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As one of nature's largest carbon sources with an annual production of around 20 billion tonnes, lignin is the third most abundant biopolymer on the planet. It becomes available as technical lignin, which is produced as a by-product in the pulp and paper industry and in smaller quantities in second generation biofuel refineries. Current estimates suggest that less than 10% of all technical lignin is reused. The high polydispersity, complex heterogeneous structure and uncertain reactivity are the major limiting factors for further processing. The most common applications for various technical lignins without extensive modifications are for example: Surface active substances, additives in bitumen, cement and animal feed. One way to make lignin usable is to break the structure into oligomer units and thus reduce the polydispersity and average molar mass. In addition, it is advantageous to introduce new functionalities such as hydroxyl or carbonyl groups when splitting the high-molecular-weight (HMW) fractions, or to convert existing functionalities. In this study, a mechanochemical method is presented that can degrade and modify technical kraft lignin by means of sodium percarbonate (SPC).
The characterization of technical lignins is a key step for the efficient use and processing of this material into valuable chemicals and for quality control. In this study 31 lignin samples were prepared from different biomass sources (hardwood, softwood, straw, grass) and different pulping processes (sulfite, Kraft, organosolv). Each lignin was analysed by attenuated total reflectance Fourier transform infrared (ATR-FT-IR) spectroscopy. Statistical analysis of the ATR-FT-IR spectra by means of principal component analysis (PCA) showed significant differences between the lignins. Hence, the samples can be separated by PCA according to the original biomass. The differences observed in the ATR-FT-IR spectra result primarily from the relative ratios of the p-hydroxyphenyl, guaiacyl and syringyl units. Only limited influence of the pulping process is reflected by the spectral data. The spectra do not differ between samples processed by Kraft or organosolv processes. Lignosulfonates are clearly distinguishable by ATR-FT-IR from the other samples. For the classification a model was created using the k-nearest neighbor (k NN) algorithm. Different data pretreatment steps were compared for k=1…20. For validation purposes, a 5-fold cross-validation was chosen and the different quality criteria Accuracy (Acc), Error Rate (Err), Sensitivity (TPR) and specificity (TNR) were introduced. The optimized model for k=4 gives values for Acc = 98.9 %, Err = 1.1 %, TPR = 99.2 % and TNR = 99.6 %.