cosmetic. “LL-Refiner was the only method to yield reasonably accurate background depth estimation,” highlights Prof. Ma. “The other approaches failed to capture background structures, indicating their limited effectiveness in supporting downstream tasks under low-light conditions.” 

Overall, the results suggest that this coarse-to-fine approach could inform future systems designed to process high-resolution images efficiently on consumer-grade hardware. In turn, this could serve as the foundation for various applications in photography, surveillance, and many computer-vision technologies that depend on images captured in difficult lighting. 

 

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Reference
DOI: 10.1109/JAS.2026.125939 

 

About Wuhan University 
Wuhan University (WHU) is a comprehensive and key national university directly under the administration of the Ministry of Education. Founded in 1893 by Zhang Zhidong, it is one of the “211 Project” and “985 Project” universities that received full support in construction and development from the central and local governments of China. The university currently has over 53,000 students and 3,700 teachers and is recognized as one of China’s leading institutions for education and research, with a strong international presence and broad academic strengths spanning the sciences, engineering, medicine, humanities, and social sciences. 
Website: https://en.whu.edu.cn/ 

 

About Professor Jiayi Ma from Wuhan University 
Dr. Jiayi Ma received a B.S. degree in information and computing science and a Ph.D. degree in control science and engineering from Huazhong University of Science and Technology in 2008 and 2014, respectively. He is currently a Professor at both the Electronic Information School and the School of Robotics at Wuhan University. He has coauthored more than 400 refereed journal and conference papers, with publications in Cell, IEEE Transactions on Pattern Analysis and Machine Intelligence, International Journal of Computer Vision, and other prestigious journals.  

 

About Dr. Hao Zhang from Wuhan University 
Dr. Hao Zhang received a B.E. degree from the School of Mechanical Engineering and Electronic Information at the China University of Geosciences in 2019, as well as M.S. and Ph.D. degrees from Wuhan University in 2021 and 2024, respectively. He is currently a Postdoctoral Researcher with the Electronic Information School at Wuhan University. He has first-authored over 10 refereed journal and conference papers, with publications in IEEE Transactions on Pattern Analysis and Machine Intelligence, International Journal of Computer Vision, Conference on Computer Vision and Pattern Recognition (CVPR), NeurIPS, and AAAI, among others. His research interests include computer-vision, machine learning, and pattern recognition. 

 

Funding information 
This work was supported by the National Natural Science Foundation of China (625B2135, 62506268, and 62276192). 

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Journal

IEEE/CAA Journal of Automatica Sinica

DOI

10.1109/JAS.2026.125939 

Method of Research

Experimental study

Subject of Research

Not applicable

Article Title

LL-Refiner: Learning Adaptive Refinement for Ultra-High-Definition Low-Light Image Enhancement