The Future of Aqueous Batteries: Harnessing Hydrogen Bonds for High Performance

By Quantum Server Networks | July 2025

Aqueous Batteries and Hydrogen Bonds

Aqueous batteries have long been hailed as safer alternatives to lithium-ion systems thanks to their water-based electrolytes. However, their adoption has been limited by lower energy densities—until now. A breakthrough study led by Prof. Pan Feng at Peking University reveals how hydrogen-bond network engineering can unlock faster charging, higher capacities, and safer batteries for a sustainable energy future.

Protons: The Ideal Charge Carrier

Unlike lithium (Li⁺) or sodium (Na⁺), protons (H⁺) are light and highly mobile. They move through materials not by simple diffusion but via a Grotthuss-type mechanism, hopping across hydrogen bonds at lightning speed. This ultra-fast “diffusion-free” transport makes protons promising candidates for next-generation aqueous batteries.

Published in Matter, the study titled "Proton storage and transfer in aqueous batteries" delves into how protons interact with hydrogen bonds, revealing a roadmap for safer and more efficient energy storage systems.

Three Strategies for High-Performance Aqueous Batteries

The team proposes a unified framework for optimizing aqueous battery performance:

  • Electrode Design: Embedding hydrogen-bond networks in solid-state materials to create efficient proton pathways.
  • Electrolyte Tuning: Adjusting acid concentrations and anion types to stabilize proton conductivity.
  • Interface Engineering: Modifying electrode surfaces with hydroxyl (–OH) and carboxyl (–COOH) groups to enhance charge transfer and reaction kinetics.

"Our findings lay the foundation for aqueous batteries that combine the safety of water-based systems with performance levels comparable to or exceeding lithium-ion technology," says Prof. Pan Feng.

Applications and Future Outlook

With hydrogen-bond engineering, aqueous batteries could power everything from grid-scale energy storage to portable electronics and electric vehicles. By enhancing energy density, charging speed, and lifespan, this approach brings us closer to a carbon-neutral future.

Read the original article on Tech Xplore

Explore Further

Learn more about this breakthrough in Matter.

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

#AqueousBatteries #HydrogenBonds #EnergyStorage #RenewableEnergy #GreenTech #MaterialsScience #PWmat #QuantumInnovation

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