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A Density Functional Theory‐Genetic Algorithm Approach for the Structural Elucidation of Linear Diamantane Polyesters and their Nonlinear Optical Properties

We report a series of diamantane polyesters synthesized as head‐to‐head and side‐to‐side linear chain polymers. The incorporation of the bulky and rigid diamantyl moiety significantly enhances the thermal stabilities of… Click to show full abstract

We report a series of diamantane polyesters synthesized as head‐to‐head and side‐to‐side linear chain polymers. The incorporation of the bulky and rigid diamantyl moiety significantly enhances the thermal stabilities of these polymers compared to well‐established linear aliphatic polyesters, such as poly(ε‐caprolactone), poly(lactic acid), poly(butylene succinate), and poly(glycolic acid). This enhancement is confirmed by thermogravimetric analyzes, which show degradation onsets in the range of 520 to 580 °C. The structural characterization of these polyesters posed significant challenges due to virtual insolubility in organic solvents, limiting the use of techniques such as mass spectrometry and liquid‐state nuclear magnetic resonance (NMR) spectroscopy. To address these challenges, a genetic algorithm for structure prediction based on periodic density functional theory computations was employed, yielding a structure that closely matches the powder X‐ray diffraction data. Heavily constrained Rietveld refinement provided excellent agreement with the experimental results. These findings, combined with solid‐state NMR data, have been integrated to provide a comprehensive understanding of the polyester properties. These polyesters demonstrate promising nonlinear optical responses, including white‐light emission upon continuous infrared laser irradiation, making them candidates for optoelectronic applications.

Keywords: diamantane polyesters; nonlinear optical; genetic algorithm; functional theory; density functional

Journal Title: European Journal of Organic Chemistry
Year Published: 2025

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