TL;DR:
- Researchers at Oak Ridge National Laboratory used machine learning to develop a carbonaceous supercapacitor material with four times the energy storage capacity of the best commercial material.
- The new material has applications in improving regenerative brakes, power electronics, and auxiliary power supplies.
- Machine learning guided the design of the superlative material, achieving an impressive capacitance of 611 farads per gram.
- The success came swiftly, with the data-driven approach reducing development time from at least a year to three months.
- The key to the breakthrough lies in a porous structure combining mesopores and micropores, resembling a golf ball with deep dimples.
- This research has the potential to advance carbon materials for supercapacitor applications further.
Main AI News:
In a significant stride towards advancing energy storage technology, researchers at the Department of Energy’s Oak Ridge National Laboratory (ORNL) have developed a groundbreaking carbonaceous supercapacitor material guided by machine learning. This innovative material boasts an impressive capability, storing four times more energy than the leading commercial counterpart. This achievement opens the door to enhanced energy storage solutions, with potential applications spanning from improving regenerative brakes to enhancing power electronics and auxiliary power supplies.
Lead chemist Tao Wang, affiliated with both ORNL and the University of Tennessee, Knoxville, expressed the significance of their work, stating, “By combining a data-driven method and our research experience, we created a carbon material with enhanced physicochemical and electrochemical properties that pushed the boundary of energy storage for carbon supercapacitors to the next level.”
The study, published in Nature Communications, was a collaborative effort between Wang and chemist Sheng Dai of ORNL and UTK. Dai, who conceptualized and designed the experiments alongside Wang, emphasized the monumental nature of their achievement, proclaiming, “This is the highest recorded storage capacitance for porous carbon. This is a real milestone.”
Their research was conducted at the Fluid Interface Reactions, Structures, and Transport Center (FIRST), an ORNL-led DOE Energy Frontier Research Center that operated from 2009 to 2022. FIRST, in collaboration with three national labs and seven universities, focused on exploring fluid-solid interface reactions with implications for capacitive electrical energy storage. Capacitance, which pertains to the ability to collect and store electrical charge, played a central role in their investigation.
While batteries are the most well-known energy storage devices, converting chemical energy into electrical energy efficiently, capacitors offer an alternative approach. Capacitors store energy in the form of an electric field, similar to static electricity. While they may not match batteries in energy storage volume, capacitors possess the advantage of rapid recharge and the ability to retain their charge over time. Supercapacitors, a specialized type of capacitor found in applications like electric buses, can store more charge than conventional capacitors and have quick charge and discharge capabilities.
Commercial supercapacitors typically consist of two electrodes—an anode and a cathode—separated and immersed in an electrolyte. Porous carbons are the preferred materials for these electrodes, offering a substantial surface area for storing electrostatic charge.
The ORNL-led study leveraged machine learning, a form of artificial intelligence that learns from data to optimize outcomes, to guide their quest for exceptional supercapacitor material. Researchers Runtong Pan, Musen Zhou, and Jianzhong Wu from the University of California, Riverside, built an artificial neural network model trained to achieve a specific goal: the development of an ideal “dream material” for energy delivery.
The model’s prediction indicated that a carbon electrode co-doped with oxygen and nitrogen could achieve the highest capacitance at 570 farads per gram. Wang and Dai then designed an exceptionally porous doped carbon with a substantial surface area for interfacial electrochemical reactions. Wang synthesized this novel material—a carbon framework rich in oxygen—optimized for charge storage and transport.
To enhance the material’s properties, it underwent activation to generate additional pores and introduce functional chemical groups at sites conducive to oxidation or reduction reactions. Traditional activation agents, such as potassium hydroxide, require extremely high temperatures, approximately 800 degrees Celsius, which expels oxygen from the material. Five years ago, Dai developed an alternative process utilizing sodium amide as the activation agent, which operates at a lower temperature of around 600 degrees Celsius, creating more active sites while preserving functional groups. Dai explained, “Material synthesis in this ‘Goldilocks zone’—not too cold, not too hot—made a real difference in not decomposing the functional groups.”
The synthesized material exhibited a remarkable capacitance of 611 farads per gram, an astonishing fourfold improvement over typical commercial materials. Pseudocapacitance, arising from continuous, fast, and reversible oxidation-reduction reactions at the electrode surface, contributed 25% to the overall capacitance. Notably, the material’s surface area exceeded 4,000 square meters per gram, setting a new high-water mark for carbonaceous materials.
What makes this achievement even more remarkable is the speed at which it was realized. The data-driven approach enabled Wang and Dai to achieve in three months what would have traditionally taken at least a year of trial and error. Wang emphasized, “We achieved the performance of carbon materials at the limit. Without the goal that machine learning set, we would have kept optimizing materials through trial and error without knowing their limit.“
The key to their success lay in the creation of two types of pores—mesopores ranging from 2 to 50 nanometers and micropores smaller than 2 nanometers. Experimental analyses revealed that this combination provided not only a high surface area for energy storage but also channels for the transport of electrolytes. Scanning transmission electron microscopy conducted by Miaofang Chi and Zhennan Huang at the Center for Nanophase Materials Sciences, a DOE Office of Science user facility at ORNL, characterized the mesopores. However, the micropores were too minute to visualize, resulting in a microscopic appearance resembling a golf ball with deep dimples, where the dimples represented mesopores and micropores resided in between.
Dai elaborated on the significance of this porous structure, stating, “You are building a highway for ion transport. Supercapacitors are all about high-rate performance—fast charging, fast discharging. In this structure that Tao and I designed, you have a larger pore, which you can view as a superhighway. This is connected to smaller roads or tinier pores.” Wang added, “The smaller pores provide a larger surface for storing charge, but the larger pores are like a highway that can speed up the charge/discharge rate performance. A balanced amount of small and large pores can realize the best performance, as predicted by the artificial neural network model.”
To gain insights into the transport of electrolytes within the carbon pores, Murillo Martins and Eugene Mamontov of the Spallation Neutron Source, a DOE Office of Science user facility at ORNL, employed quasielastic neutron scattering. This innovative approach allowed them to track the speed of electrolyte movement in different types of pores—quickly in the mesopores and more slowly in the micropores.
Wang further quantified the capacitance contributions from pores of various sizes and oxidation-reduction reactions at their surfaces using modified step potential electrochemical spectroscopy, a specialized technique available only in a few locations worldwide. He observed that mesopores doped with oxygen and nitrogen made the most significant contribution to the overall capacitance.
This pioneering research has the potential to expedite the development and optimization of carbon materials for supercapacitor applications. While this study has already harnessed the best available data, the door is now open for future research to leverage even more extensive datasets to advance the boundaries of carbon supercapacitors further. Wang concluded, “Using more data, we can set a new target and push the boundaries of carbon supercapacitors even further. The successful application of machine learning in materials design is a testament to the power of data-driven approaches in advancing technology.“
Conclusion:
This groundbreaking development in supercapacitor technology not only represents a significant leap in energy storage capacity but also showcases the power of data-driven approaches in materials design. With applications ranging from improved vehicle braking systems to enhanced power electronics, this innovation has the potential to drive substantial market growth in the energy storage sector, offering more efficient and versatile solutions for a wide range of industries.
