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

^*Korresponderande författare för detta arbete

Forskningsoutput: Tidskriftsbidrag › Artikel › Vetenskaplig › Peer review

4 Citeringar (Scopus)

41 Nedladdningar (Pure)

Sammanfattning

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.

Originalspråk	Engelska
Artikelnummer	996
Tidskrift	Scientific Data
Volym	12
Nummer	1
DOI	https://doi.org/10.1038/s41597-025-05327-8
Status	Publicerad - 13 juni 2025
MoE-publikationstyp	A1 Tidskriftsartikel-refererad

Finansiering

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.

FN:s SDG:er

Detta resultat bidrar till följande hållbara utvecklingsmål:

SDG 9 – Hållbar industri, innovationer och infrastruktur

Åtkomst av dokument

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

s41597-025-05327-8Slutlig publicerad version, 2,66 MBLicens: CC BY

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

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https://www.scopus.com/pages/publications/105008220617

Åbo Akademi Functional Printing Center
Toivakka, M. (PI), Rosenholm, J. (PI), Anttu, N. (PI), Bobacka, J. (PI), Huynh, T. P. (PI), Peltonen, J. (PI), Wang, X. (PI), Wilen, C.-E. (PI), Xu, C. (PI), Zhang, H. (PI) & Österbacka, R. (PI)
Fakulteten för naturvetenskap och teknik
Utrustning/facilitet: Facilitet

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@article{8fe55b1af3434e5f94766eeba9354679,

title = "SP-LCC {\textemdash} 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.",

author = "M. Alopaeus and M. Stosiek and D. Diment and J. L{\"o}fgren and M. Cho and J. Hemming and T. Tirri and A. Pranovich and P.C. Eklund and D. Rigo and M. Balakshin and C. Xu and P. Rinke",

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doi = "10.1038/s41597-025-05327-8",

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

Sammanfattning

Finansiering

FN:s SDG:er

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Utrustning

Åbo Akademi Functional Printing Center

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