A Cooler Way to Capture Carbon: Georgia Tech’s LNG-Powered Climate Innovation

Georgia Tech Carbon Capture

As the fight against climate change accelerates, researchers are innovating ways to make carbon capture technologies more efficient, affordable, and scalable. In a groundbreaking study, scientists at Georgia Tech have developed a method to enhance CO₂ capture by utilizing extreme cold from liquefied natural gas (LNG) and common porous materials. This “cooler” approach has the potential to revolutionize direct air capture (DAC) systems and cut greenhouse gas emissions on a global scale.

Published in Energy & Environmental Science, the study highlights how physisorbent materials, when paired with LNG’s unused cold energy, can outperform traditional chemical-based systems in both efficiency and cost.

Chilling Out to Fight a Warming Planet

Current DAC technologies often rely on amine-based materials that chemically react with CO₂. While effective, these systems are costly, energy-intensive, and degrade over time. By contrast, physisorbents physically absorb gases without chemical bonding, allowing for faster uptake, lower energy requirements, and longer lifespans. However, they typically struggle in warm, humid air.

To overcome this limitation, the Georgia Tech team repurposed the extreme cold from LNG regasification—the process of turning liquefied gas back into its gaseous state. This innovative approach chills ambient air to near-cryogenic levels, naturally removing water vapor and creating ideal conditions for physisorbents to thrive.

Professor Ryan Lively from Georgia Tech explained, “We’re showing that you can capture carbon at low costs using existing infrastructure and safe, low-cost materials.”

Materials That Work Better in the Cold

The study tested materials like Zeolite 13X and CALF-20. At -78°C, these materials captured up to three times more CO₂ compared to their performance at room temperature. Both materials also exhibited low energy requirements for CO₂ release, making them highly practical for industrial applications.

“Beyond their high CO₂ capacities, both physisorbents exhibit critical characteristics such as low desorption enthalpy, cost efficiency, scalability, and long-term stability, all of which are essential for real-world applications,” said lead author Seo-Yul Kim, a postdoctoral researcher in the Lively Lab.

Economic and Environmental Impact

Economic modeling suggests that this method could reduce DAC costs to as low as $70 per metric ton—less than one-third the cost of current systems. By leveraging existing LNG infrastructure, the technology could be deployed globally, including in temperate and coastal regions, which are often unsuitable for conventional DAC methods.

Professor Matthew Realff, co-author of the study, added, “Many physisorbents that were previously dismissed for DAC suddenly become viable when you drop the temperature. This unlocks a whole new design space for carbon capture materials.”

Looking Ahead

The research team is now focusing on refining these materials and optimizing system designs to ensure long-term performance at industrial scales. With this approach, the untapped cold energy from LNG terminals could enable the capture of over 100 million metric tons of CO₂ annually by 2050, offering a promising pathway toward net-zero emissions.

Read the full article on Interesting Engineering here: Scientists Find a Cooler Way to Fight Climate Change.

Sponsored by PWmat (Lonxun Quantum) – a leading developer of GPU-accelerated materials simulation software for cutting-edge quantum, energy, and semiconductor research. Learn more about our solutions at: https://www.pwmat.com/en

📘 Download our latest company brochure to explore our software features, capabilities, and success stories: PWmat PDF Brochure

📞 Phone: +86 400-618-6006
📧 Email: support@pwmat.com

Hashtags: #CarbonCapture #ClimateChange #SustainableEnergy #Physisorbents #GreenTechnology #QuantumServerNetworks #PWmat

Comments

Post a Comment

Popular posts from this blog

Water Simulations Under Scrutiny: Researchers Confirm Methodological Errors

Image
Accurate water simulations are vital for research in biomolecular structures, drug discovery, and materials science. But a recent study from Oak Ridge National Laboratory (ORNL) has revealed a potential pitfall in long-standing computational methods, raising concerns about the reliability of countless molecular dynamics studies. The Time Step Problem in Simulations At the heart of the issue is the “time step” used in simulations—the small intervals at which calculations are performed. Since the 1970s, scientists have used time steps of 2 femtoseconds (quadrillionths of a second) to simulate molecular motion efficiently. However, the ORNL team has shown that using this standard can introduce significant errors when modeling liquid water, potentially leading to inaccurate predictions of properties such as density, pressure, and hydration energies. “Using longer time steps is tempting because it reduces computational cost,” explained senior scientist Dilip Asthagiri. “But a...
Read more

Quantum Chemistry Meets AI: A New Era for Molecular Machine Learning

Image
In a powerful leap forward for computational chemistry and materials design, researchers from Carnegie Mellon University have developed a new kind of molecular machine learning model—one that goes beyond simplistic molecular graphs and integrates fundamental principles of quantum chemistry. Their work, published in Nature Machine Intelligence , could radically enhance our ability to predict chemical behavior, optimize catalysts, and discover new drugs, all while using less data and gaining more scientific insight. The Problem: Traditional Molecular Representations Fall Short Molecular machine learning (ML) models underpin key processes in drug discovery, materials science, and catalysis. However, most existing models use simplified representations such as SMILES strings, 2D graphs, or coordinate matrices. These techniques often lack the quantum-chemical information that governs how molecules truly behave—particularly electron interactions that define reactivity, stability, and...
Read more

OMol25: A Record-Breaking Dataset Set to Revolutionize AI in Computational Chemistry

Image
Published on: Quantum Server Networks | Date: May 2025 In an era where artificial intelligence is reshaping every scientific frontier, a groundbreaking resource named Open Molecules 2025 (OMol25) has just been launched. This record-breaking dataset is poised to radically transform the way researchers model and simulate molecular interactions, accelerating innovation in materials science, biochemistry, and clean energy. Berkeley Lab News Center reports that the dataset, jointly developed by Meta’s Fundamental AI Research (FAIR) lab and the U.S. Department of Energy’s Lawrence Berkeley National Laboratory, encompasses over 100 million high-fidelity 3D molecular snapshots . These were generated using Density Functional Theory (DFT), a quantum mechanical modeling method renowned for its precision but traditionally limited by massive computational demands. What Makes OMol25 So Important? DFT simulations have long been a gold standard for predicting how atoms behave, includ...
Read more