Fractionation and Absolute Molecular Weight Determination of Organosolv Lignin and Its Fractions: Analysis by a Novel Acetone-Based SEC─MALS Method

Ponnudurai, Arulselvan; Schulze, Peter; Seidel‐Morgenstern, Andreas; Lorenz, Heike

doi:10.1021/acssuschemeng.2c06309

Cited by 13 publications

(37 citation statements)

References 49 publications

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“…This finding is particularly interesting for determining the efficiency of pyrolysis processes based on residual lignin fragments after the depolymerization process of RCF. The observations by Van Aelst et al correspond with other studies where lignin fractions exhibited a linear correlation between their size and polarity. − This correlation is expected, considering the polyphenolic structure of lignin, where larger sizes result in a higher number of hydroxyl groups in the fraction …”

Section: Challenges and Perspectivessupporting

confidence: 75%

Past to Future: Application of Gel Permeation Chromatography from Petroleomics and Metallopetroleomics to New Energies Applications: A Minireview

et al. 2023

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In the field of petroleomics and metallopetroleomics, the gel permeation chromatography (GPC) technique coupled with high-resolution detection technologies has made significant contributions as an analytical and preparative tool for over five decades. This bibliographic minireview highlights the study of the supramolecular and structural behavior of heavy crude oil and its fractions, as well as their reactivity to various processes by use of GPC. The preferred mobile phase is tetrahydrofuran (THF), whereas the stationary phase is polystyrene−divinylbenzene copolymer to avoid compound retention in the column. Other techniques such as HPTLC, RPLC, and NPHPLC have been used to provide multidimensional separations complementary to GPC. The high molecular weight (HMW) fraction, due to its greater polarity, reactivity to polymerization, and resistance to hydrodemetallization processes, has been the focus of interest for years. GPC coupled with high-resolution techniques has proven to be reliable for the detection of organic and inorganic species in bio-oils, making it a valuable tool for researchers and industry professionals in the context of feedstocks changes and new energy production.

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Section: Challenges and Perspectivessupporting

confidence: 75%

Past to Future: Application of Gel Permeation Chromatography from Petroleomics and Metallopetroleomics to New Energies Applications: A Minireview

et al. 2023

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“…Capturing more complex polymer distributions that cannot be completely characterized by a single average molar mass metric is still a challenge. This is specially important in the case of biobased polymers like lignin involving much more complex molar mass distributions . However, the future role of this type of biopolymeric materials is envisioned to become increasingly important in the context of biorefineries.…”

Section: Discussionmentioning

confidence: 99%

Gibbs–Helmholtz Graph Neural Network for the Prediction of Activity Coefficients of Polymer Solutions at Infinite Dilution

Sanchez Medina,

Kunchapu,

Sundmacher

2023

J. Phys. Chem. A

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Machine learning models have gained prominence for predicting pure-component properties, yet their application to mixture property prediction remains relatively limited. However, the significance of mixtures in our daily lives is undeniable, particularly in industries such as polymer processing. This study presents a modification of the Gibbs− Helmholtz graph neural network (GH-GNN) model for predicting weightbased activity coefficients at infinite dilution (Ω ij ∞ ) in polymer solutions. We evaluate various polymer representations ranging from monomer, repeating unit, periodic unit, and oligomer and observe that, in data-scarce scenarios of polymer−solvent mixtures, polymer representation specifics have a reduced impact compared to data-rich environments. Leveraging transfer learning, we harness richer activity coefficient data from small-size systems, enhancing model accuracy and reducing prediction variability. The modified GH-GNN model achieves remarkable prediction results in mixture interpolation and solvent extrapolation tasks having an overall mean absolute error of 0.15, showcasing the potential of graph-neural-network-based models for property prediction of polymer solutions. Comparative analysis with the established models UNIFAC-ZM and Entropic-FV suggests a promising avenue for future research on the use of data-driven models for the prediction of the thermodynamic properties of polymer solutions.

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“…Lignin fractionation methods have been developed to produce lignin samples of reduced dispersity in distinct molar mass ranges. − For acetone organosolv lignin, for example, fractionation could be achieved by consecutive solubilization in a variety of solvents or by stepwise precipitation by water dilution of the liquor. ,, The beech and birch wood lignin fractions thus obtained showed that a significant part of the lignin consists of large lignin fragments with weight-average molecular weights ( M w ) roughly between 2500 and 10 000 g/mol.…”

Section: Introductionmentioning

confidence: 99%

Reductive Partial Depolymerization of Acetone Organosolv Lignin to Tailor Lignin Molar Mass, Dispersity, and Reactivity for Polymer Applications

Smit

Dezaire

Riddell

et al. 2023

ACS Sustainable Chem. Eng.

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Lignin partial depolymerization by reduction (PDR) was developed as a strategy to tailor a technical lignin's molar mass and reduce its heterogeneity and to potentially increase the reactivity of lignin hydroxyl groups in polymer applications such as PU foams and coatings. The process aims to cleave remaining lignin β-O-4 linkages, thereby reducing the molar mass of large lignin fragments and overall lignin dispersity. Acetone organosolv lignin from pilot-scale fractionation of industrial-size wood chips was depolymerized using methanol, a Ru/C catalyst, and externally supplied hydrogen. The effect of reaction temperatures (in the presence and absence of the catalyst) was fully detailed using SEC, 31 P NMR, and 2D-HSQC NMR analyses of the depolymerized lignin. The Ru/C catalyst promoted molar mass reduction by hydrogenolysis and slightly increased the lignin aliphatic OH content. Process parameter screening showed effective depolymerization at high lignin concentrations but required relatively high catalyst loadings. PDR depolymerization efficiency proved to be dependent on the technical lignin's quality. A less-condensed lignin with a higher β-O-4 content showed improved ether cleavage, yielding a lower lignin molar mass after PDR and increased formation of 4-n-propanol end groups. Overall, the PDR process provides control over key lignin characteristics, which in turn offers potential to tailor biobased polymer properties for various applications.

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Fractionation and Absolute Molecular Weight Determination of Organosolv Lignin and Its Fractions: Analysis by a Novel Acetone-Based SEC─MALS Method

Cited by 13 publications

References 49 publications

Past to Future: Application of Gel Permeation Chromatography from Petroleomics and Metallopetroleomics to New Energies Applications: A Minireview

Past to Future: Application of Gel Permeation Chromatography from Petroleomics and Metallopetroleomics to New Energies Applications: A Minireview

Gibbs–Helmholtz Graph Neural Network for the Prediction of Activity Coefficients of Polymer Solutions at Infinite Dilution

Reductive Partial Depolymerization of Acetone Organosolv Lignin to Tailor Lignin Molar Mass, Dispersity, and Reactivity for Polymer Applications

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