Revolutionizing Solar Energy Storage With Photoswitches

A groundbreaking study has identified molecular photoswitches that can improve solar energy storage. Using quantum computing, researchers analyzed a large database to find molecules best suited for this technology, marking a significant step in emission-free solar energy utilization. Credit: SciTechDaily.com

Optimizing molecular photoswitches for solar energy harvesting.

Molecular photoswitches that can both convert and store energy could be used to make solar energy harvesting more efficient. A team of researchers has used a quantum computing method to find a particularly efficient molecular structure for this purpose. As the team described in the journal Angewandte Chemie, their procedure was based on a dataset of more than 400,000 molecules, which they screened to find the optimum molecular structure for solar energy storage materials.

The MOST Project: A New Solar Energy Pathway

At present, solar energy is either used directly to generate electricity, or indirectly via the energy stored in heat reservoirs. A third route could involve first storing the energy from the sun in light-sensitive materials and then releasing it as needed. The EU-backed project MOST (“Molecular Solar Thermal Energy Storage”) is exploring molecules such as photoswitches that can absorb and store solar energy at room temperature to create entirely emission-free utilization of solar energy a reality.

The research teams of Kurt V. Mikkelsen at the University of Copenhagen, (Denmark) and Kasper Moth–Poulsen at the Technical University of Catalonia, Barcelona (Spain), have taken a closer look at the photoswitches best suited for this task. They studied molecules known as bicyclic dienes, which switch to a high-energy state when illuminated. The most prominent example of this bicyclic diene system is known as norbornadiene quadricyclane, but a vast number of similar candidates exist. The researchers explain: “The resulting chemical space consists of approximately 466,000 bicyclic dienes that we have screened for their potential applicability in MOST technology.”

Innovative Screening Method and Promising Findings

Screening a database of this size is typically done by machine learning, but this requires large amounts of training data based on real-world experiments, which the team did not have. Using a previously developed algorithm and a novel evaluation score, “eta,” the screening and evaluation of the database molecules yielded a clear result: all six of the top scoring molecules differed from the original norbornadiene quadricyclane system at a crucial point in the structure. The researchers concluded that this structural change, an expansion of the molecular bridge between the two carbon rings in the bicyclic part, allowed the new molecules to store more energy than the original norbornadiene.

The researchers’ work demonstrates the potential for optimizing solar energy storage molecules. However, the new molecules must first be synthesized and tested under real conditions. “Even though the systems can be synthetically prepared, there is no guarantee that they are soluble in relevant solvents and that they will actually photoswitch in high yield or at all, as we have assumed in eta,” the authors caution.

Impact and Future Potential

Despite this, the team has developed a new, large set of training data for machine learning algorithms and has thus shortened the arduous research step prior to synthesis for chemists tackling such systems in the future. The authors envision this much larger repository of bicyclic dienes coming into its own for research into photoswitches for a variety of applications, potentially making it easier for molecules to be tailored to specific requirements.

Reference: “Searching the Chemical Space of Bicyclic Dienes for Molecular Solar Thermal Energy Storage Candidates” by Andreas Erbs Hillers-Bendtsen, Jacob Lynge Elholm, Oscar Berlin Obel, Helen Hölzel, Kasper Moth-Poulsen and Kurt V. Mikkelsen, 25 July 2023, Angewandte Chemie International Edition.
DOI: 10.1002/anie.202309543




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