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Science and Technology Partnerships and Outreach

Emerging Manufacturing Technologies for Next-Generation Battery Materials

The Opportunity

Lithium-ion batteries power many of the technologies and products we use every day – including cell phones, laptops and electric vehicles – but they come with limitations and challenges in key areas such as energy density, safety and cost.

When compared with lithium-ion batteries, solid-state batteries offer promise of higher energy density and improved safety, all at a lower cost. But because of limitations related to the processing and scale-up of materials in those batteries, no one is currently able to mass produce solid-state batteries at a cost that is comparable or less expensive than that of lithium-ion batteries.

As a result, the significant potential of solid-state batteries has gone unrealized.

Argonne’s Approach

The U.S. Department of Energy’s Argonne National Laboratory is developing processes and technologies that would enable solid-state batteries to be produced at a cost that is comparable or less than that of lithium-ion batteries.

Among those innovations are scalable synthesis technologies and novel sintering strategies that can address some of the greatest challenges that limit materials for solid-state batteries.

This work is made possible by Argonne’s unique combination of facilities, including:

  • Materials Engineering Research Facility (MERF): Research at the MERF bridges the gap between small-scale laboratory research and high-volume manufacturing. Researchers apply various techniques for the scale-up of newly invented materials. The facility produces kilogram quantities of materials and makes samples available for industrial evaluation, prototyping, and to support further research. The MERF develops economically viable processes for materials manufacturing at scale and produces detailed process descriptions for accurate cost (techno-economic) modeling.
  • Advanced Photon Source (APS): The APS provides ultra-bright x-ray beams for research in almost all scientific disciplines, and it enables x-ray tomography, which is critical to the development of next generation batteries. In-operando tomography studies of solid electrolytes gives Argonne researchers insight into the densification of solid state battery materials and enables quantification of porosity as a function of sintering temperature.
  • Center for Nanoscale Materials (CNM): The CNM is home to expertise, instrumentation and infrastructure for nanoscience and nanotechnology research. High-resolution transmission electron microscopy (HR-TEM) allows scientists to observe the structure of materials, at the atomic level, and how it is influenced by synthesis conditions. In particular, understanding the interfacial structure in solid state batteries enables these interfaces to be better engineered and thereby produce batteries with enhanced lifetime and faster charging.

    Understanding how parameters influence material characteristics and performance using advanced characterization is a critical step towards intelligent engineering of new technologies that reduces financial risk and can therefore expedite their transition to industry.

The Benefits

Argonne’s innovative techniques and technologies offer advantages in the materials engineering process such as:

  • Faster processing
  • Pressureless sintering
  • Large-scale uniformity
  • Higher densification

These in turn could enable solid-state batteries to be produced at a cost that is comparable or less than that of lithium-ion batteries. When compared with lithium-ion batteries, solid-state batteries offer:

  • Higher energy density
  • Improved safety
  • Lower cost

Case Study

Operando sintering of LLZO solid state electrolyte

Argonne researchers sought to understand how a solid-state electrolyte is formed from a precursor synthesized using Flame Spray Pyrolysis (FSP).

The research team gained critical insights by:

  • Monitoring phase changes during heating using synchrotron X-rays to generate high-resolution data
  • Simulating grain ripening using a computational model