Chemical imaging to reveal the resin distribution in impregnation-treated wood at different spatial scales. (January 2023) - Altgen, Michael; Awais, Muhammad; Altgen, Daniela; Klüppel, André; Koch, Gerald; Mäkelä, Mikko; Olbrich, Andrea; Rautkari, Lauri
Graphical abstract: Highlights: Chemical heterogeneity is a common challenge in the application of biomaterials such as wood. Chemical imaging methods unravel spatial heterogeneity in wood on different length scales. These methods can quantify the chemical distribution in treated wood on macroscopic and cellular levels. Combining imaging methods provides a holistic insight into structure–property relations of biomaterials. Abstract: An inhomogeneous chemical distribution can be problematic in many biomaterial applications, including wood impregnation. Since wood is a hierarchically structured material, the chemical distribution must be considered on different length scales. Here, a combination of imaging methods revealed the distribution of phenol–formaldehyde resin in impregnation-treated European beech wood within the scale of several millimeters or larger (macroscopic) and the micron scale (cellular level). The macroscopic resin distribution was quantified by hyperspectral near-infrared (NIR) image regression. A partial least square regression model accurately predicted the resin content in the range of 0–30 % with average prediction errors of ≤0.93 % for calibration and the test set. The cellular resin distribution was investigated by mapping the UV absorbance in selected regions of interest at high lateral resolution using UV microspectrophotometry (UMSP). The application of both imaging techniques to board sections revealed a process-dependent resin distribution. NIR image regression quantified the drying-induced migration of resin toward the board surfaces. UMSP measurements in selected regions revealed that this resin migration also affected the resin distribution across cell walls. Overall, the results demonstrate the potential of combining chemical imaging techniques to quantify process-dependent heterogeneity and to develop efficient treatments for wood and other biomaterials.