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Optimized Slurry Processing Could Unlock Better Lithium-Ion Batteries

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Optimized Slurry Processing Could Unlock Better Lithium-Ion Batteries
Optimized Slurry Processing Could Unlock Better Lithium-Ion Batteries

Researchers at Tokyo University of Science have developed a new method that could significantly improve the manufacturing of lithium-ion batteries by optimizing how electrode slurries are processed during production.

The study focuses on the preparation of battery electrode slurries — mixtures containing active materials, conductive additives, binders, and solvents — which play a critical role in determining battery conductivity, stability, and long-term performance. Scientists explained that conventional analysis methods struggle to capture what happens inside these slurries during real industrial coating processes where strong shear forces are involved.

To address this challenge, the Japanese research team applied rheo-impedance spectroscopy, a technique combining controlled shear deformation with electrochemical impedance spectroscopy (EIS). This approach allowed them to observe how conductive networks evolve inside battery slurries under manufacturing-like conditions.

The experiments were conducted on lithium iron phosphate (LiFePO4) cathode slurries commonly used in lithium-ion batteries. Researchers tested multiple shear rates to reproduce different coating speeds used in industrial production lines.

Their findings revealed that processing conditions have a direct impact on battery efficiency. Low shear rates caused conductive particles to remain clustered, reducing electrical connectivity, while excessively high shear rates fragmented the conductive network. According to the study, an intermediate shear rate of around 50 s-1 created the best balance, producing a uniform conductive structure that improved charge-discharge performance and cycle stability.

Associate Professor Isao Shitanda stated that the method can help manufacturers identify optimal coating conditions before assembling battery cells, reducing trial-and-error processes and minimizing material waste.

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