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Frequency-dependent viscoelastic properties of Chinese fir (Cunninghamia lanceolata) under hygrothermal conditions. Part 1: moisture adsorption

  • Tianyi Zhan , Jiali Jiang , Jianxiong Lu EMAIL logo , Yaoli Zhang and Jianmin Chang
Published/Copyright: March 30, 2019
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Holzforschung
From the journal Holzforschung Volume 73 Issue 8

Abstract

To elucidate the frequency-dependent viscoelasticity of wood under a moisture non-equilibrium state, changes in stiffness and damping as a function of frequency were investigated during the moisture adsorption process. The moisture adsorption processes were carried out at six temperatures (30–80°C) and three relative humidity levels (30, 60 and 90% RH). During the moisture adsorption process, the wood stiffness decreased, and damping increased with the increment of moisture content (MC). Regardless of the moisture adsorption time, the wood stiffness increased, and damping decreased with the increasing testing frequency. Based on the re-organized Williams-Landel-Ferry (WLF) model, the time-moisture superposition (TMS) relation was assumed to be applicable for developing a master curve of wood stiffness during the moisture adsorption process. The frequency ranges of the stiffness master curves spanned from 16 to 23 orders of magnitude at temperatures ranging from 30 to 80°C. However, the TMS relation was not able to predict the wood damping properties during the moisture adsorption process due to the multi-relaxation system of the wood and the non-proportional relationship between free volume and MC at transient moisture conditions.

Keywords: adsorption; frequency-dependent; hygrothermal conditions; moisture state; polymer relaxation; time-moisture superposition; viscoelasticity

Introduction

In the processing and manufacturing operations in the wood and paper industries, wood and fiber materials are often under the impact of dynamic loadings, such as cutting, pulping and pressing (Marchal et al. 2009; Zhou et al. 2010; Navi and Pizzi 2015; Pichler et al. 2018). Besides these dynamic loadings, ambient temperature and relative humidity (RH) continuously change during these operations. In other words, wood or fiber materials are often subject to combined “thermo-hygro-mechanical (THM)” interactions (Li et al. 2018a; Liu 2018; Liu et al. 2018; Wang et al. 2018).

Under the dynamic loading, wood exhibits both elastic behavior and viscous behavior that is termed as viscoelasticity. The viscoelastic properties of wood are associated with the amplitude and frequency of the dynamic loading, ambient temperature and wood moisture content (MC). Understanding the effect of THM interactions on the viscoelasticity of wood is helpful for optimizing manufacturing operations and improving the quality of wood and its products.

Dynamic mechanical analysis (DMA) is an effective tool for describing the viscoelastic properties of wood (Menard 2002). DMA applies an oscillating force to a wood sample and analyzes the sample’s response to the applied force. The most common use of DMA is the temperature-scan technique, which could enhance the understanding of the organization and properties of in situ wood polymers (Sun et al. 2007). During the temperature-scan process, several relaxation peaks are observed, in which a major relaxation peak occurs over a range of 70–200°C. This relaxation peak is attributed to the glass transition of lignin in wet or water-saturated state of wood (Salmén 1984; Olsson and Salmén 1997; Laborie et al. 2004; Placet et al. 2007; Guo et al. 2017). The glass transition temperature (Tg) of lignin moves to lower temperature ranges at higher wood MCs and is related to the testing frequency and the lignin structure (Olsson and Salmén 1997). Regardless of the wood MC (dry, moist or water-saturated state), another relaxation peak could be found due to the motions of methylol groups in amorphous regions of the wood cell wall at around −90°C (Kimura and Nakano 1976; Sugiyama and Norimoto 1996; Obataya et al. 2001).

In addition, Backman and Lindberg (2001), Jiang and Lu (2008) and Li et al. (2018b) reported a small peak at around room temperature or even below ambient temperatures when the wood MC ranged from around 3 to 14.1%. Li et al. (2018b) assumed that this small peak was attributed to the melting of frozen water. Backman and Lindberg (2001) and Jiang and Lu (2008) assumed that the small peak was attributed to the glass transition of hemicellulose. Paiva and Magalhães (2018) reported a similar result in nearly dried cork and assumed that the damping change between 10 and 25°C might be associated with the melting of suberin in cork.

The viscoelastic changes during temperature scan could also be observed over a sufficiently wide frequency range when the temperature remains constant. Plot of viscoelastic parameter vs. frequency appears as a reverse-like temperature scan (Menard 2002). The collections of frequency-dependent viscoelastic data as a function of temperature are also commonly obtained for exploring the effect of frequency on temperature-driven changes in the material (Amada and Lakes 1997; Jiang et al. 2010; Zhang et al. 2012; Li et al. 2015). From the viewpoint of molecular chain movement, smaller-scale molecular movements are involved at higher testing frequencies. Specifically, high stiffness could be obtained at high frequencies, while it could also be found at low temperatures. Hence, the changes in stiffness by altering the temperature are somewhat similar to those caused by frequency changes. The time-temperature superposition principle (TTSP) is widely applied to predict the rheological properties of polymers at wide ranges of frequency or time (Chevali et al. 2009; Townsend et al. 2011; Chowdhury and Frazier 2013; Sirk et al. 2013; Tan and Guo 2013; Arbe et al. 2016). According to TTSP, a master curve is obtained by shifting a series of multiplexed frequency scans relative to a reference curve. Various models have been developed for the shift. The most commonly used models are the Williams-Landel-Ferry (WLF) model and the Arrhenius equation (Ferry 1980; Menard 2002). The constructed master curve covers a range much greater than that of the original data. Based on the constructed master curve, the mechanical properties at long-term or extreme-temperature conditions could be predicted. Salmén (1984) firstly demonstrated that TTSP was applicable to the water-saturated wood around Tg of lignin. Numerous wood-TTSP studies have been carried out using both static and dynamic methods (Samarasinghe et al. 1994; Lenth and Kamke 2001; Laborie et al. 2004; López-Suevos and Frazier 2005, 2006; Placet et al. 2007; Sun and Frazier 2007; Dlouhá et al. 2009; Peng et al. 2017; Wang et al. 2017; Hsieh and Chang 2018). TTSP is effective for analyzing the in situ softening of lignin and for improving the understanding of structure/property relationships in plasticized lignocellulose (Salmén 1984; Laborie et al. 2004; Chowdhury and Frazier 2013). However, the applicability of TTSP to wood is still controversial, and some researchers stated that it was applicable only under limited conditions (Salmén 1984; Kelley et al. 1987; Nakano 1995, 2013). Salmén (1984) stated that the WLF model was applicable for water-saturated wood when the temperature was within Tg to Tg+30°C. Sun and Frazier (2007) found that TTSP was valid in the range of 100–170°C for dry wood that received a prior 30-min thermal treatment. For the ethyl formamide- or ethylene glycol-treated wood, the applicable temperature was from Tg to Tg+85°C (Kelley et al. 1987) or Tg to Tg+40°C (Laborie et al. 2004), respectively. The limited conditions of applicability of TTSP to wood viscoelasticity were because wood is a polymer blend with multiple transition zones, and different components have different temperature-dependent performances. On the other hand, wood MC changes as a result of the temperature change. The increase of both MC and temperature is known to reduce the stiffness of wood and other polymers. Using an analogous approach to TTSP, several authors suggested that viscoelastic measurements of polymers at different MCs (or RH levels) can be rationalized on the basis of a time- moisture superposition (TMS) relation (Chu and Robertson 1994; Zhou et al. 2001; Ishisaka and Kawagoe 2004; Mano 2008; Patankar et al. 2008; Fabre et al. 2018).

In actual manufacturing and processing operations, wood undergoes varieties of MC (i.e. moisture adsorption or desorption). Hence, exploring the TMS relations on wood viscoelasticity during the moisture variation process is necessary for further explaining the effect of THM interactions on the rheological response of wood. The present study set out to evaluate the frequency-dependent viscoelasticity of Chinese fir (Cunninghamia lanceolata [Lamb.] Hook.) during the moisture adsorption process. The influences of ambient temperature and RH on stiffness/damping as a function of frequency were investigated. Furthermore, the applicability of TMS on wood stiffness and damping was evaluated during the moisture adsorption process.

Materials and methods

Materials

Specimens were prepared from Chinese fir heartwood with a dimension of 60×12×2.5 mm3 (L×R×T). Before testing, all specimens were conditioned in a sealed container over P2O5, which provided a RH of 0%. The corresponding MC of the conditioned specimens was 0.6%.

Viscoelasticity measurement

A DMA Q800 (TA Instruments, New Castle, DE, USA) was used to automatically examine the elastic (storage modulus E′) and viscoelastic (loss modulus E′′) responses. The DMA was equipped with a humidity accessory which controlled the RH precisely by modulating the mixture of dry nitrogen and saturated moisture. A dual-cantilever bending mode with a span of 35 mm was selected. The specimens were clamped on the radial surfaces and the bending occurred in the tangential direction. The sinusoidal bending displacement was 15 μm in a series of frequencies scanned from 50 to 1 Hz, and the corresponding strain was 0.032–0.034% in the temperature ranging from 30 to 80°C. All tests were carried out within the linear viscoelastic region, where the strain response was directly proportional to the mechanical input (stress), and the reproducible data were expected to accurately reflect the relationships between molecular structure and viscoelastic behavior (Sun et al. 2007; Kaboorani and Blanchet 2014).

The specimens were mounted in the testing chamber and pre-heated at 0% RH for 30 min at six constant temperatures (30, 40, 50, 60, 70 and 80°C). After the pre-heating period, the RH was ramped up from 0 to 30 (Figure 1a), 60 (Figure 1b) or 90% RH (Figure 1c), respectively, with a ramping rate of 2% RH min−1. The RH was then kept isohume for 240 min. MC was measured on a dry basis by weighing specimens before and after the moisture adsorption. To monitor the MC changing trend, some separate specimens were also tested under the same hygrothermal conditions, but only until the time points marked with the frequency scan symbols in Figure 1 (i.e. termination of RHramp, 60 min under RHisohume and 120 min under RHisohume). The viscoelastic data are presented for the 240 min isohume period for the three RH levels. Three measurements were performed for each condition.

Figure 1: Experimental setup for the frequency scan tests.a: 30% RH, b: 60% RH, c: 90% RH.
Figure 1:

Experimental setup for the frequency scan tests.

a: 30% RH, b: 60% RH, c: 90% RH.

Results and discussion

Storage modulus

Figure 2 shows the typical changes in frequency-dependent E′ during the moisture adsorption process when the temperatures were 30 and 80°C. The viscoelastic values at the other temperatures are shown in Supplementary Table S1. Regardless of temperature or adsorption time, an increase in E′ was observed with the increment of frequency. The wood exhibited more elastic-like behavior as the testing frequency increased, and E′ tended to slope upward as frequency increased (Menard 2002). At higher temperatures, larger amounts of moisture adsorption were found under these conditions (Figure 3). As in our previous study (Zhan et al. 2018), none of the specimens reached the equilibrium MC, and not even at the end of the 240-min RHisohume period. With the extension of adsorption time, E′ decreased as a consequence of increasing MC. The reduction in wood stiffness was attributed to the plasticization effect of moisture (Li et al. 2018c). At higher temperatures or humidity levels, greater decrements of E′ could be found because the plasticization effect was intensified at hygrothermal conditions.

Figure 2: Influence of frequency on storage modulus at five moisture adsorption time points: ◊, experimental initiation; □, termination of RHramp; ∆, 60 min under RHisohume; ×, 120 min under RHisohume; ○, 240 min under RHisohume (storage modulus scaling varied at 30 and 80°C).
Figure 2:

Influence of frequency on storage modulus at five moisture adsorption time points: ◊, experimental initiation; □, termination of RHramp; ∆, 60 min under RHisohume; ×, 120 min under RHisohume; ○, 240 min under RHisohume (storage modulus scaling varied at 30 and 80°C).

Figure 3: Changes in the moisture content at five moisture adsorption time points: ◊, experimental initiation; □, termination of RHramp; ∆, 60 min under RHisohume; ×, 120 min under RHisohume; ○, 240 min under RHisohume.
Figure 3:

Changes in the moisture content at five moisture adsorption time points: ◊, experimental initiation; □, termination of RHramp; ∆, 60 min under RHisohume; ×, 120 min under RHisohume; ○, 240 min under RHisohume.

It was found that the increasing temperature and MC had similar effects on the stiffness reduction of Chinese fir (Zhan et al. 2018). According to TTSP, E′ at a given temperature (T) is the same as that of another reference temperature (Tr), providing that the frequency (f) axis is shifted as (Menard 2002):

(1)E′ (T, log f)=E′ (Tr, log aT/Tr+log f)

where log aT/Tr is the horizontal shift factor. The most commonly used WLF model for the shifting is given as (Williams et al. 1957):

(2)log aT/Tr=−C1(T−Tr)C2+(T−Tr)

where C1 and C2 are material constants, fitted by the relationship between T and Tr. Sometimes, the vertical shift should also be considered for obtaining a smooth master curve. The vertical shift factor may be associated with the temperature dependency of the wood density (Wang et al. 2017). Taking into account that moisture has a similar implication as temperature during the change in wood stiffness, it was rationalized to display a TMS relationship based on TTSP. Hence, Equation (1) may be rewritten as:

(3)E′ (MC, log f)=E′ (MCr, log aMC/MCr+log f)

where log aMC/MCr is the horizontal shift factor with respect to a reference value of the MC (MCr). log aMC/MCr is calculated by a re-organized WLF model:

(4)log aMC/MCr=−C′1(MC−MCr)C′2+(MC−MCr)

where C′1 and C′2 are material constants, fitted by the relationship between MC and MCr. In addition, the vertical shift is associated with the moisture dependency of the wood density, which is not discussed in this study.

To investigate the possibility of TMS, the evolution of E′ at all the three RH levels is displayed together as a function of frequency in the left side of Figure 4. In total, there were 13 frequency sweeps at each given temperature. At each RH level, four frequency sweeps were obtained corresponding to four time points (termination of RHramp, 60 min under RHisohume, 120 min under RHisohume and 240 min under RHisohume). Additionally, one more frequency sweep was obtained based on the average value of the frequency-dependent E′ at the experimental initiation, and this frequency sweep was regarded as the reference curve with the MC of 0.6%. Equation (3) was used to horizontally shift a series of MC-dependent E′ to the reference curve for constructing the superposition master curve. The master curve was established and is presented in the right side of Figure 4. Smooth master curves could be developed at all hygrothermal temperatures. From 30 to 80°C, the frequency ranges of the master curves spanned from 16 to 23 orders of magnitude. With the increasing temperature, the plasticization effect was accelerated and intensified (Dlouhá et al. 2009), which was seen as a broadened frequency range.

Figure 4: Relations of wood storage modulus and frequency (left column) at different adsorption times and master curve as a function of frequency (right column) (storage modulus scaling varied from 30 to 80°C).
Figure 4:

Relations of wood storage modulus and frequency (left column) at different adsorption times and master curve as a function of frequency (right column) (storage modulus scaling varied from 30 to 80°C).

The TMS relation of viscoelasticity has also been demonstrated for other materials, including cement (Chu and Robertson 1994)), glass-fiber reinforced epoxy pipe (Yao and Ziegmann 2006), chitosan (Mano 2008), proton exchange membrane (Patankar et al. 2008) and polyamide 6,6 (Fabre et al. 2018). The shift factors of the amorphous thermoplastics commonly exhibited non-linear temperature (or moisture) dependencies, while the experimental value of log aMC/MCr, as a function of MC, was highly linear in this study (Figure 5). The Arrhenius (linear) behavior has also been reported for ethylene glycol and N,N-dimethylformamide plasticized wood (Laborie et al. 2004; Chowdhury and Frazier 2013), and attributed to intermolecular cooperativity within polymeric materials. When the wood MC is around 0.6%, a slight relaxation occurs at 30–80°C (Sun et al. 2007; Jiang et al. 2008), which may explain why log aMC/MCr and MC exhibited linear relationships in Figure 5. Decreasing log aMC/MCr could be observed with increasing MC. The shift factor represents the ratio of relaxation time at a given MC to that at the reference MC (0.6%). The greater absolute value of log aMC/MCr means less relaxation time of molecular movement. At the end of the 240-min RHisohume period (90%) when the temperature was 30 or 80°C, the absolute value of log aMC/MCr was 11.7 or 15.8, respectively. The increment of water molecules speeded up the movement pace of amorphous polymers within the wood cell walls, and shortened the relaxation times.

Figure 5: Shift factor log aMC/MCr$\log \ {a_{MC/M{C_{\rm r}}}}$ relationships with moisture content from the master curves and fit of the shift factor log aMC/MCr$\log \ {a_{MC/M{C_{\rm r}}}}$ to the re-organized WLF model.
Figure 5:

Shift factor log aMC/MCr relationships with moisture content from the master curves and fit of the shift factor log aMC/MCr to the re-organized WLF model.

Loss modulus

Figure 6 shows the typical changes in frequency-dependent E′′ during the moisture adsorption processes when the temperatures were 30 and 80°C. The E′′ values at the other temperatures are shown in Supplementary Table S1. During the moisture adsorption process, the increment of E′′ was found with the increment of adsorption time regardless of the testing frequency. Similar to Equation (3), E′′ may be re-organized as:

Figure 6: Influence of frequency on loss modulus at five moisture adsorption time points: ◊, experimental initiation; □, termination of RHramp; ∆, 60 min under RHisohume; ×, 120 min under RHisohume; ○, 240 min under RHisohume (loss modulus scaling varied at 30 and 80°C).
Figure 6:

Influence of frequency on loss modulus at five moisture adsorption time points: ◊, experimental initiation; □, termination of RHramp; ∆, 60 min under RHisohume; ×, 120 min under RHisohume; ○, 240 min under RHisohume (loss modulus scaling varied at 30 and 80°C).

(5)E′′ (MC, log f)=E′′ (MCr, log aMC/MCr+log f)

In the left side of Figure 7, 13 frequency sweeps of E′′ are displayed as well as the frequency-dependent E′ in Figure 4. Based on Equation (5), master curves of E′′ in the right side of Figure 7 were constructed. Master curves of E′′ with scattered plots are shown at all the hygrothermal temperatures. The non-smooth behavior of E′′ might illustrate the non-applicability of the TMS relation to wood damping. From the point of view of composite materials, the wood cell wall is a multi-phase composite of elastic fibrils of cellulose and a viscoelastic matrix of amorphous lignin and hemicelluloses. The TMS relation did not apply for this multi-phase system because different components did not have the same moisture-dependent relaxation behavior (Nakano 1995, 2013).

Figure 7: Relations of wood loss modulus and frequency at different adsorption times (left column) and master curve as a function of frequency (right column) (loss modulus scaling varied from 30 to 80°C).
Figure 7:

Relations of wood loss modulus and frequency at different adsorption times (left column) and master curve as a function of frequency (right column) (loss modulus scaling varied from 30 to 80°C).

According to Ferry (1980), the WLF model can be applied when the free volume within a polymer increases proportionally to its MC. While, during the moisture adsorption process, the free volume is not only influenced by the amount of moisture, but also associated with the transient state of the moisture within the wood cell wall (Zhan et al. 2016). The free volume within the wood cell wall forms localized stresses that unequally disturbed the equilibrium packing of polymer molecules, resulting in more energy dissipation (Takahashi et al. 2004). The non-proportional relationship between free volume and MC may be another reason for explaining why the TMS relation failed to predict the wood damping properties.

Conclusions

  1. The stiffness decreased and the damping increased with increasing time of moisture adsorption under all testing frequencies of Chinese fir wood. An increase in stiffness was observed with the increase of frequency, irrespective of MC.

  2. Based on the re-organized WLF model, the TMS relation was applicable for developing master curves of wood stiffness. The frequency ranges of the stiffness master curves spanned from 16 to 23 orders of magnitude at temperatures ranging from 30 to 80°C.

  3. The TMS relation was not able to predict the wood damping properties during the moisture adsorption process due to the multi-relaxation system of the wood. The non-proportional relationship between free volume and MC at transient moisture conditions could also explain why the TMS relation failed to predict the wood damping properties.

Funding source: National Natural Science Foundation of China

Award Identifier / Grant number: 31700487

Funding source: Natural Science Foundation of Jiangsu Province

Award Identifier / Grant number: BK20170926

Funding source: Nanjing Forestry University

Award Identifier / Grant number: CX2017002

Funding statement: This work was financially supported by the National Natural Science Foundation of China (Funder Id: http://dx.doi.org/10.13039/501100001809, no. 31700487), the Natural Science Foundation of Jiangsu Province (CN) (Funder Id: http://dx.doi.org/10.13039/ 501100004608, no. BK20170926), the Innovation Fund for Young Scholars of Nanjing Forestry University (CX2017002) and the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD). Tianyi Zhan would like to gratefully acknowledge the financial support from the Jiangsu Provincial Government Scholarship Program.

  1. Author contributions: All the authors have accepted responsibility for the entire content of this submitted manuscript and approved submission.

  2. Employment or leadership: None declared.

  3. Honorarium: None declared.

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Supplementary Material

The online version of this article offers supplementary material (https://doi.org/10.1515/hf-2018-0208).

Received: 2018-09-15
Accepted: 2019-02-21
Published Online: 2019-03-30
Published in Print: 2019-07-26

©2019 Walter de Gruyter GmbH, Berlin/Boston

Articles in the same Issue

  1. Frontmatter
  2. Original Articles
  3. Influence of tree height on the hydrophilic and lipophilic composition of bark extracts from Eucalyptus globulus and Eucalyptus nitens
  4. Modelling the viscoelastic mechanosorptive behaviour of Norway spruce under long-term compression perpendicular to the grain
  5. Frequency-dependent viscoelastic properties of Chinese fir (Cunninghamia lanceolata) under hygrothermal conditions. Part 1: moisture adsorption
  6. Frequency-dependent viscoelastic properties of Chinese fir (Cunninghamia lanceolata) under hygrothermal conditions. Part 2: moisture desorption
  7. Evolution of extractive composition in thermally modified Scots pine during artificial weathering
  8. Dynamic mechanical analysis (DMA) at room temperature of archaeological wood treated with various consolidants
  9. Visual and machine strength grading of European ash and maple for glulam application
  10. Evaluation of bond strength of cross-laminated LSL specimens under short-span bending
https://doi.org/10.1515/hf-2018-0208
Keywords for this article
adsorption; frequency-dependent; hygrothermal conditions; moisture state; polymer relaxation; time-moisture superposition; viscoelasticity

Articles in the same Issue

  1. Frontmatter
  2. Original Articles
  3. Influence of tree height on the hydrophilic and lipophilic composition of bark extracts from Eucalyptus globulus and Eucalyptus nitens
  4. Modelling the viscoelastic mechanosorptive behaviour of Norway spruce under long-term compression perpendicular to the grain
  5. Frequency-dependent viscoelastic properties of Chinese fir (Cunninghamia lanceolata) under hygrothermal conditions. Part 1: moisture adsorption
  6. Frequency-dependent viscoelastic properties of Chinese fir (Cunninghamia lanceolata) under hygrothermal conditions. Part 2: moisture desorption
  7. Evolution of extractive composition in thermally modified Scots pine during artificial weathering
  8. Dynamic mechanical analysis (DMA) at room temperature of archaeological wood treated with various consolidants
  9. Visual and machine strength grading of European ash and maple for glulam application
  10. Evaluation of bond strength of cross-laminated LSL specimens under short-span bending
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