Copper Alloy Catalysts Reveal Dynamic Surface Changes for Better CO₂ Conversion

Copper Alloy Catalyst Surface Changes

In the race toward carbon neutrality, scientists from Seoul National University (SNU) have made a pivotal breakthrough in understanding how copper alloy catalysts evolve during CO₂ electroreduction reactions. Published in Nature Catalysis and featured as the cover article, their study provides never-before-seen insights into how alloy catalyst surfaces reconstruct under real reaction conditions — a leap forward in designing high-performance systems for clean energy production.

Why Electrochemical CO₂ Reduction Matters

Electrochemical CO₂ reduction reaction (CO₂RR) is a transformative technology that can convert carbon dioxide — the primary greenhouse gas driving climate change — into value-added chemicals such as ethanol and ethylene. Copper (Cu) is one of the few metals capable of facilitating these complex multi-carbon transformations. However, pure copper catalysts often suffer from poor selectivity and limited long-term efficiency.

To overcome these hurdles, researchers have been alloying copper with other metals like silver (Ag), zinc (Zn), iron (Fe), and palladium (Pd) to create bimetallic catalysts that offer enhanced selectivity and efficiency. But until now, one crucial aspect remained elusive: how these alloys actually change during the reaction itself.

Unveiling Catalyst Surface Dynamics in Real Time

Led by Professors Young-Chang Joo and Jungwon Park, the Korean research team tackled this long-standing mystery by utilizing in-situ liquid-phase transmission electron microscopy (TEM) under high current-density conditions — a real-world setup mimicking industrial applications. The team observed dramatic reconstructions at the atomic level, driven by metal dissolution and redeposition dynamics.

In the case of Cu–Ag alloys, copper nanoparticles were seen forming on the surface during reaction, boosting ethanol selectivity even at high silver concentrations. Meanwhile, Cu–Zn alloys showed stable surface compositions but tended to favor CO production instead, due to fewer copper-rich active sites.

Mapping Materials Behavior and Designing Smarter Catalysts

The team’s development of a “material selection map” — based on oxophilicity and miscibility between Cu and its alloying metal — allows scientists to predict how surfaces will evolve during use. This map acts as a blueprint for tailoring catalysts that can adapt dynamically, improving product selectivity and stability in real-time operation.

Moreover, they demonstrated that applying pulsed potentials could even manipulate the dissolution–redeposition process, redirecting selectivity from CO to ethanol — a significant step toward scalable, tunable CO₂ reduction systems.

Toward Commercial CO₂ Conversion

This breakthrough sets a new benchmark in catalyst development by shifting focus from static, pre-synthesized structures to dynamic, in-operando architectures. The insights could eventually generalize to even more complex multimetallic systems, offering durable and adaptable catalysts for industrial-scale CO₂ utilization.

Professor Joo emphasized, “This is the first study to systematically unveil the dynamic reconstruction behavior of alloy catalysts during electrochemical CO₂ reduction. It opens the door to a new paradigm of catalyst engineering based on in-situ evolution.”

As efforts to combat climate change intensify, such innovations in catalyst science are not only intellectually exciting but environmentally essential.

📖 Original source: Phys.org – Copper alloy catalysts' surface changes mapped during CO₂ conversion reactions (September 18, 2025)

*This blog post was prepared with the help of AI technologies to assist in content generation and formatting.*

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

🎁 Interested in trying our software? Fill out our quick online form to request a free trial and receive additional information tailored to your R&D needs: Request a Free Trial and Info

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

#CopperCatalysts #CO2Reduction #Electrocatalysis #CleanEnergy #MaterialsScience #Nanotechnology #GreenTech #SeoulNationalUniversity #NatureCatalysis #QuantumServerNetworks #PWmat

Comments

Post a Comment

Popular posts from this blog

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

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

CrystalGPT: Redefining Crystal Design with AI-Driven Predictions

Image
July 18, 2025 In a major leap for materials chemistry, researchers in the UK have developed Molecular Crystal Representation from Transformers (MCRT) —an AI model likened to ChatGPT for its transformative impact. Dubbed "CrystalGPT" , this foundation model rapidly predicts the physical properties of molecular crystals, revolutionizing how chemists design materials in silico . The AI Revolution in Crystal Design Understanding and predicting the properties of molecular crystals is critical for designing new materials for applications ranging from pharmaceuticals to energy storage. Traditional computational approaches, though powerful, are often resource-intensive and slow. Machine learning methods such as Smooth Overlap of Atomic Positions (SOAP) and atom-centred symmetry functions (ACSFs) have offered improvements, but they struggle with large datasets and subtle structural nuances. CrystalGPT, developed by a team led by Andy Cooper at the University of Liverpool, o...
Read more