Breaking Barriers in Hydrogen Storage: A Low-Temperature Hydrogen Battery for a Clean Energy Future

Low-temperature hydrogen battery breakthrough

Hydrogen has long been hailed as a cornerstone of the clean energy transition, yet its widespread adoption has been slowed by a critical bottleneck: storage. Traditional methods require either ultra-cold temperatures (–252.8 °C) or high pressures (350–700 bar), making hydrogen storage costly and logistically complex. Now, researchers at the Institute of Science Tokyo have developed a breakthrough solution: a low-temperature hydrogen battery that safely stores and releases hydrogen at just 90 °C.

Published in Science, the team led by Dr. Takashi Hirose, Assistant Professor Naoki Matsui, and Professor Ryoji Kanno unveiled a device that integrates magnesium hydride (MgH2) as the anode and hydrogen gas as the cathode. The innovation lies in the use of a new solid electrolyte, Ba0.5Ca0.35Na0.15H1.85, which efficiently transports hydride ions (H–), enabling reversible hydrogen absorption and release at dramatically reduced temperatures.

How the Hydrogen Battery Works

The design of the hydrogen battery mimics conventional electrochemical systems but with a key difference: instead of electrons alone, hydrogen is also shuttled. During charging, MgH2 releases H– ions, which move through the electrolyte and are oxidized to produce hydrogen gas. During discharge, the reverse happens: H2 gas is reduced at the cathode and converted back into MgH2 at the anode. This cycle provides both high capacity and reversibility.

In tests, the battery achieved the full theoretical storage capacity of MgH2, about 2,030 mAh g–1 or 7.6 wt.% H2, repeatedly and reliably. This is a substantial improvement over prior solid-state hydrogen storage systems that either required extreme heat (300–400 °C) or suffered from poor efficiency and reversibility with liquid electrolytes.

Why This Matters for the Hydrogen Economy

The hydrogen economy depends on safe, compact, and affordable storage solutions. Solid-state hydrogen batteries such as this one could enable next-generation hydrogen-powered vehicles, grid-scale energy storage, and industrial applications without the infrastructure burden of cryogenics or extreme pressures. Crucially, the new battery operates at temperatures low enough to integrate with existing energy systems while maintaining safety and efficiency.

“This development offers a foundation for efficient hydrogen storage systems suitable for use as energy carriers,” explained Hirose. By enabling hydrogen to be stored and released under practical conditions, the new approach addresses one of the most formidable barriers in clean energy technology.

Looking Ahead: Toward Carbon-Free Energy Systems

Future research will likely focus on scaling the technology, improving cycle life, and integrating these batteries into real-world energy systems. If successful, low-temperature hydrogen batteries could complement fuel cells, renewable energy sources, and even heavy-industry applications—driving forward a truly carbon-free economy.

By making hydrogen storage safer, denser, and more efficient, this breakthrough could help accelerate the global shift toward sustainable energy systems and make hydrogen a practical energy carrier for everyday use.

Original article: https://techxplore.com/news/2025-09-barriers-hydrogen-storage-temperature-battery.html

This blog post was prepared with the assistance of AI technologies for 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

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