The characterization of the dielectric properties of wood–polymer composites (WPCs) is essential to understand their interaction with electromagnetic fields and evaluate their potential use for new applications. Thus, dielectric spectroscopy monitored the evolution of the dielectric properties of WPCs over a wide frequency range of 1 MHz to 1 GHz. WPCs were prepared using mixtures of different proportions (40%, 50%, and 60%) of wood and bark fibers from various species, high-density polyethylene, and maleated polyethylene (3%) by a two-step process, extrusion and compression molding. Results indicated that wood fibers modify the resistivity of polyethylene at low frequencies but have no effect at microwave frequencies. Increasing the fiber content increases the composites’ dielectric properties. The fibers’ cellulose content explains the variation in the dielectric properties of composites reinforced with fibers from different wood species. Indeed, composites with high cellulose content show higher dielectric constants.

Four matched groups of black spruce logs were processed with a chipper-canter at temperatures of 20, 0, -10, and -20 °C. Each log was transformed at two moisture contents (MC, green and air-dried) using two cutting widths (CW, 12.7 and 25.4 mm). Mean MC for each CW was assessed from a sample of the obtained chips. Knot characteristics were measured on the cant surfaces after log processing. Chip size was assessed by thickness (Domtar classifier) and width/length (Williams classifier). The results showed that the chip size was significantly affected by the CW and temperature, and in a lesser degree by the chip MC. The weighted mean chip thickness (WCT) increased with the CW. As temperature decreased below 0 °C, WCT and accepts decreased, while proportions of fines and pin chips increased. Chips obtained from green logs were thinner compared to air-dried logs when processed at the coldest temperature (minus 20 °C). The number and size of knots had an important impact on chip size, particularly on WCT. Multiple regressions were developed to predict WCT. Results showed the potential benefits of measuring log temperature and knot features to reduce chip thickness variation during fragmentation and thus improving chip size uniformity.

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