Oak Ridge Lab Boosts Battery Performance with Carbon Fiber

Researchers at Oak Ridge National Laboratory have developed several technologies in recent weeks. These include a method utilising carbon fibre to enhance the performance of dry-processed lithium-ion batteries, a machine learning approach to accurately model the properties of molten salts relevant to nuclear energy, and a tool – FLAT – employing segmentation algorithms and machine learning to assess the level of concrete foundations. Furthermore, scientists at ORNL have achieved up to a 75% reduction in porosity within large-scale 3D-printed polymer parts via vacuum-assisted extrusion, and, in collaboration with the University of Colorado Boulder, identified that partial gene suppression in photosynthetic bacteria can significantly modify their stress response.

Recent research at Oak Ridge National Laboratory has addressed manufacturing challenges in lithium-ion battery production. Scientists have successfully demonstrated a method utilising a dry process – an alternative to conventional wet chemical methods – for battery manufacture, resulting in improved electricity flow and a diminished risk of thermal runaway. This development is significant as it offers a pathway to more affordable battery production for electric vehicles and portable electronic devices. The research focuses on enhancing performance through process optimisation rather than novel material composition, representing a pragmatic approach to cost reduction and scalability.

Furthermore, investigations into materials science extend to reducing reliance on critical minerals within battery components. Licensed technology from ORNL aims to minimise dependence on rare earth elements, addressing supply chain vulnerabilities and promoting sustainability within the energy storage sector. While not directly related to battery chemistry, this work complements advancements in battery manufacturing by addressing broader material sourcing concerns. Complementary to these efforts, computational modelling has also progressed, with a new machine learning approach achieving accurate predictions of properties within molten salts – materials with applications in advanced nuclear energy systems, potentially informing future battery electrolyte designs. This ‘molten salt modelling’ represents a significant step in the computational materials science field, enabling more efficient exploration of novel materials.