Mapping the Secret World of Platinum Atoms: A Breakthrough in Single-Atom Catalysts

ETH Zurich Single Atom Catalyst

In a groundbreaking study, scientists at ETH Zurich have peered into the hidden world of single-atom catalysts, uncovering how individual platinum atoms interact with their surroundings on an atomic scale. Using advanced nuclear magnetic resonance (NMR)—a technique akin to MRI scans—they have mapped the atomic neighborhoods of platinum atoms, paving the way for greener and more efficient chemical production.

The Power and Challenge of Catalysis

Catalysis—the acceleration of chemical reactions using special substances—lies at the heart of modern industry. From fuels to pharmaceuticals, nearly 80% of all chemical products rely on catalysts. Platinum, one of the most effective catalysts, enables these reactions but comes with serious downsides: it is rare, expensive, and energy-intensive to produce. Scientists have long sought ways to maximize platinum’s catalytic efficiency while minimizing its use and environmental impact.

Precision Engineering with Single Atoms

Enter single-atom catalysts (SACs), where individual platinum atoms are dispersed across a porous surface like nitrogen-doped carbon. These single atoms can act as highly efficient reaction sites—if their local atomic environments are understood and optimized.

The ETH Zurich team, led by Javier Pérez-Ramírez and Christophe Copéret, discovered that each platinum atom sits in a slightly unique atomic environment, affecting its catalytic behavior. Using NMR, they could map these surroundings in unprecedented detail, revealing subtle but crucial variations invisible to electron microscopes.

A Collaborative Innovation

This scientific breakthrough emerged from interdisciplinary collaboration and serendipity. An impromptu meeting between researchers during the NCCR Catalysis program sparked the idea of applying NMR to SACs. The subsequent partnership with computational experts from Aarhus University enabled the development of custom simulation tools to interpret complex atomic data.

Implications for Sustainable Chemistry

The ability to map atomic neighborhoods sets a new benchmark for designing next-generation catalysts. By tailoring the atomic environments of platinum atoms, researchers can develop catalysts that are not only more effective but also use less platinum, thereby reducing costs and environmental impact. This has far-reaching implications for industries such as:

  • Energy: More efficient fuel cells and hydrogen production.
  • Environmental technologies: Cleaner automotive exhaust systems.
  • Pharmaceuticals: Greener, more precise drug synthesis.

As Pérez-Ramírez notes, “This analytical method sets a new benchmark in the field.” The team’s work, recently published in Nature, underscores the critical role of advanced characterization techniques in advancing sustainable chemistry.

Learn more about the original study here: Scientists Mapped the Secret World of Platinum Atoms – and It Changes Everything.

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

#MaterialsScience #SingleAtomCatalysts #SustainableChemistry #PlatinumAtoms #ETHZurich #Catalysis #QuantumServerNetworks

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