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<p>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.</p>

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SP-LCC — a dataset on the structure and properties of lignin-carbohydrate complexes from hardwood

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年6月13日

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  • Marie Alopaeus,
  • Matthias Stosiek,
  • Daryna Diment,
  • Joakim Löfgren,
  • MiJung Cho,
  • Jarl Hemming,
  • Teija Tirri,
  • Andrey Pranovich,
  • Patrik C. Eklund,
  • Davide Rigo,
  • Mikhail Balakshin,
  • Chunlin Xu,
  • Patrick Rinke

摘要

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.