| Feb 18, 2025 |
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(Nanowerk Spotlight) Computational calculations are revolutionizing modern scientific research, offering a powerful means to predict the potential applications of new materials. Unlike traditional experimental methods, which require the physical synthesis of materials, computational techniques create virtual models to analyze performance without the need for tangible prototypes.
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Despite their transformative potential, these methods are not without challenges. Discrepancies often arise between computational predictions and experimental results, and even different computational approaches can yield conflicting outcomes for the same material, leaving some aspects of these calculations clouded in mystery.
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A recent breakthrough by Prof. Zhongfang Chen’s team at the University of Puerto Rico, Rio Piedras, sheds light on one such mystery. Their findings suggest that discrepancies may stem from neglecting weak interactions during the initial stages of material modeling, especially the lone pair-π, and hydrogen-π interactions. Using catalysts for the CO2 reduction reaction as a case study – where CO2 is converted into valuable fuels like CO under applied voltage – the researchers discovered that variations in the initial structural models could lead to significant energy differences and, consequently, altered predictions of material performance.
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This research has been published in ChemsusChem (“The Significance of the ‘Insignificant’: Non-covalent Interactions in CO2 Reduction Reactions with 3C-TM (TM = Sc-Zn) Single-Atom Catalysts”).
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“In the real world, weak interactions shape the structure of materials, guiding them to naturally adopt the most stable configurations, such as the double helix of DNA or the folding of proteins,” explains Linguo Lu, the first author of this work and a PhD student in Chen’s group. “In computational calculations, however, the results are heavily influenced by the researcher’s input. An inaccurate initial structural model can lead to predictions that diverge significantly from reality.”
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By studying CO2 adsorption—the first step in the reaction process—the team demonstrated that ignoring lone pair-π and hydrogen-π interactions could lead to energy errors up to 4 times the chemical accuracy standard. These errors magnify to as much as 10 times during subsequent reaction steps, underscoring the importance of precise modeling in computational predictions.
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Beyond CO2 reduction, the study also addresses the hydrogen evolution reaction (HER), a side reaction that competes with CO2 reduction and can compromise the purity of the desired products.
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“Different reactions are driven by distinct forces, which help overcome barriers and determine the final product,” says Linguo Lu. “However, previous computations often ignored these driving forces, directly comparing reactions with their side reactions. This oversight can lead to misleading results.”
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To address this issue, the researchers introduced a driving force analysis, which accounts for the influence of reaction forces on HER relative to CO2 reduction. Their findings reveal that Mn, Fe, Co, and Zn-based single-atom catalysts face strong competition from HER, making them less suitable for producing pure CO2-derived products. Instead, the study recommends other 3D transition metal single-atom catalysts as more promising candidates for high-purity Carbon-based production.
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“Our research brings attention to the often-overlooked impact of weak noncovalent interactions. These interactions, although subtle, significantly influence the energy profiles of CO2RR intermediates, fine-tuning the reactivity and selectivity of the catalytic process”, Professor Chen said, “By incorporating these effects into our models, we bridge a gap in current CO2RR catalyst design and provide a more accurate framework for predicting catalytic behavior and competitive side reactions such as the hydrogen evolution reaction (HER).”
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Source: Provided by the University of Puerto Rico
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