Home Physical Sciences Interactive effects of temperature, moisture content, and decay on the electrical resistance of Populus ussuriensis wood
Article Open Access

Interactive effects of temperature, moisture content, and decay on the electrical resistance of Populus ussuriensis wood

  • ORCID logo , , , , , EMAIL logo , and
Published/Copyright: December 17, 2025
Download Article (PDF)
Holzforschung
From the journal Holzforschung Volume 80 Issue 1

Abstract

The study sought to examine the response of the logarithm of electrical resistance (logR) of Populus ussuriensis wood to temperature fluctuations ranging from −20 to 20 °C and moisture content variations between 0 and over 100 %. Resistance tests were performed on 48 healthy and 72 decayed samples via a digital bridge. Studies revealed that temperatures dropping below the freezing point markedly affect the logR value of healthy wood, exhibiting an abrupt shift in the −5 to 0 °C spectrum. The logR of healthy wood was more significantly affected by the moisture content below the fiber saturation point (FSP). A comparison of the logR of decayed samples with that of healthy wood revealed a similar trend with the changes of temperature and moisture content. At the same temperature, the logR of healthy wood was a slightly higher than that of the decayed samples. The logR of healthy wood exhibited a slightly higher than that of decayed samples at same moisture content. The multivariate linear regression models established between moisture content, temperature, mass loss rate and logR when the mass loss rate (Es) was ≤10 % demonstrated a satisfactory fit, with coefficients of determination (R2) exceeding 0.7.

Keywords: decay wood; wood nondestructive testing; wood electrical resistance; temperature; moisture content

1 Introduction

The forestry value chain confronts two interlinked strategic objectives: (1) enhancing yield optimization from existing dendrological resources and (2) ensuring continuous quality improvement in processed wood commodities. Nondestructive testing of wood can reduce wood loss and is an essential means to improve wood utilization. The electrical resistance method can be used to detect the early decay of standing trees as early as possible, thereby reducing the damage caused by decay. This is the unique advantage of the electrical resistance method in the nondestructive testing of trees. Prior investigations have established diverse methodologies for assessing wood resistivity, including the Shigometer needle-type standing resistance tester (Shigo et al. 1977), four-point test method (Larsson et al. 2004), and electrical resistant tomography (ERT) (Nicolotti et al. 2003). With the continuous development of ERT and computer technology, a tree resistance tomography instrument referred to as Picus-Tree-Tronic has been developed (Bieker and Rust 2010; Lin et al. 2012; Yue et al. 2017) which is a widely used wood-specific testing instrument. Research has revealed that current type and frequency significantly influence wood resistance and that the relationship between the voltage and waveform of the AC signal and wood resistance is not significant (Bao and Wang 2015). A novel method for measuring electrical resistance in wood was presented (Casans et al. 2019), which could reduce the transient time required for value stabilization.

Forest resources exhibit geographical dispersion across regions coupled with seasonal climatic variations. As a natural, organic composite, wood’s structural complexity is manifested through three inherent characteristics: its porous architecture, high moisture absorption capacity, and directional property variance. It was evident that the electrical resistance of wood was influenced by a multitude of factors. Among these, temperature and moisture content were considered to be of paramount importance (Ruan 2005). To optimize the reliability of electrical resistance measurements in dendrological conservation applications, particularly for cross-climate comparative analysis and noninvasive decay diagnostics, systematic investigations into thermohygric parameter correlations with the conductive properties of wood have become methodologically imperative.

The inherent molecular architecture of lignocellulosic matrices results in limited mobile charge carriers, establishing anhydrous wood as a dielectric material under standard conditions. The application of external electromagnetic fields induces ionization phenomena within these biopolymeric networks. Weak conductive behavior arises from transient ionic species, including dissociated functional groups from structural polymers and impurity-derived charge carriers distributed throughout the hierarchical wood microstructure. Thermodynamic equilibrium governs ion population dynamics at fixed thermal states. Elevated temperatures promote (1) a reduction in the activation energy for bound ion dissociation and (2) an exponential increase in the mobile charge density. Consequently, the bulk conductivity (σ) can be increased through reciprocal resistivity relationships (ρ = 1/σ). Hydration effects manifest dual-phase modulation mechanisms: (1) Sub-FSP regime (<∼28 % MC): ionic concentration dominates charge transport via solution thermodynamics; (2) post-FSP regime (>∼28 % MC): ion mobility becomes a rate-limiting factor controlled by percolation dynamics in capillary water systems.

The effects of temperature and moisture content on wood conductivity remain underexplored in the literature. Pioneering work by Hiruma (1915) established inverse proportionality between hydration states and impedance characteristics in arboreal matrices, demonstrating 38–62 % resistivity reduction per 10 % moisture increment. Subsequent investigations by Hasselblatt (1926) revealed bifurcated conduction regimes in Betula spp. (1) the sub-FSP phase (MC < 28 %) exhibited ohmic behavior with a linear current‒voltage relationship (R2 = 0.89–0.94). (2) The post-FSP phase (MC > 28 %) transitioned to ionic hopping conduction through capillary water networks. Recent advancements in thermal modification research (van Blokland and Adamopoulos 2022) corroborate this dichotomy, showing that even torrefied Picea abies maintains predictable σ ‒ MC correlations (slope = −0.074 kΩ−1/% MC) below FSP through preserved hydroxyl group mobility. Comparative analysis confirmed that analogous logR–MC linearity (β = −0.081 ± 0.005, p < 0.01) persisted across the modified/unmodified samples when MC ≤ FSP, which was attributable to consistent bound‒water dielectric relaxation dynamics.

In contrast, no apparent relationship could be noted when the moisture content was above the FSP. Based on the results of Hasselblatt (Hiruma 1915), a method to quickly detect the moisture content of wood was studied (Stamm 1927), and the reliability of the detection of moisture content under the influence of different factors, such as temperature, tree species, and density, was examined. On this basis, a method for detecting the FSP of wood based on electrical resistance was studied (Stamm 1929). Since then, many studies have investigated the relationship between the electrical resistance of wood and moisture content. A variety of moisture detectors for wood-based materials utilizing the theory of electrical resistance has been developed. These detectors have also been used to produce and process wood (Dunlap 1944, 1945; James 1963). A theory on the conduction of textile fiber materials was subsequently proposed (Hearle 1952, 1953), which was applied in the study of wood conductivity (Brown et al. 1963). The change in the electrical resistance of wood was studied and analyzed over a broader range of temperatures and moisture contents (Forsén and Tarvainen 2000). These findings indicate that the transfer of charges could cause the conduction of wood from ions rather than electrons. The effects of the electrical resistance and moisture content of wood on cell activity have been studied (Song et al. 1994). Du Hongshuang (Du et al. 2002) studied the relationship between temperature and moisture content on electrical resistivity during the drying of wood and reported that the electrical resistivity was affected by temperature and moisture content. To analyze the effects of rainfall, diurnal variation and other factors on changes in the water content of standing trees, the ERT (electrical resistance tomography) technique was used to study the water distribution and movement in standing tree trunks (Wu et al. 2008, 2009; Xu et al. 2006). The electrical conductivity of green New Zealand-grown radiata pine sapwood was studied over a 20–90 °C temperature range. The studied sapwood had a moisture content in the range of 100–200 % (Nursultanov et al. 2017). The effects of wood parameters such as grain orientation, moisture content, and basic density were evaluated. The effects of temperature and grain orientation on the conductivity were found to be much greater than those of moisture content and basic density. At 23 °C, the conductivity in the longitudinal direction was approximately 20 times greater than that in the tangential direction and 10 times greater than that in the radial direction. The responses of the ER to stem temperature in three Australian native tree species were investigated (Luo et al. 2019). The ER exponentially decreases with increasing temperature (5–40 °C) for all the sample wood sections. The electrical conductivity of the unseasoned timber of three softwood species and one hardwood species was studied (Nursultanov et al. 2020) over the temperature range of 20–90 °C, which sought to provide possible explanations for the EC variation in green wood.

However, most of the above studies focused on measuring the moisture content of wood via the electrical resistance method. Few studies have focused on the influence of temperature and changes in the water state on the electrical resistance of wood. However, there is still a lack of research on the influence of temperature on the electrical resistance of wood, especially at low temperatures. This study is important for applying electrical resistance tomography to assess the quality of wood and detect internal decay defects in cold regions with a long period below 0 °C. In the effective utilization of electrical resistance tomography as a convenient tool for timely detection and evaluation of the decay defects of standing timber and logs in cold areas, it is highly important to study the influences of temperature and moisture content on the electrical resistance of wood.

To detect defects and analyze the properties of wood via the electrical resistance method, healthy and defect-free wood samples and wood samples infected by wood-rotting fungi were selected for this study. The effects of temperature changes on the resistance of healthy and decayed wood at different moisture contents and the effects of moisture content changes on the resistance of healthy and decayed wood at different temperatures were investigated separately. Furthermore, the combined effects of temperature, moisture content and decay were analyzed.

2 Materials and methods

2.1 Selection and preparation of test materials

Populus ussuriensis was selected as the test subject. At the Fangzheng Forestry Bureau of Heilongjiang Province, in combination with the winter harvesting operation of the forest farm, two trees were considered, where a wooden section with a size of Φ20 mm × 3,000 mm was taken from near the chest height (1.3 m) and transported back to the laboratory. They were then subjected to peel processing and dried to equilibrium moisture content at room temperature (approximately 20 °C) in the air. Since there is no unified standard for testing wood resistance and based on earlier reports, the sample size was adjusted to a specific current conduction length to increase the test resistance (Gao et al. 2017). The sapwood of healthy wood was processed into 120 rectangular samples with dimensions of 20 mm × 20 mm × 50 mm, and the length was parallel to the rift grain. Forty-eight of them were used as a sample (numbered 1, 2… 48) of healthy wood, and the other 72 samples (numbered 49, 50…120) were subjected to infection by wood‒rot fungi, with the strain used in the test being Gloeephyllum trabeum. The samples decayed in 2-week phases, and 12 phases were conducted for a total of 24 weeks. Six samples from three culture bottles were tested in each phase.

2.2 Test equipment

A TL2812D LCR digital bridge with a range of 0.1–199.99 MΩ at 0–40 °C was used to measure the resistance of the sample (Ai et al. 2022; Gao et al. 2018; Zheng et al. 2024). An ultralow temperature freezer was used to adjust the temperature of the wood samples. A high-frequency wood moisture meter (Model FD-100B) was used to measure the moisture content of the wood samples. An electronic balance was used to determine the weights of the samples. An electric blast-drying oven (Model 101-1A) was used to dry the samples. A thermocouple thermometer (35,100 K) was used to measure the temperature of the wood samples.

2.3 Test methods

2.3.1 Testing healthy wood samples

First, 48 air-dried healthy poplar samples were tested. The decayed wood samples were collected at regular intervals throughout the decay process, and their resistance and moisture content were measured immediately.

To facilitate the resistance measurements, a stainless-steel screw approximately 5 mm in length was attached to both ends of the sample. Fifty screws were selected and weighed, and the average weight of the screws was obtained. When the required weight data (such as moisture content) were calculated, the weights of the stainless-steel screws were subtracted. Under normal temperature (the average temperature of the laboratory during the test was approximately 20 °C), each sample’s moisture content was measured by a high-frequency wood moisture meter. Then, the digital bridge was opened and preheated for 15 min, and the ends of the electrode were clipped on the stainless-steel screws at the ends of the wood sample. The digital bridge’s AC signal frequency was set to 1 kHz, the level was set to 1 V, and the range was set to automatic. When the data stabilized (approximately 30–60 s), the resistance of the measurement display board was recorded. With the above method, all the pieces were tested, and the measured data were recorded.

After the test at room temperature, all the samples were placed in an ultralow temperature freezer to adjust the temperature before testing. The freezer temperature was set to 10, 5, 0, −5, −10, or −20 °C. First, the specimen was wrapped with plastic film, and the wrapped sample was sealed in a plastic bag to avoid the exchange of moisture from the wood sample. Then, the sample was placed in the freezer for 12 h so that the temperature inside the sample matched the freezer temperature. A thermocouple thermometer was used to measure the actual temperature of each sample to ensure that the actual temperature of the wood differed from the set temperature within ±0.3 °C, after which the next test was performed. At each temperature, the weight and electrical resistance of each sample were measured and recorded. It took time to stabilize the resistance data during the electrical resistance measurements. To prevent changes in the internal temperature of the sample, the wires and clips at both ends of the bridge electrode were placed in the freezer to measure the internal temperature of the sample, as shown in Figure 1.

Figure 1: Experimental setup for measuring wood specimens in a controlled low-temperature environment.
Figure 1:

Experimental setup for measuring wood specimens in a controlled low-temperature environment.

To study the effect of moisture content on the electrical resistance at different temperatures and the effect of temperature on the electrical resistance at different moisture contents, after the completion of the above test, the sample was subjected to saturated water treatment, placed in a constant temperature and humidity chamber, and removed after a specified interval. Weighing was repeated until the sample reached the equilibrium state of moisture content corresponding to the temperature and humidity set by the constant temperature and humidity chamber. Once the specimens were at equilibrium, the same procedure followed as given above and then subjected to a variety of temperatures. Additionally, the samples were wrapped in plastic film while in equilibrium and placed in the freezer, with measurements taken every 12 h. Its weight and electrical resistance were recorded at different temperatures until the electrical resistance test was completed at the minimum relative humidity. Finally, all the samples were dried, the electrical resistance at different temperatures in the dry state was measured, and the moisture content of each sample was obtained via inverse calculation.

2.3.2 Testing of decayed wood samples

Wood samples were removed periodically at each stage of decay, and resistance and moisture measurements were taken immediately after removal. The resistance and moisture content were measured in the same way as described in Section 2.3.1, where the resistance and moisture content were measured at ambient temperature. After the tests were completed at ambient temperature, all the samples were wrapped in plastic wrap and placed in an ultralow-temperature freezer. The freezer temperature was set to 10, 5, 0, −5, −10, or −20 °C. Then, the sample was placed in the freezer for 12 h so that the temperature inside the sample matched the freezer temperature. The test methods for different temperatures are described in Section 2.3.1. When wood decays, its moisture content changed. Therefore, the moisture content of decayed samples varied. To study the effect of moisture content on the resistance and the effect of temperature on the resistance under different moisture contents, after the above tests, the samples were left in air to reduce the moisture content, and the moisture content and resistance at different temperatures were measured and recorded for each sample in turn after different periods of standing. The moisture content and resistance at different temperatures were measured and recorded for each sample in turn. When the moisture content of the sample was reduced to the equilibrium moisture content by leaving the sample in the air until it dried out, no further resistance test was carried out, during which time each sample was subjected to the same test as above, and finally, all the samples were dried.

3 Results and analysis

3.1 Variation in the electrical resistance of wood with temperature at different moisture contents

3.1.1 Variation in the electrical resistance of healthy samples with temperature at different moisture contents

A total of 48 pieces were subjected to testing via an LCR digital bridge (TL2812D), and the resulting resistance data for a range of moisture contents at various temperatures were collected. Since the resistance values varied significantly at different temperatures, the logarithm of electrical resistance, logR (where R is measured in Ω), was calculated for better analysis. The effect of temperature on logR was subsequently analyzed, and a logR-temperature (logR–T) diagram was constructed, as shown in Figure 2.

Figure 2: Effects of temperature on the electrical resistance of Populus ussuriensis wood at different MCs.
Figure 2:

Effects of temperature on the electrical resistance of Populus ussuriensis wood at different MCs.

First, from the relationship between changes in temperature and logR, as shown in Figure 2, in terms of overall moisture content, logR decreased sharply below 0 °C and stabilized at temperatures above freezing (0 °C). LogR first decreased and then stabilized with increasing temperature. Notably, when the moisture content was less than 25 % (Figure 2a), the decrease in logR was distinct among the different moisture contents, and the changes in the trend were not obvious at each moisture content. However, it presents a steady downward trend in the ranges of the temperature measurements. When the moisture content was greater than 25 %, as shown in Figure 2b and c, logR exhibited the same regularity between different moisture contents, i.e., an abrupt jump at approximately 0 °C. When the moisture content was between 25 % and 50 %, as shown in Figure 2b, an abrupt jump occurred at −5 °C; when the moisture content was greater than 50 %, as shown in Figure 2c, an abrupt jump occurred at 0 °C. On the basis of this phenomenon, logR–T can be divided into three temperature regions: below the freezing point, logR decreases with increasing temperature; near the freezing point (−5 to 0 °C), logR changes rapidly with increasing temperature; above the freezing point, logR changes slowly with increasing temperature; and the higher the temperature is, the more stable logR is. This phenomenon is likely related to the saturated vapour pressure which is a function of temperature but has different expressions below and above the freezing point (Fortino et al. 2021).

The above experimental results were consistent with the results of previous studies. James (James 1963) reported that the resistivity decreases with increasing temperature at a determined moisture content when wood moisture is measured. Lin (Lin 1965) studied the factors influencing wood conductivity and reported that temperature significantly influences the electrical conductivity of wood. With increasing temperature, the resistivity decreased with increasing moisture content. Lin also reported that the reciprocal relationship curve between the logarithm of electrical resistance and temperature appeared as a discontinuous turning point when the temperature was approximately 0 °C and when the moisture content was above the FSP. When the moisture content was close to the FSP, the temperature was between 10 and 0 °C, the logarithmic relationship curve between temperature, and resistivity might be due to the “ice crystal”, which was formed by the freezing of wet moisture in the cell cavity of the wood (Kollmann et al. 2012). During drying, the temperature significantly affects the DC resistance of wood, as evidenced by the eucalyptus resistivity tests conducted at different temperatures (Du et al. 2002). Electrical resistance tomography was used to measure electrical resistance at various temperatures to study the influence of ambient temperature on standing trees’ electrical resistance. The results revealed that the overall average electrical resistance of the cross-sections of standing trees increased with decreasing temperature (Wang et al. 2016; Yue et al. 2018).

Thermally mediated resistivity variations in wood systems originate from dual interdependent mechanisms. Primarily, cryogenic conditions induce the following:

  1. Suppression of ionic mobility through increased thermal activation barriers (ΔG‡↑);

  2. Depletion of mobile charge carriers via recombination kinetics;

  3. Concomitant reduction in the bulk conductivity (σ = nqμ), where n denotes the charge density, q denotes the elementary charge, and μ denotes the ionic mobility.

Second, subambient thermal gradients trigger hygroscopic contraction phenomena governed by Kelvin equation effects. This phase transition involves bound water migration from cell wall matrices to lumen surfaces, driven by vapor pressure differentials (ΔP = Psat − Pvap) across hierarchical cellular architectures. This moisture redistribution alters the dielectric permittivity (ε′) while modifying charge transport pathways through tracheid networks.

The change in the electrical resistance of wood with temperature may also be related to the moisture content in the wood and its state. When the temperature decreases below the freezing point, most moisture in wet wood changes its liquid water phase to solid ice (Xu and Wang 2012). The resistivity of solid ice is much greater than that of water. Therefore, the electrical resistance of wood increased with increasing number of ice crystals when the temperature decreased below zero, and an abrupt jump was noted at approximately 0 °C. The amplitude of the jump could be related to the moisture content of the wood. At a low moisture content, no jump in logR was observed in the test. This is mainly due to the phase change from free water inside the wood, and there is a small part of the combined water. When the moisture content of the wood was above the FSP, there was free water inside the wood. Therefore, in the experiments, the change in logR occurs near the freezing point when the water content is greater than 25 %. When the moisture content is 25 %, the change in logR occurs between −5 and 0 °C, indicating that not all the liquid water becomes solid ice since the free water in the cell is not rapidly frozen into ice. The process of changing the state of water and the process of temperature changes were not completed simultaneously. When the moisture content is high enough, there is more free water inside the wood. Therefore, when the temperature decreased to 0 °C, a large amount of free water underwent a phase change, affecting the electrical resistance so that transformation was initiated at 0 °C. When there is not enough moisture content, although there is free water, it is less abundant, and as the process changes in the state of water, it cannot form ice quickly. Nevertheless, as the temperature continues to decrease to −5 °C, a small amount of free water also freezes into ice. Thus, it has a significant effect on the electrical resistance, leading to transformation. Therefore, in this experiment, when the moisture content was greater than 50 %, the logR was abrupt at 0 °C, and when the moisture content was greater than the FSP and less than 50 %, a change occurred at −5 °C.

The analysis presented above allows us to divide the relationship between logR–T into three temperature categories. In the first category, below the freezing point, logR decreases with increasing temperature. In the second category, near the freezing point (approximately −5 to 0 degrees Celsius), logR increases with temperature and rapidly changes. In the third category, above the freezing point, logR changes slowly with increasing temperature, and the higher the temperature is, the more stable logR is.

3.1.2 Variation in the electrical resistance of decayed samples with temperature at different moisture contents

Seventy-two rotten pieces were tested via an LCR digital bridge (TL2812D), and resistance data for a range of moisture contents at different temperatures were obtained. As previously described, logR (the unit of R is Ω) was calculated. The impact of temperature on the logR of decayed wood samples was subsequently investigated, resulting in the construction of a logR-temperature (logR–T) diagram, as illustrated in Figure 3.

Figure 3: Effects of temperature on the electrical resistance of decayed Populus ussuriensis wood at different MCs.
Figure 3:

Effects of temperature on the electrical resistance of decayed Populus ussuriensis wood at different MCs.

Figure 3 shows that the pattern of change in logR with temperature for decayed wood was similar to that for healthy wood. Both exhibited abrupt changes within a certain freezing point range, although the specific temperature range differed slightly. In the case of decayed wood, the change occurred abruptly at approximately −5 °C, whereas in the case of healthy wood, the change was only abrupt when the moisture content was above 25 %, with the temperature at the time of the abrupt change being approximately 0 °C. At the same temperatures, the logR of healthy wood was marginally greater than that of decayed wood, as shown in Figure 4, indicating that the electrical resistance of healthy wood was greater than that of decayed wood.

Figure 4: Mean logarithm values of electrical resistance at different temperatures before and after decay.
Figure 4:

Mean logarithm values of electrical resistance at different temperatures before and after decay.

3.2 Variation in the electrical resistance of wood with moisture content at different temperatures

3.2.1 Variation in the electrical resistance of healthy wood with moisture content at different temperatures

To analyze the influence of moisture content on electrical resistance, the relationships among the logarithmic values of the electrical resistance of wood (logR)-water content (logR–MC) were determined, as shown in Figure 5. This figure shows that when the moisture content was less than 30 % of the FSP, logR decreased with increasing moisture content very quickly, and the magnitude of decrease was relatively large. Above the FSP, the logR trend was gradual. This could be because one of the important dividing points for changes in the physical properties of wood is the FSP of the wood (Li 2002). When the moisture content of wood is lower than the FSP, moisture is present in the cell wall and significantly influences wood properties. When the fiber is above FSP, the bound water in the cell wall of the wood is saturated, and the moisture in the wood is free and present in the cell cavity. Water also affects the physical properties of wood, which gradually decrease.

Figure 5: Effect of MC on the electrical resistance of healthy wood at different temperatures.
Figure 5:

Effect of MC on the electrical resistance of healthy wood at different temperatures.

A regression equation was established between logR and MC, using the fiber saturation point as the boundary. A linear relationship was observed between logR and MC below the FSP. The logR–MC regression model at each temperature is presented in Table 1. The coefficient of determination of the regression models exceeded 0.85, indicating a high correlation between logR and moisture content. The impact of moisture content on electrical resistance is notable. However, above FSP, no correlation was observed between logR and MC, and the effect of moisture content on logR was no longer significant.

Table 1:

Linear regression model between the logR and MC.

Temperature value (°C) Regression model R 2
−20 y = −0.0576x + 8.6107 0.921
−10 y = −0.078x + 8.466 0.898
−5 y = −0.0927x + 8.6036 0.879
0 y = −0.095x + 8.5927 0.888
5 y = −0.103x + 8.3456 0.958
20 y = −0.0912x + 7.887 0.933

The above analysis revealed that at different temperatures, the logarithm of the electrical resistance of wood has the same trend depending on the moisture content. Below FSP, logR decreased significantly with increasing moisture content. The decrease was substantial; after the moisture content reached FSP, the logR of some specimens may continue to decline as the moisture content increased, but there was no regular change or characteristic pattern. The impact of moisture content on the electrical resistance of wood is not noticeable, which is consistent with earlier results. Stamm (1929) studied the correlation between the logarithm of the resistivity of Sequoia and the moisture content (8–170 %). The results revealed that the logarithm of conductivity was linearly related to the water content when the moisture content was lower than the FSP. Musser (1938) obtained the same relationship as Stamm when studying the relationship between various resistivities and moisture contents of wood. Bao Zhenyu (Bao and Wang 2015) conducted electrical resistance tests of aspen wood at different moisture contents (greater than 8 %) and reported that with increasing moisture content, the electrical resistance of the wood tended to decrease. Below FSP, the resistance significantly reduced as the moisture content increased, whereas above FSP, the change in electrical resistance of the wood was not significant. Yue Xiaoquan (Yue et al. 2016) measured the influence of moisture content on the electrical resistance of decayed and healthy wood by testing wood logs at different moisture contents. The values of electrical resistance detected via electrical resistance tomography did not significantly change with increasing moisture content at higher moisture contents.

3.2.2 Variation in the electrical resistance of decayed wood with moisture content at different temperatures

To analyze the influence of different moisture contents on the electrical resistance of decayed wood, the relationship between logR and moisture content was determined, as shown in Figure 6. Figure 6 shows that the influence trend of moisture content on logR at different temperatures was similar. When the moisture content was low (less than 40 %), the trend of the logR of decayed wood decreased with increasing moisture content, whereas with increasing moisture content, the trend of the logR of decayed wood clearly tended to flatten.

Figure 6: Effect of MC on the electrical resistance of decayed wood at different temperatures.
Figure 6:

Effect of MC on the electrical resistance of decayed wood at different temperatures.

In comparison with healthy wood, the alteration in the logR of rotten wood in relation to moisture content exhibited a correlation similar to that observed in healthy wood. When the moisture content was low, the changes in both variables were more pronounced; conversely, as the moisture content increased, the changes in logR became less pronounced. However, at the same moisture content, the logR of healthy wood was slightly greater than that of decayed wood, as illustrated in Figure 7. These findings suggest that the electrical resistance of healthy wood is greater than that of decayed wood.

Figure 7: Mean logarithm values of electrical resistance at different MCs before and after decay.
Figure 7:

Mean logarithm values of electrical resistance at different MCs before and after decay.

3.3 Combined effects of temperature and moisture content on the electrical resistance of wood

3.3.1 The combined effects of temperature and moisture content on the electrical resistance of healthy wood

In order to present the combined effects of moisture content and temperature on wood resistance in a more intuitive manner, a surface plot was employed, as shown in Figure 8.

Figure 8: Three-dimensional curves about the effect of MC and temperature on the electrical resistance of P. ussuriensis wood.
Figure 8:

Three-dimensional curves about the effect of MC and temperature on the electrical resistance of P. ussuriensis wood.

To find a more specific relationship between the moisture content, temperature, and electrical resistance of healthy wood, SPSS (statistical analysis software) (Rahman and Muktadir 2021) was used to perform multiple regression analysis on logR at different temperatures and moisture contents. A regression model was established, as shown in Table 2. Since the FSP of P. ussuriensis wood was approximately 30 %, a regression equation was established at a moisture content of 30 % as the boundary. As shown in Table 2, the coefficient of determination (R2) reached 0.8 or greater, indicating that the model had a good fit, and the F test confidence level was 0.01.

Table 2:

The binary linear regression models between the log R and MC for specific temperature conditions.

Range of MC (%) Regression model y = ax1 + bx2 + c
y x 1 x 2 a b c R 2 F sig
MC < 30 logR T MC −0.2 −0.1 9.354 0.87 111.55 0
MC > 30 logR T MC −0.362 −0.001▼ 6.716 0.887 341.25 0

  1. The coefficient with ▼ indicates that the test is not significant.

The correlations between logR and temperature, as well as moisture content, were analyzed separately. The results showed that the temperature of the wood is significantly correlated with logR at the 0.05 level. For moisture content and logR, when the moisture content is <30 %, it is significantly correlated with logR at the 0.05 level; when the water content is >30 %, the correlation between moisture content and logR does not reach a significant level (the coefficient with ▼ in Table 2 indicates that the test is not significant).

Therefore, the logR of healthy P. ussuriensis wood can be estimated from the regression model when MC is ≤30 %.

3.3.2 The combined effects of temperature and moisture content on the electrical resistance of decayed wood

As with the previous analysis of healthy wood, a three-dimensional diagram was used to visually demonstrate the combined effects of moisture content and temperature on on the electrical resistance of decayed P. ussuriensis wood, as shown in Figure 9.

Figure 9: Three-dimensional dimensions curves about the effect of MC and temperature on the electrical resistance of P. ussuriensis decayed wood.
Figure 9:

Three-dimensional dimensions curves about the effect of MC and temperature on the electrical resistance of P. ussuriensis decayed wood.

To find a more specific relationship between the moisture content, temperature, and electrical resistance of decayed wood, SPSS statistical analysis software was used to perform multiple regression analysis on logR at different temperatures and moisture contents. A regression model was established, as shown in Table 3.

Table 3:

The binary linear regression models between the logarithm values of electrical resistance and MC for specific temperature conditions.

Range of MC (%) Regression model y = ax1 + bx2 + c
y x 1 x 2 a b c R 2 F sig
MC < 40 logR T MC −0.352 −0.308 7.669 0.855 61.772 0
MC > 40 logR T MC −0.346 −0.021▼ 6.481 0.807 167.255 0

  1. The coefficient with ▼ indicates that the test was not significant.

As analyzed in Section 2.2.2, the trend of logR with the rate of reduction in moisture content was more pronounced when the moisture content was lower than approximately 40 %. Therefore, a regression equation was established with a moisture content of 40 % as the boundary, as shown in Table 3. The coefficient of determination (R2) reached 0.8, indicating a good fit of the model, and the F test confidence was 0.01.

The correlations between logR and temperature, as well as moisture content, were analyzed separately. The results revealed that the temperature of the decayed wood was significantly correlated with logR at the 0.05 significance level. For moisture content and logR, when the moisture content was <40 %, it was significantly correlated with logR at the 0.05 level; when the water content was >40 %, the correlation between moisture content and logR did not reach a significant level (the coefficient with ▼ in Table 3 indicates that the test was not significant). Therefore, when the moisture content was less than 40 %, the logR corresponding to different temperatures and water contents of P. ussuriensis decayed wood could be estimated via a regression model.

3.4 Comprehensive effects of temperature, moisture content and decay on the electrical resistance of wood

To improve the accuracy of the resistance method for detecting wood, the comprehensive influence of temperature, moisture content and decay on wood resistance was considered to provide a scientific basis for the accuracy of the resistance method for detecting wood decay defects.

The temperature and moisture content data were calculated as described in Section 2. The mass loss rate was employed here as a means of indicating the degree of decay of the wood. The mass loss rate of the wood sample was determined through calculation. The absolute dry weight of the wood sample prior to the onset of decay (m0) and the absolute dry weight of the wood sample following the conclusion of the decay process (mf) were recorded simultaneously during the procedure outlined in Section 1. Accordingly, the mass loss rate of the wood sample following decay was calculated from the recorded absolute dry weights before and after decay, designated Es. This was calculated via the following formula (1):

(1) E s = m 0 − m f m 0 × 100 %

To determine the impact of moisture content, temperature, and decay on the electrical resistance of wood, SPSS (Statistical Analysis Software) was used to perform multiple regression analysis on the logR at different temperatures, different moisture contents and different mass loss rates. The regression model was established by categorizing the mass loss rate (ES) into four categories (ES ≤ 10 %, 11 % ≤ ES ≤ 24 %, 25 ≤ ES ≤ 44 %, and ES ≥ 45 %) according to the national standard for the natural decay resistance grade of wood, as shown in Table 4. The regression equations were obtained, where the confidence level of the F test was 0.01 and the determination coefficient (R2) reached 0.7, which indicated that the model had a good degree of fit.

Table 4:

Regression models of logR to MC, temperature and weight loss rate in the decayed samples.

The Es range Regression model y = d + ax1 + bx2 + cx3
y x 1 x 2 x 3 d a b c R 2 F sig
Es ≤ 10 % logR Es Temp MC 7.13 −0.01 −0.01 −0.49 0.77 64.08 0
11 % ≤ Es ≤ 24 % logR Es Temp MC 6.94 −0.01 −0.01 −0.51 0.76 82.47 0
25 ≤ Es ≤ 44 % logR Es Temp MC 7.35 −0.01 −0.01 −0.51 0.79 59.32 0
Es ≥ 45 % logR Es Temp MC 6.57 −0.02 −0.03 −0.55 0.75 144.7 0

The correlations between logR and temperature, moisture content and mass loss rate were analyzed separately, as shown in Table 5. The observed results revealed that the temperature and logR were significantly correlated at the 0.05 level. Whereas the relationship between logR and moisture content was significantly correlated at the 0.05 level for Es ≤ 24 %, the correlation has not yet reached the level of significance for Es ≥ 25 %. Similarly, the correlation between logR and the mass loss rate is significant at the 0.05 level for Es ≤ 10 % but not significant for Es ≥ 11 %. Therefore, when Es ≤ 10 %, the logR corresponding to different temperatures, moisture contents and decay levels of poplar wood can be estimated via the regression model in Table 5.

Table 5:

Correlation coefficients of the logarithm values of decayed wood electrical resistance and MC, temperature and weight loss rate.

The Es range Es Temp MC
Determination coefficient Significance test P value Determination coefficient Significance test P value Determination coefficient Significance test P value
Es ≤ 10 % −0.608* 0.039 −0.477* 0.019 −0.408* 0.028
11 % ≤ Es ≤ 24 % −0.327 0.096 −0.491* 0.032 −0.467* 0.076
25 ≤ Es ≤ 44 % −0.033 0.872 −0.191* 0.047 −0.212 0.088
Es ≥ 45 % −0.018 0.95 −0.238* 0.041 −0.156 0.128

  1. Values marked * in the table indicate that the correlation coefficient is significant at the 0.05 level.

4 Conclusions

This study considered 48 healthy P. ussuriensis wood samples and 72 decayed P. ussuriensis wood samples through the regulation of temperature and water content. The electrical resistance of wood was tested systematically at different moisture contents (healthy wood: 0 to above 100 %; decayed wood: 20 % to above 120 %) and different temperatures (20, 10, 5, 0, −5, −10, and −20 °C). The effects of moisture content and temperature on the logarithm of electrical resistance (logR) of healthy wood and decayed wood were both analyzed. The results of this study are summarized as follows:

  1. At different moisture contents, the logR of healthy samples as a whole first decreased and then stabilized as the temperature increased. When the moisture content was below 25 %, as the temperature increased, the variation in logR among the different moisture contents was inconsistent; when the moisture content was above 25 %, logR exhibited the same trend among the different moisture contents, i.e., an abrupt increase at approximately 0 °C. When the temperature was lower than 0 °C, logR decreased with temperature. When the temperature was higher than 0 °C, logR decreased with temperature and did not become apparent or stable.

  2. At different temperatures, the logR values of the healthy samples were consistent with the changes in moisture content. Below the fiber saturation point (FSP), logR decreased significantly, as the moisture content increased, and logR, as well as the moisture content, were linear. The regression coefficient at each temperature was above 0.85, indicating a clear correlation between the water content and logR. Above FSP, the decreasing trend of logR with increasing water content was no longer significant, suggesting that the effect of water content on logR was not as pronounced.

  3. At different moisture contents, the pattern of change in the logR of decayed samples with temperature was similar to that of healthy samples, and both changed abruptly at approximately −5 °C. The logR of healthy wood was slightly greater than that of decayed wood at the same temperature.

  4. For decayed samples at different temperatures, the trend of logR with the rate of moisture content reduction was obvious when the moisture content was less than 40 %. However, as the moisture content increased, the change in the trend of logR became obviously flat. In addition, the pattern of logR change with moisture content was similar to that of healthy samples, and at the same moisture content, the logR of healthy samples was slightly greater than that of decayed samples.

  5. Moisture content and temperature had interactive effects on the logR of the wood of P. ussuriensis. Binary linear regression models between moisture content, temperature and logR were established. For healthy samples, the binary linear regression models showed a good fit when the MC < 30 %, with coefficients of determination R2 above 0.8. For decayed samples, the binary linear regression models showed a good fit when the MC was <40 %, with coefficients of determination R2 above 0.8.

  6. A multiple linear regression model was established to analyze the combined effects of temperature, moisture content, and decay on wood resistance. The model established relationships between logR and moisture content, temperature, and mass loss rate. The fitting degree was high when Es ≤ 10 %, and the determination coefficient (R2) was above 0.7.

Through indoor tests on small samples of wood, the patterns of changes in the electrical resistance of healthy and decayed wood during changes in temperature and moisture, especially under freezing and nonfreezing conditions, were explored, laying the foundation for accurate nondestructive testing of decayed wood in living logs.

Corresponding author: Lihai Wang, College of Engineering and Technology, Northeast Forestry University, Heilongjiang, 150040, Harbin, China, E-mail:

Funding source: National Natural Science Foundation of China

Award Identifier / Grant number: 32071685

Funding source: Fujian Provincial Department of Education Foundation Program

Award Identifier / Grant number: JAT210074

Acknowledgments

The authors gratefully acknowledge all the research partners in these projects for their cooperation and collaboration. The authors would also like to express their gratitude to EditSprings (https://www.editsprings.com/) for the expert linguistic services provided.

  1. Research ethics: Not applicable.

  2. Informed consent: Not applicable.

  3. Author contributions: X.Y. and L.W. conceived and designed the experiments; H.L. and X.S. performed the experiments; J.Y. collected the data; J.Y. and L.H. analyzed the data; X.Y. wrote the manuscript; W.H. and Y.S. supervised the work and reviewed the manuscript. In the revised manuscript, J.Y. was responsible for adding the three-dimensional diagrams and modifying the reference format. All authors have accepted responsibility for the entire content of this manuscript and approved its submission.

  4. Use of Large Language Models, AI and Machine Learning Tools: None declared.

  5. Conflict of interest: The authors declare no conflicts of interest.

  6. Research funding: National Natural Science Foundation of China (No. 32071685) Fujian Provincial Department of Education Foundation Program (No. JAT210074).

  7. Data availability: The raw data can be obtained on request from the corresponding author.

References

Ai, M., Cui, Y., Gao, S., Zhao, Z., and Zhang, L. (2022). Design and temperature control capability study of an automatic temperature control device for measuring the electrical resistance of frozen wood. China For. Prod. Ind. 59.Search in Google Scholar

Bao, Z.Y. and Wang, L.H. (2015). Effects of excitation signal on electrical resistance of Populus davidiana wood. China For. Sci. Technol. 29: 79–82. (In Chinese).Search in Google Scholar

Bieker, D. and Rust, S. (2010). Electric resistivity tomography shows radial variation of electrolytes in Quercus robur. Can. J. For. Res. 40: 1189–1193, https://doi.org/10.1139/x10-076.Search in Google Scholar

Brown, J.H., Davidson, R.W., and Skaar, C. (1963). Mechanism of electrical conduction in wood. For. Prod. J. 13: 455–459.Search in Google Scholar

Casans, B.S., Garcia-Gil, R., Navarro, A.E., and Rosado-Muñoz, A. (2019). Novel wood resistance measurement method reducing the initial transient instabilities arising in DC methods due to polarization effects. Electronics 8: 1253, https://doi.org/10.3390/electronics8111253.Search in Google Scholar

Du, H.S., Li, R., Xiu, H.B., Sun, J.J., Chen, Y.H., and Zhao, Y. (2002). Relations among resistance percentage, temperature and moisture of Oak in the seasoning process. J. Beihua Univ. (Nat. Sci.) 3: 349–351. (In Chinese).Search in Google Scholar

Dunlap, M.E. (1944). Electrical moisture meters for wood. Dept. of Agriculture, Forest Service, Forest Products Laboratory.Search in Google Scholar

Dunlap, M.E. (1945). Methods used for the measurement of the moisture content of wood In: Proceedings of American Society for Testing Materials, Committee Reports; Research Division: Beecroft, Australia, Vol. 45, pp. 269–274.Search in Google Scholar

Forsén, H., Tarvainen, V. (2000). Accuracy and functionality of hand-held wood moisture content meters. Report 79, VTT, Espoo, Research.Search in Google Scholar

Fortino, S., Hradil, P., Koski, K., Korkealaakso, A., Fülöp, L., Burkart, H., and Tirkkonen, T. (2021). Health monitoring of stress-laminated timber bridges assisted by a hygro-thermal model for wood material. Appl. Sci. 11: 98, https://doi.org/10.3390/app11010098.Search in Google Scholar

Gao, S., Bao, Z.Y., Wang, L.H., and Yue, X. (2017). Comparison of voltammetry and digital bridge methods for electrical resistance measurements in wood. Comput. Electron. Agric. 145: 161–168, https://doi.org/10.1016/j.compag.2017.11.004.Search in Google Scholar

Gao, S., Yue, X.Q., and Wang, L.H. (2018). Effect of the degree of decay on the electrical resistance of wood degraded by brown-rot fungi. Can. J. For. Res. 49: 145–153, https://doi.org/10.1139/CJFR-2018-0282.Search in Google Scholar

Hasselblatt, M. (1926). Der Wasserdampfdruck und die elektrische Leitfähigkeit des Holzes in Abhängigkeit von seinem Wassergehalt. Z. Anorg. Allg. Chem. 154: 375–385, https://doi.org/10.1002/zaac.19261540133.Search in Google Scholar

Hearle, J.W.S. (1952). The electrical resistance of textile materials: a review of the literature. J. Text. Inst. Trans. 43: 194–223, https://doi.org/10.1080/19447015208664007.Search in Google Scholar

Hearle, J.W.S. (1953). The electrical resistance of textile materials: IV. Theory. J. Text. Inst. Trans. 44: 177–198, https://doi.org/10.1080/19447025308659737.Search in Google Scholar

Hiruma, J. (1915). Experiment of electrical resistance in wood; extracts from the Bulletin of the Forest Experiment Station, Meguro, Tokyo, Vol. 2. Bureau of Forestry, Department of Agriculture and Commerce, Tokyo, Japan, pp. 59–65.Search in Google Scholar

James, W.L. (1963). Electric moisture meters for wood. Department of Agriculture, Forest Service, Forest Products Laboratory, Madison, WI, USA.Search in Google Scholar

Kollmann, C.Jr. (2012). Principles of wood and wood technology: solid wood. Jiang liangyou translated. Forestry Publishing Press, Beijing, China.Search in Google Scholar

Larsson, B., Bengtsson, B., and Gustafsson, M. (2004). Nondestructive detection of decay in living trees. Tree Physiol. 24: 853–858.10.1093/treephys/24.7.853Search in Google Scholar PubMed

Li, J. (2002). Wood Science. Higher Education Press, Beijing, China.Search in Google Scholar

Lin, R.T. (1965). A study on the electrical conduction in wood. For. Prod. 15: 506–551.Search in Google Scholar

Lin, C.J., Chung, C.H., Yang, T.H., and Lin, F.-C. (2012). Detection of electric resistivity tomography and evaluation of the sapwood-heartwood demarcation in three Asia gymnosperm species. Silva Fennica 46: 415–424, https://doi.org/10.14214/sf.440.Search in Google Scholar

Luo, Z., Guan, H., Zhang, X. (2019). The temperature effect and correction models for using electrical resistivity to estimate wood moisture variations. J. Hydrol. 578: 124022. https://doi.org/10.1016/j.jhydrol.2019.124022.Search in Google Scholar

Musser, E. (1938). Die Bestimmung der Holzfeuchtigkeit durch Messung des elektrischen Widerstandes. Holz Roh- Werkst. 1: 417–420, https://doi.org/10.1007/BF02605258.Search in Google Scholar

Nicolotti, G., Socco, L., Martinis, R., and Sambuelli, L. (2003). Application and comparison of three tomographic techniques for detection of decay in trees. J. Arboric 29: 66–78, https://doi.org/10.48044/jauf.2003.009.Search in Google Scholar

Nursultanov, N., Altaner, C., and Heffernan, W.J.B. (2017). Effect of temperature on electrical conductivity of green sapwood of Pinus radiata (radiata pine). Wood Sci. Technol. 51: 795–809, https://doi.org/10.1007/s00226-017-0917-6.Search in Google Scholar

Nursultanov, N., Heffernan, W.J.B., Altaner, C., and Pang, S. (2020). Anisotropic electrical conductivity of green timber within 20–90 °C temperature range. Wood Sci. Technol. 54: 1181–1196, https://doi.org/10.1007/s00226-020-01212-2.Search in Google Scholar

Rahman, A. and Muktadir, M.G. (2021). SPSS: an imperative quantitative data analysis tool for social science research. Int. J. Res. Innov. Soc. Sci. 5: 300–302, https://doi.org/10.47772/ijriss.2021.51012.Search in Google Scholar

Ruan, X.G. (2005). Wood physics. China Forestry Publishing Press, Beijing, China.Search in Google Scholar

Shigo, A.L., Shortle, W.C., and Ochrymowych, J. (1977). Detection of active decay at ground line in utility poles. Forest 2: 77.Search in Google Scholar

Song, Z.C., Lin, Y.Z., and Gu, G.B. (1994). The influence of wood cell activity on moisture content measurement by electric resistance. J. Northeast For. Univ. 22: 113–116. (In Chinese).Search in Google Scholar

Stamm, A.J. (1927). The electrical resistance of wood as a measure of its moisture content. Ind. Eng. Chem. 19: 1021–1025, https://doi.org/10.1021/ie50213a022.Search in Google Scholar

Stamm, A.J. (1929). The fiber-saturation point of wood as obtained from electrical conductivity measurements. Ind. Eng. Chem., Anal. Ed. 1: 94–97, https://doi.org/10.1021/ac50066a021.Search in Google Scholar

Van Blokland, J. and Adamopoulos, S. (2022). Electrical resistance characteristics of thermally modified wood. Eur. J. Wood Prod. 80: 749–752, https://doi.org/10.1007/s00107-022-01813-7.Search in Google Scholar

Wang, Y.T., Xu, H.D., Wang, L.H., Wang, X.L., and Sun, Y.T. (2016). Effects of environmental temperature on the electrical resistance on the cross section in standing tree. J. Nanjing For. Univ. (Nat. Sci. Ed.) 40: 143–148. (In Chinese).Search in Google Scholar

Wu, H.Q., Zhou, Q.Y., and Liu, H.L. (2008). Temporal and spatial monitoring of water distribution and migration in a tree trunk using electrical resistivity tomography. Pro. Geophys. 23: 1310–1316. (In Chinese).Search in Google Scholar

Wu, H.Q., Zhou, Q.Y., Liu, H.L., and Tang, M.J. (2009). Application of electrical resistivity tomography in studying water uptake process in tree trunk. Chin. J. Ecol. 28: 350–356. (In Chinese).Search in Google Scholar

Xu, H.D. and Wang, L.H. (2012). Determining the states of water and its fraction in frozen Populus ussuriensis and Pinus koraiensis green timbers. Sci. Silv. Sin. 48: 139–143. (In Chinese).Search in Google Scholar

Xu, S., Zhou, Q.Y., and Liu, H.L. (2006). Application of ERT method in monitoring tree water movement. J. Guilin Univ. Technol. 26: 347–352. (In Chinese).Search in Google Scholar

Yue, X.Q., Wang, L.H., Liu, Z.X., Wang, X.L., and Rong, B.B. (2016). Electrical resistance tomography and stress wave tomography to quantitatively detect wood decay under different moisture contents. J. Fujian Agric. For. Univ. North-East For. Univ. 45: 593–598. (In Chinese).Search in Google Scholar

Yue, X.Q., Wang, L.H., Wang, X.L., Rong, B., Ge, X., Liu, Z., and Chen, Q. (2017). Quantitative detection of internal decay degree for standing trees based on three NDT methods: electrical resistance tomography, stress wave imaging and resistograph techniques. Sci. Silv. Sin. 53: 138–146. (In Chinese).Search in Google Scholar

Yue, X.Q., Wang, L.H., Shi, X.L., Xu, M., and Zhu, Z. (2018). Investigations on the effects of seasonal temperature changes on the electrical resistance of living trees. Forests 9: 550, https://doi.org/10.3390/f9090550.Search in Google Scholar

Zheng, Z., Ma, Y., Cao, X., Wu, Z., and Bai, J. (2024). Maximum efficiency tracking of underwater wireless power transmission system based on dynamic coupling coefficient estimation. Prog. Electromagn. Res. C 147: 117–125, https://doi.org/10.2528/pierc24071512.Search in Google Scholar
Received: 2025-03-24
Accepted: 2025-11-12
Published Online: 2025-12-17
Published in Print: 2026-01-23

© 2025 the author(s), published by De Gruyter, Berlin/Boston

This work is licensed under the Creative Commons Attribution 4.0 International License.

Articles in the same Issue

  1. Frontmatter
  2. Wood Chemistry
  3. Characterization of the bark of Plathymenia reticulata Benth. from the Brazilian Cerrado in the context of bioprospecting natural products for multiple uses
  4. Wood Physics/Mechanical Properties
  5. Visual strength grading and bending strength class assignment for Spanish provenance shining gum [Eucalyptus nitens (Deane and Maiden) Maiden]
  6. Interactive effects of temperature, moisture content, and decay on the electrical resistance of Populus ussuriensis wood
  7. Wood Technology/Products
  8. Comparison of termite resistance of wood modified with lignin cleavage products-phenol-formaldehyde and phenol-formaldehyde resins against subterranean and drywood termites
  9. Wood Science — Non-Tree Plants
  10. Study on the anatomical structure and chemical characteristics of several common bamboo rhizomes
  11. Anatomical and density properties of Oxytenanthera abyssinica culm
  12. Rapid extraction of lignin from bamboo with high yield by microwave-assisted choline chloride-formic acid method
https://doi.org/10.1515/hf-2025-0025
Keywords for this article
decay wood; wood nondestructive testing; wood electrical resistance; temperature; moisture content
Creative Commons
BY 4.0

Articles in the same Issue

  1. Frontmatter
  2. Wood Chemistry
  3. Characterization of the bark of Plathymenia reticulata Benth. from the Brazilian Cerrado in the context of bioprospecting natural products for multiple uses
  4. Wood Physics/Mechanical Properties
  5. Visual strength grading and bending strength class assignment for Spanish provenance shining gum [Eucalyptus nitens (Deane and Maiden) Maiden]
  6. Interactive effects of temperature, moisture content, and decay on the electrical resistance of Populus ussuriensis wood
  7. Wood Technology/Products
  8. Comparison of termite resistance of wood modified with lignin cleavage products-phenol-formaldehyde and phenol-formaldehyde resins against subterranean and drywood termites
  9. Wood Science — Non-Tree Plants
  10. Study on the anatomical structure and chemical characteristics of several common bamboo rhizomes
  11. Anatomical and density properties of Oxytenanthera abyssinica culm
  12. Rapid extraction of lignin from bamboo with high yield by microwave-assisted choline chloride-formic acid method
Sign up now to receive a 20% welcome discount
Institutional Access
How does access work?
Winner of the OpenAthens UX Award 2026
Winner of the OpenAthens UX Award 2026
Have an idea on how to improve our website?
Please write us.
© 2026 De Gruyter Brill
Downloaded on 30.3.2026 from https://www.degruyterbrill.com/document/doi/10.1515/hf-2025-0025/html
Scroll to top button