A Novel Formaldehyde-Free Wood Adhesive Synthesized by Straw Soda Lignin and Polyethyleneimine

To eliminate toxic formaldehyde from wood-based panels, a new formaldehyde-free wood adhesive (named OL/PEI adhesive) was synthesized by a reaction of oxidized lignin (OL) and polyethylenimine (PEI) reaction in the presence of sodium periodate. The curing mechanism of the OL/PEI adhesive was clarified by Fourier transform infrared spectroscopy (FTIR) and solid-state cross-polarization magic angle spinning carbon-13 nuclear magnetic resonance (CP/MAS13C-NMR) spectroscopy. The results showed that the sodium periodate could selectively oxidize wheat straw lignin to produce the ortho-quinone, and then the ortho-quinone in OL could further react with amino groups in PEI to form the OL/PEI adhesive. The as-prepared poplar particleboard was investigated with regard to hot-pressing temperature, the hot-pressing time, the OL/PEI weight ratio, and the dosage of OL/PEI adhesive. Under the optimum conditions, e.g., hot pressing temperature of 180 °C, hot pressing time of 13 min, the OL/PEI weight ratio of 1:1, and the dosage of 10%, OL/PEI adhesive was found to disperse evenly into the voids among the shavings of poplar particleboard, followed by the curing of OL/PEI adhesive using hot-pressing to form tightly bonds between the shavings. The resulting particleboard reached the requirement of mechanical properties (GB/T 4897.3-2003), higher water resistance properties, and better heating resistivity. This study demonstrated a new way to produce a formaldehyde-free wood adhesive with unique properties. This material could replace formaldehyde wood adhesive in wood bonding

A Novel Formaldehyde-Free Wood Adhesive Synthesized by Straw Soda Lignin and Polyethyleneimine

To eliminate toxic formaldehyde from wood-based panels, a new formaldehyde-free wood adhesive (named OL/PEI adhesive) was synthesized by a reaction of oxidized lignin (OL) and polyethylenimine (PEI) reaction in the presence of sodium periodate. The curing mechanism of the OL/PEI adhesive was clarified by Fourier transform infrared spectroscopy (FTIR) and solid-state cross-polarization magic angle spinning carbon-13 nuclear magnetic resonance (CP/MAS13C-NMR) spectroscopy. The results showed that the sodium periodate could selectively oxidize wheat straw lignin to produce the ortho-quinone, and then the ortho-quinone in OL could further react with amino groups in PEI to form the OL/PEI adhesive. The as-prepared poplar particleboard was investigated with regard to hot-pressing temperature, the hot-pressing time, the OL/PEI weight ratio, and the dosage of OL/PEI adhesive. Under the optimum conditions, e.g., hot pressing temperature of 180 °C, hot pressing time of 13 min, the OL/PEI weight ratio of 1:1, and the dosage of 10%, OL/PEI adhesive was found to disperse evenly into the voids among the shavings of poplar particleboard, followed by the curing of OL/PEI adhesive using hot-pressing to form tightly bonds between the shavings. The resulting particleboard reached the requirement of mechanical properties (GB/T 4897.3-2003), higher water resistance properties, and better heating resistivity. This study demonstrated a new way to produce a formaldehyde-free wood adhesive with unique properties. This material could replace formaldehyde wood adhesive in wood bonding

Extraction of Cropland Spatial Distribution Information Using Multi-Seasonal Fractal Features: A Case Study of Black Soil in Lishu County, China

Accurate extraction of cropland distribution information using remote sensing technology is a key step in the monitoring, protection, and sustainable development of black soil. To obtain precise spatial distribution of cropland, an information extraction method is developed based on a fractal algorithm integrating temporal and spatial features. The method extracts multi-seasonal fractal features from the Landsat 8 OLI remote sensing data. Its efficiency is demonstrated using black soil in Lishu County, Northeast China. First, each pixel’s upper and lower fractal signals are calculated using a blanket covering method based on the Landsat 8 OLI remote sensing data in the spring, summer, and autumn seasons. The fractal characteristics of the cropland and other land-cover types are analyzed and compared. Second, the ninth lower fractal scale is selected as the feature scale to extract the spatial distribution of cropland in Lishu County. The cropland vector data, the European Space Agency (ESA) WorldCover data, and the statistical yearbook from the same period are used to assess accuracy. Finally, a comparative analysis of this study and existing products at different scales is carried out, and the point matching degree and area matching degree are evaluated. The results show that the point matching degree and the area matching degree of cropland extraction using the multi-seasonal fractal features are 90.66% and 96.21%, and 95.33% and 83.52%, respectively, which are highly consistent with the statistical data provided by the local government. The extracted accuracy of cropland is much better than that of existing products at different scales due to the contribution of the multi-seasonal fractal features. This method can be used to accurately extract cropland information to monitor changes in black soil, and it can be used to support the conservation and development of black soil in China

Detection of diseased pine trees in unmanned aerial vehicle images by using deep convolutional neural networks

This study presents a method that uses high-resolution remote sensing images collected by an unmanned aerial vehicle (UAV) and combines MobileNet and Faster R-CNN for detecting diseased pine trees. MobileNet is used to remove backgrounds to reduce the interference of background information. Faster R-CNN is adopted to distinguish between diseased and healthy pine trees. The number of training samples is expanded due to the insufficient number of available UAV images. Experimental results show that the proposed method is better than traditional machine learning approaches, such as support vector machine and AdaBoost, and methods of DCNN, such as Alexnet, Inception and Faster R-CNN. Through sample expansion and background removal, the proposed method achieves effective detection of diseased pine trees in UAV images by using deep learning technology