The use of cost effective solvents may be necessary to store wood pyrolysis bio-oil in order to stabilize and control its viscosity, but this part of the production system has not been explored. Conversely, any rise in viscosity during storage, that would occur without a solvent, will add variance to the production system and render it cost ineffective. The purpose of this study was to modify bio-oil with a common solvent and then react the bio-oil with an epoxy for bonding of wood without any loss in properties. The acetone pretreatment of the bio-oil/epoxy mixture was found to improve the cross-linking potential and substitution rate based on its mechanical, chemical, and thermal properties. Specifically, the bio-oil was blended with epoxy resin at weight ratios ranging from 2:1 to 1:5 and were then cured. A higher bio-oil substitution rate was found to lower the shear bond strength of the bio-oil/epoxy resins. However, when an acetone pretreatment was used, it was possible to replace the bio-oil by as much as 50% while satisfying usage requirements. Extraction of the bio-oil/epoxy mixture with four different solvents demonstrated an improvement in cross-linking after acetone pretreatment. ATR-FTIR analysis confirmed that the polymer achieved a higher cross-linked structure. DSC and TGA curves showed improved thermal stability with the addition of the acetone pretreatment. UV-Vis characterization showed that some functional groups of the bio-oil to epoxy system were unreacted. Finally, when the resin mixture was utilized to bond wood, the acetone pretreatment coupled with precise tuning of the bio-oil:epoxy ratio was an effective method to control cross-linking while ensuring acceptable bond strength.

The effect of paraffin wax encapsulated microcrystalline cellulose (EMC) particles on the mechanical and physical properties of EMC/epoxy composites were investigated. It was demonstrated that the compatibility between cellulose and epoxy resin could be maintained due to partial encapsulation resulting in an improvement in epoxy composite mechanical properties. This work was unique because it was possible to improve the physical and mechanical properties of the EMC/epoxy composites while encapsulating the microcrystalline cellulose (MCC) for a more homogeneous dispersion. The addition of EMC could increase the stiffness of epoxy composites, especially when the composites were wet. The 1% EMC loading with a 1:2 ratio of wax:MCC demonstrated the best reinforcement for both dry and wet properties. The decomposition temperature of epoxy was preserved up to a 5% EMC loading and for different wax:MCC ratios. An increase in wax encapsulated cellulose loading did increase water absorption but overall this absorption was still low (<1%) for all composites.

The mechanism underlying thermal degradation of oriented strand board was tested under a humid environment. Near-infrared reflectance (NIR) spectroscopy coupled with chemometric modeling was utilized to better understand the degradation of functional groups over time. The flexural properties, internal bond (IB), water absorption, and thickness swelling were tested after exposure to various times of 0, 3, 6, and 9 weeks in a climate-controlled laboratory to 76.7°C and 60 percent relative humidity. The largest reduction in all flexural and physical properties occurred during the first 3 weeks of exposure and then leveled off thereafter, while IB decreased significantly through the 9-week period (α = 0.05). Chemometric models built from NIR spectra revealed pertinent chemical changes in wood chemistry and resin components.

The data analysis of visible-near infrared (Vis-NIR) spectroscopy is critical for precise information extraction and prediction of fiber morphology. The objectives of this study were to discuss the de-noising of Vis-NIR spectra, taken from wood, to improve the prediction accuracy of tracheid length in Dahurian larch wood. Methods based on lifting wavelet transform (LWT) and local correlation maximization (LCM) algorithms were developed for optimal de-noising parameters and partial least squares (PLS) was employed as the prediction method. The results showed that: (1) The values of tracheid length in the study were generally high and had a great positive linear correlation with annual rings (R = 0.881), (2) the optimal de-noising parameters for larch wood based Vis-NIR spectra were Daubechies-2 (db2) mother wavelet with 4 decomposition levels while using a global fixed hard threshold based on LWT, and (3) the Vis-NIR model based on the optimal LWT de-noising parameters ( ${\mathrm{R}}_{\mathrm{c}}^2$ = 0.834, $\mathrm{RMSEC}$ = 0.262, ${\mathrm{RPD}}_{\mathrm{c}}$ = 2.454) outperformed those based on the LWT coupled with LCM algorithm (LWT-LCM) ( ${\mathrm{R}}_{\mathrm{c}}^2$ = 0.816, $\mathrm{RMSEC}$ = 0.276, ${\mathrm{RPD}}_{\mathrm{c}}$ = 2.331) and raw spectra ( ${\mathrm{R}}_{\mathrm{c}}^2$ = 0.822, $\mathrm{RMSEC}$ = 0.271, ${\mathrm{RPD}}_{\mathrm{c}}$ = 2.370). Thus, the selection of appropriate LWT de-noising parameters could aid in extracting a useful signal for better prediction accuracy of tracheid length.

Cellulose fibril aggregates (CFAs) in micro/nano scales are made by the process of delaminating cellulosic fibers in high-pressure homogenizers. However, blockage of the slot during processing by cellulose fibers or particles is possible and so high-intensity ultrasonication was probed as a pretreatment method. This study was unique because of the combination of ultrasonication and homogenizer to produce high quality cellulose fibrils while preventing blockage that typically occurs using conventional homogenizer methods. The CFAs were characterized by the water-retention value (WRV), dynamic light scattering, scanning electron microscopy, atomic force microscopy, and two-dimensional (2-D) correlation spectroscopy. It was found that the ultrasonic treatment was helpful for CFA isolation before homogenizer processing. Less than 10 minutes of ultrasonic processing eliminated most of the blockage at the beginning of the homogenizer process. The results indicate that increasing the ultrasonic treatment time or homogenizer pressure significantly increased the WRVs of the treated cellulose while the CFAs were a mixture of different sized particles in the range of micro to nano scales. 2-D correlation spectroscopy between the near- and mid-infrared region helped to further explain the mechanism of size reduction which was attributable to the breakage of β-1,4-glucosidic bonds along the molecule axis due to shear forces.

Due to the multidimensional complexity and redundancy between wavelengths in the visible and near infrared (Vis-NIR) region, the speed and accuracy of data analysis can be affected. This study aims to investigate the feasibility of simplifying high dimensional data based on transformation of the spectra and local correlation maximization (LCM). These two methods will be applied to determine the prediction accuracy of air-dry density of Ulmus pumila wood. In this study, the reflectance spectra (Refl.) were subjected to the reciprocal (1/Refl.) and logarithm reflectance to improve the spectra signal for prediction. LCM was developed for selecting spectral sensitive regions that were important in the prediction of density. A local correlation coefficient (r) criterion was developed such that if the r ≥ 0.75 (between wavelength and density), then partial least squares and support vector machine (SVM) were employed as the prediction method. Likewise, 2D correlation spectroscopy plots were used to further reduce the data matrix by removing redundant wavelengths. The results showed that (1) although the sensitive region of density was different, the region of r ≥ 0.80 was mainly in the Vis and NIR spectral region. Additionally, the performance of models developed from the sensitive region was better than that of data used from the less-sensitive region. (2) The SVM model was optimized by a genetic algorithm based on the log (1/Refl.) of the sensitive region. In conclusion, it was found that the spectral transformation presented better density estimation results ( = 0.909, root mean square error of calibration = 0.014) than when less sensitive wavelengths were used in the data matrix.