Low Temperature Synthesis of Carbonaceous Electrodes Through Laser-Reduction for Electrochemical Applications

Technology

This technology describes a novel method that enables the efficient laser-reduction of novel electrode materials, including PAN. First, the polymer is heat treated to prevent its destruction during lasing. Next, the polymer is micro structured (i.e. etched) to minimize damage from thermal stresses during lasing. An added advantage of laser-reduction is that the morphology, and therefore the properties, of the electrode may be tuned by varying the manufacturing conditions. This technology further includes the integration of machine-learning to automate the manufacturing process. Using Bayesian optimization, 20 candidate lasing conditions may be tested. Finally, the properties of the output materials are measured, and the results are processed to modify conditions for a subsequent lasing test sheet. This process is repeated for multiple cycles until the properties of the material have been optimized.

Problem Addressed

The performance and properties of carbon electrodes vary depending on the manufacturing method. Their synthesis typically requires an energy intensive carbonization step. Laser-reduction is an emerging, alternative manufacturing method that offers improved energy efficiency. However, certain materials like polyacrylonitrile (PAN) pose challenges in terms of their compatibility with the laser process. Furthermore, optimization of the lasing conditions to improve the properties of the resulting electrodes is a slow and time-consuming process. Methods that enable laser-reduction of cheap, commercially available polymers, such as PAN, and accelerate the optimization of the manufacturing conditions are necessary to reduce production costs. 

To address these challenges, this technology includes modifications to the laser-reduction conditions and pretreatment protocols to prepare these difficult materials for laser-reduction. Integration of a Bayesian optimization algorithm, which adjusts the manufacturing conditions in an iterative manner to achieve optimal properties based on the performance of previous products, further reduces development costs.

Advantages

• More energy efficient due to omitting a high-temperature carbonization step. 

• Utilizes commercially available polymers that were previously thought to be incompatible with laser-reduction. 

• Machine-learning automates optimization of the electrode properties during manufacture. 

Publications

Patil, Jatin J., et al. "Bayesian-Optimization-Assisted Laser Reduction of Poly (acrylonitrile) for Electrochemical Applications." ACS nano 17.5 (2023): 4999-5013.