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

M. Alopaeus; M. Stosiek; D. Diment; J. Löfgren; M. Cho; J. Hemming; T. Tirri; A. Pranovich; P.C. Eklund; D. Rigo; M. Balakshin; C. Xu; P. Rinke

doi:10.1038/s41597-025-05327-8

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

M. Alopaeus
, M. Stosiek
, D. Diment
, J. Löfgren
, M. Cho
, J. Hemming
, T. Tirri
, A. Pranovich
, P.C. Eklund
, D. Rigo
, M. Balakshin
, C. Xu^*
, P. Rinke

^*Corresponding author for this work

Research output: Contribution to journal › Article › Scientific › peer-review

4 Citations (Scopus)

37 Downloads (Pure)

Abstract

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.

Original language	English
Article number	996
Journal	Scientific Data
Volume	12
Issue number	1
DOIs	https://doi.org/10.1038/s41597-025-05327-8
Publication status	Published - 13 Jun 2025
MoE publication type	A1 Journal article-refereed

Funding

The authors gratefully acknowledge the Research Council of Finland for the support through the project No. 316601, 341589, and 341596/2021, the FinnCEREs BioEconomy flagship, and the Finnish Center for Artificial Intelligence (FCAI). Parts of this work were conducted within the Research Council of Finland Research Infrastructure “Printed Intelligence Infrastructure” (PII-FIRI). In addition, we acknowledge the Aalto Science-IT project and the Aalto Materials Digitalization (AMAD) platform.

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 9 Industry, Innovation, and Infrastructure

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10.1038/s41597-025-05327-8Licence: CC BY

s41597-025-05327-8Final published version, 2.66 MBLicence: CC BY

https://urn.fi/URN:NBN:fi-fe2025090894806

Cite this

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

abstract = "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.",

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T1 - SP-LCC — a dataset on the structure and properties of lignin-carbohydrate complexes from hardwood

AU - Alopaeus, M.

AU - Stosiek, M.

AU - Diment, D.

AU - Löfgren, J.

AU - Cho, M.

AU - Hemming, J.

AU - Tirri, T.

AU - Pranovich, A.

AU - Eklund, P.C.

AU - Rigo, D.

AU - Balakshin, M.

AU - Xu, C.

AU - Rinke, P.

PY - 2025/6/13

Y1 - 2025/6/13

N2 - 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.

AB - 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.

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ER -

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

Abstract

Funding

UN SDGs

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Åbo Akademi Functional Printing Center

Cite this

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

Abstract

Funding

UN SDGs

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Other files and links

Fingerprint

Equipment

Åbo Akademi Functional Printing Center

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