The modification of wood and its surface is one of the challenges that is being perfected with the aim of transitioning to sustainable management. This study investigated the dynamic mechanical and thermal behaviour of unmodified and styrene modified fir wood (Abies alba Mill.). Styrene monomer was chosen and impregnated into the porous structure of fir wood by reversible addition-fragmentation chain transfer (RAFT) polymerisation. Attenuated total reflection Fourier-transform infrared spectroscopy (FTIR-ATR), dynamic mechanical analysis (DMA), and thermogravimetric analysis (TGA) were employed to characterise the chemical structure, viscoelastic properties, and thermal stability of unmodified and modified (surface-modified) wood. All tests have to be regarded as being preliminary due to the small number of specimens. Fourier transform infrared analysis showed evidence of the phenyl group from styrene at 700 cm⁻¹. DMA results showed that the modified wood caused an increase in the glass transition temperature relative to the unmodified wood. In addition, modification with styrene improves thermal stability, as revealed by thermogravimetric analysis (TGA).

The world’s expanding population presents a challenge through its rising demand for wood products. This requirement contributes to increased production and, ultimately, the high-quality and efficient utilization of basic materials. Detecting defects in wood elements, which are inevitable when working with a natural material such as wood, is one of the difficulties associated with the issue above. Even in modern times, people still identify wood defects by visually scrutinizing the sawn surface and marking the defects. Industrial scanners equipped with software based on convolutional neural networks (CNNs) allow for the rapid detection of defects and have the potential to accelerate production and eradicate human subjectivity. This paper evaluates the suitability of defect recognition software in industrial scanners against software specifically designed for this task within a research project conducted using Adaptive Vision Studio, focusing on feature detection techniques. The research revealed that the software installed as part of the industrial scanner is more effective for analyzing knots (77.78% vs. 70.37%), sapwood (100% vs. 80%), and ambrosia wood (60% vs. 20%), while the software derived from the project is more effective for analyzing cracks (70% vs. 65%), ingrown bark (42.86% vs. 28.57%), and wood rays (81.82% vs. 27.27%).