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18 pages, 2706 KiB

Open AccessArticle

Characterization and Prediction of Mechanical and Chemical Properties of Luanta Fir Wood with Vacuum Hydrothermal Treatment

by Ming-Chi Hsieh, Ke-Chang Hung, Jin-Wei Xu, Yi-Hung Wu, Wen-Shao Chang and Jyh-Horng Wu

Polymers 2023, 15(1), 147; https://doi.org/10.3390/polym15010147 - 28 Dec 2022

Cited by 1 | Viewed by 1907

Abstract

Since the chemical composition of wood is closely related to its mechanical properties, chemical analysis techniques such as near-infrared (NIR) spectroscopy provide a reasonable non-destructive method for predicting wood strength. In this study, we used NIR spectra with principal component analysis (PCA) to reveal that vacuum hydrothermal (VH) treatment causes degradation of hemicellulose as well as the amorphous region of cellulose, resulting in lower hydroxyl and acetyl group content. These processes increase the crystallinity of the luanta fir wood (Cunninghamia konishii Hayata), which, in turn, effectively increases its compressive strength (σ_c,max), hardness, and modulus of elasticity (MOE). The PCA results also revealed that the primary factors affecting these properties are the hemicellulose content, hydroxyl groups in the cellulose amorphous region, the wood moisture content, and the relative lignin content. Moreover, the ratios of performance deviation (RPDs) for the σ_c,max, shear strength (σ_s,max), hardness, and modulus of rupture (MOR) models were 1.49, 1.24, 1.13, and 2.39, indicating that these models can be used for wood grading (1.0 < RPD < 2.5). Accordingly, NIR can serve as a useful tool for predicting the mechanical properties of VH-treated wood. Full article

(This article belongs to the Special Issue Advances in Wood-Based Materials and Wood Polymer Composites)

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17 pages, 5872 KiB

Open AccessArticle

Characterization and Prediction of Physical Properties of Luanta Fir Wood with Vacuum Hydrothermal Treatment

by Ming-Chi Hsieh, Ke-Chang Hung, Jin-Wei Xu, Wen-Shao Chang and Jyh-Horng Wu

Polymers 2022, 14(20), 4374; https://doi.org/10.3390/polym14204374 - 17 Oct 2022

Cited by 2 | Viewed by 1881

Abstract

This study used the luanta fir (Cunninghamia konishii Hayata) wood, one of the most used wood construction and building materials in Taiwan, as specimens to examine the impact of different conditions of vacuum hydrothermal (VH) treatment on the physical properties of this wood. A prediction model for these properties was created using a nondestructive spectroscopy technique. The test results revealed that the mass loss, moisture exclusion efficiency, anti-swelling efficiency, color difference, and surface contact angle of the VH-treated wood all increased under increasing heat treatment temperature and time. Moreover, the use of near-infrared (NIR) spectroscopy in creating the prediction model for the physical properties of the VH-treated luanta fir wood revealed that the ratios of performance to deviation (RPD) for mass loss, equilibrium moisture content, and color difference were all above 2.5, indicating a high prediction accuracy. These results suggested that an NIR spectrometer can serve as a useful instrument for the accurate prediction of the physical properties or for controlling the quality of VH-treated wood. Full article

(This article belongs to the Special Issue Advances in Wood-Based Materials and Wood Polymer Composites)

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14 pages, 2121 KiB

Open AccessArticle

Application of Models to Predict Stand Volume, Aboveground Biomass Accumulation, and Carbon Storage Capacity for a Konishii Fir (Cunninghamia konishii Hayata) Plantation in Central Taiwan

by Minhas Hussain, Zheng-Rong Lin, Tian-Ming Yen and Chih-Chuan Lin

Forests 2021, 12(10), 1406; https://doi.org/10.3390/f12101406 - 15 Oct 2021

Cited by 6 | Viewed by 2973

Abstract

Konishii fir (Cunninghamia konishii Hayata) is an important conifer in Taiwan. The purpose of this study was to predict stand volume (V), aboveground biomass accumulation (AGB), and aboveground carbon storage (AGCST) for a Konishii fir plantation. This study was located at the Huisun Experimental Forest Station of Nantou County located in central Taiwan. Four sample plots, each with an area of 0.05 ha, were installed and surveyed from 29 June to 2 July 2020. Two models, the diameter distribution model (DDM) and allometric model (AM), were used to predict V, AGB, and AGCST. Each item predicted by these two models was compared by the paired sample t-test. We employed the Weibull function to quantify stand diameter distribution and this function can effectively quantify diameter distribution, because all plots passed the examination by the Kolmogorov–Smirnov test (non-significant). Therefore, the Weibull function was suitable for developing the DDM. The predicted V, AGB, and AGCST were 538.43 ± 140.52 m³ ha⁻¹, 203.25 ± 52.79 Mg ha⁻¹, and 100.85 ± 26.30 Mg ha⁻¹ by DDM; and 555.90 ± 145.42 m³ ha⁻¹, 209.10 ± 51.25 Mg ha⁻¹, and 103.78 ± 25.51 Mg ha⁻¹ by AM, respectively. Each item was insignificantly different between DDM and AM, indicating similarity in results for both predictions. Meanwhile, using DDM is advantageous, as it can provide more yield information in diameter classes; therefore, this approach was recommended for yield prediction of the Konishii fir plantation. Full article

(This article belongs to the Topic Estimating Carbon Stocks in Forest Ecosystems: From Allometric Equations to Remote Sensing-Based Methods)

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