In silico study of the solubility of fullerene derivatives in chlorobenzene

IF 4.2 Q2 CHEMISTRY, MULTIDISCIPLINARY

Results in Chemistry Pub Date : 2025-09-29 DOI:10.1016/j.rechem.2025.102754

Melek Türker Saçan , Gulcin Tugcu , Natalja Fjodorova , Katja Venko , Durbek Usmanov , Bakhtiyor Rasulev , Safiye Sağ Erdem , Marjana Novič

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Abstract

Since the seminal discovery of fullerene in 1985, fullerene C60 and its derivatives have emerged as versatile nanostructured materials with applications spanning optics, electronics, cosmetics, and biomedicine. The synthesis of various fullerene derivatives (FDs) involves the addition of functional groups to C60. Primary production costs are intricately tied to the extraction and purification of C60, given its limited solubility in both polar and many organic solvents. A critical physicochemical characteristic defining FDs' industrial utility is their solubility, a factor integral to substance distribution in the environment.

The application of fullerenes, particularly in renewable energy, has driven extensive Quantitative Structure-Property Relationship (QSPR) research into their performance-related properties. This study assesses FD solubility in chlorobenzene. Solubility in organic solvents is essential, as it is linked to bioaccumulation. We have generated linear and nonlinear machine learning-based QSPR models, validated for their robustness and accuracy. These models are based on experimental solubility data obtained for a set of FDs in chlorobenzene. Subsequently, these models were deployed to predict the solubility of a novel set comprising 117 FDs. The examined dataset encompasses diverse groups attached to the C60- and C70-core structures, with side chains strategically linked to the cyclopropane group.

The insights drawn from this study bear significance in understanding how FDs with varying solubility levels can be assessed for distinct applications. By exclusively relying on developed “in silico” models and circumventing extensive testing, these QSPR models offer early evaluative tools, shedding light on FDs with optimal solubility for specific application domains. The outcomes of this research hold promise for advancing our comprehension of fullerene derivatives and streamlining their application in diverse industrial sectors.

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富勒烯衍生物在氯苯中的溶解度的硅研究

自1985年富勒烯的开创性发现以来，富勒烯C60及其衍生物已成为多功能纳米结构材料，应用于光学、电子、化妆品和生物医学等领域。各种富勒烯衍生物（FDs）的合成涉及到在C60上添加官能团。考虑到C60在极性溶剂和许多有机溶剂中的溶解度有限，初级生产成本与C60的提取和纯化密切相关。确定FDs工业用途的一个关键物理化学特性是它们的溶解度，这是物质在环境中分布的一个重要因素。富勒烯的应用，特别是在可再生能源领域的应用，推动了对其性能相关性质的定量构性关系（QSPR）研究。本研究评估FD在氯苯中的溶解度。溶解度在有机溶剂中是必不可少的，因为它与生物积累有关。我们已经生成了基于线性和非线性机器学习的QSPR模型，并验证了它们的鲁棒性和准确性。这些模型是基于一组FDs在氯苯中的溶解度的实验数据。随后，这些模型被用于预测包含117个fd的新集合的溶解度。研究的数据集包括附着在C60和c70核心结构上的不同基团，侧链与环丙烷基团有战略联系。从这项研究中得出的见解对于理解如何评估具有不同溶解度水平的fd用于不同的应用具有重要意义。这些QSPR模型完全依赖于已开发的“硅片”模型，并规避了广泛的测试，提供了早期评估工具，阐明了特定应用领域中具有最佳溶解度的fd。本研究的结果有望促进我们对富勒烯衍生物的理解，并简化其在不同工业部门的应用。

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