Lawrence Berkeley National Laboratory
Assembly Line
Machine learning-accelerated discovery of heat-resistant polysulfates for electrostatic energy storage
The development of heat-resistant dielectric polymers that withstand intense electric fields at high temperatures is critical for electrification. Balancing thermal stability and electrical insulation, however, is exceptionally challenging as these properties are often inversely correlated. A traditional intuition-driven polymer design approach results in a slow discovery loop that limits breakthroughs. Here we present a machine learning-driven strategy to rapidly identify high-performance, heat-resistant polymers. A trustworthy feed-forward neural network is trained to predict key proxy parameters and down select polymer candidates from a library of nearly 50,000 polysulfates. The highly efficient and modular sulfur fluoride exchange click chemistry enables successful synthesis and validation of selected candidates. A polysulfate featuring a 9,9-di(naphthalene)-fluorene repeat unit exhibits excellent thermal resilience and achieves ultrahigh discharged energy density with over 90% efficiency at 200 °C. Its exceptional cycling stability underscores its promise for applications in demanding electrified environments.
Highly efficient Co-added Ni/CeO2 catalyst for co-production of hydrogen and carbon nanotubes by methane decomposition
The catalytic decomposition of methane (CDM) is a hydrogen and nanostructured carbon production process with minimal CO2 emission. Among the transition metal-based catalysts (e.g. Ni, Fe, Co, etc.), Ni-based catalysts are most widely studied due to the higher catalytic activity in decomposing methane. However, the limited lifespan of the catalyst makes it unsuitable for practical applications. Effective methane decomposition catalysts should be designed to optimize both reaction efficiency and catalyst lifetime. A Ni/CeO2 catalyst, developed in previous studies, Co was added to promote low-temperature (< 700 °C) activity manipulating the redox property of Co. Among the prepared catalysts with varying Ni:Co ratio, the methane conversion rate of the Ni8Co2/CeO2 catalyst was approximately twice that of the Ni10/CeO2 catalyst, confirming its excellent low-temperature activity. The reaction rate of Ni8Co2/CeO2 catalyst was 4.38 mmol/min∙gcat at 600 °C with WHSV of 36 L/gcat∙h. In terms of characteristics of carbon products, Raman spectroscopy analysis revealed that the carbon grown on the catalyst surface exhibited high crystallinity, with D-G band ratio (ID/IG) of 1.01. The fresh and used catalyst samples were characterized by TEM, XPS, XAS, and other methods to analyze the parameters affecting catalytic activity.
NREL To Lead New Lab Consortium To Enable High-Volume Manufacturing of Electrolyzers and Fuel Cells
The Roll-to-Roll (R2R) Consortium is a new national laboratory consortium with a mission to advance efficient, high-throughput, and high-quality manufacturing methods and processes to accelerate domestic manufacturing and reduce the cost of durable, high-performance proton exchange membrane fuel cell and electrolyzer systems.
The R2R Consortium is led by the National Renewable Energy Laboratory (NREL) and includes Argonne National Laboratory (ANL), Oak Ridge National Laboratory (ORNL), Lawrence Berkeley National Laboratory, and Sandia National Laboratories.
High-throughput manufacturing of fuel cells and water electrolyzers is critical for achieving widespread deployment of low-cost, clean hydrogen technologies. Roll-to-roll manufacturing of materials can increase efficiency, reduce material waste, and improve cost, but there are challenges related to materials synthesis, coating, drying, and quality control that need to be addressed to scale up these processes for industry adoption.