Alopaeus Marie, Stosiek Matthias, Diment Daryna, Löfgren Joakim, Cho MiJung, Hemming Jarl, Tirri Teija, Pranovich Andrey, Eklund Patrik C, Rigo Davide, Balakshin Mikhail, Xu Chunlin, Rinke Patrick
Laboratory of Natural Materials Technology, Åbo Akademi University, Henrikinkatu 2, 20500, Turku, Finland.
Department of Physics, Technical University Munich, James-Franck-Str. 1, 85748, Garching, Germany.
Sci Data. 2025 Jun 13;12(1):996. doi: 10.1038/s41597-025-05327-8.
Lignin-carbohydrate complexes (LCCs) are bioproducts with high potential as alternatives for petrochemicals. However, the complex structure and the lack of protocols for high-yield production limit their usage. Herein, we present data collected from a comprehensive artificial intelligence (AI)-guided optimization of the AquaSolv Omni (AqSO) biorefinery process targeting high-yield production of LCCs. The resulting database, termed SP-LCC, includes structural information extracted from nuclear magnetic resonance measurements (NMR) and data on the molar mass distribution, antioxidant activity, glass transition temperature, thermal degradation, and surface tension. In total, we collected data for 95 LCC-containing samples isolated for different AqSO process conditions. SP-LCC provides a holistic dataset for LCC development, materials understanding, and exploiting the LCC valorization potential. Furthermore, SP-LCC provides valuable data for training machine learning models for further optimization of biorefineries outside the scope of AqSO.
木质素-碳水化合物复合体（LCCs）是具有高潜力的生物产品，可作为石化产品的替代品。然而，其复杂的结构以及缺乏高产生产方案限制了它们的使用。在此，我们展示了从针对LCCs高产生产的AquaSolv Omni（AqSO）生物精炼过程的全面人工智能（AI）引导优化中收集的数据。所得的数据库，称为SP-LCC，包括从核磁共振测量（NMR）中提取的结构信息以及关于摩尔质量分布、抗氧化活性、玻璃化转变温度、热降解和表面张力的数据。我们总共收集了针对不同AqSO工艺条件分离出的95个含LCC样品的数据。SP-LCC为LCC的开发、材料理解以及挖掘LCC的增值潜力提供了一个全面的数据集。此外，SP-LCC为训练机器学习模型提供了有价值的数据，以便在AqSO范围之外进一步优化生物精炼厂。
