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Forests
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Advances in Wood Chemical Traits
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Advances in Wood Chemical Traits

A special issue of Forests (ISSN 1999-4907). This special issue belongs to the section "Wood Science and Forest Products".

Deadline for manuscript submissions: 31 May 2024 | Viewed by 5903

Special Issue Editors

Prof. Dr. Yongfeng Luo

E-Mail Website
Guest Editor
Hunan Province Key Laboratory of Materials Surface & Interface Science and Technology, College of Science, Central South University of Forestry and Technology, Changsha 410004, China
Interests: wood science and technology; biomass materials; biomass carbon functional materials
Dr. Yudong Li

E-Mail Website
Guest Editor
Key Laboratory of Bio-Based Material Science and Technology, Ministry of Education, Northeast Forestry University, Harbin 150040, China
Interests: wood functionalization; bionic; nano materials design
Special Issues, Collections and Topics in MDPI journals
Dr. Xi Yang

E-Mail Website
Co-Guest Editor
College of Materials Science and Engineering, Central South University of Forestry and Technology, Changsha 410004, China
Interests: wood science and technology; biomass carbon-based materials

Special Issue Information

Dear Colleagues,

Wood is mainly composed of cellulose, hemicellulose, and lignin. As a natural polymer composite material, the chemical properties of wood are the result of its chemical composition, complementing each other in nature and function. At present, there is a more comprehensive understanding of the basic chemistry of wood. Moreover, with the development of advanced characterization methods and modern measurement methods, the research in this field has had a significant impact. This makes it possible to continually expand our comprehensive understanding of wood, which can help us better understand materials, enabling us to apply wood and wood diffractions to areas such as ecology, environmental management, carbon sinks, and energy storage.

This Special Issue focuses on innovative methods of studying the chemical properties of wood, describes chemical properties that are not yet known or understood, discusses issues and questions related to the separation, purification, and application of typical compositions in all types of wood, and develops new materials and technologies derived from the microstructure and chemical properties of wood. We highly encourage authors from all relevant fields to submit original and review articles for this Special Issue.

Prof. Dr. Yongfeng Luo
Dr. Yudong Li
Dr. Xi Yang
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Forests is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • wood chemical properties
  • chemical wood modification
  • wood modification processes
  • wood-based materials
  • sustainability
  • bionic wood
  • wood carbon-based materials
  • wood science and technology

Published Papers (5 papers)

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Research

14 pages, 4223 KiB  
Article
Transcriptome Analysis Provides Insights into Lignin Biosynthesis in Styrax tonkinensis Branches
by Chao Han, Qiunuan Xu, Hong Chen, Huiwu Peng and Fangyuan Yu
Forests 2024, 15(4), 601; https://doi.org/10.3390/f15040601 - 26 Mar 2024
Viewed by 469
Abstract
Approximately 12% of China’s papermaking raw materials are derived from wood, while the majority are sourced from branches. Styrax tonkinensis is a more desirable species for pulpwood in subtropical ultra-short rotations, whose branches are prone to breakage. Lignin has a significant impact on [...] Read more.
Approximately 12% of China’s papermaking raw materials are derived from wood, while the majority are sourced from branches. Styrax tonkinensis is a more desirable species for pulpwood in subtropical ultra-short rotations, whose branches are prone to breakage. Lignin has a significant impact on wood quality and pulping yield, and the growth process influences lignin biosynthesis. To explore the lignin biosynthesis pathway in S. tonkinensis, we determined the lignin content in the current-year and biennial branches on 20 July, 20 September, and 20 October and analyzed the transcriptome sequencing results. It was concluded that the lignin content showed an increasing trend in the current-year branches (182.26, 206.17, and 213.47 mg/g, respectively), while that in the biennial branches showed a decrease in the samples taken in October, without significant difference (221.77, 264.43, and 261.83 mg/g, respectively). The transcriptome sequencing results showed that 91,513 unigenes were spliced with a total length of 92,961,618 bp. KEGG pathway analysis indicated that the upregulated DEGs were mainly enriched in the phenylpropanoid biosynthesis pathway. Our study suggested that CCoAOMT, COMT, peroxidase, and F5H may serve as key enzymes regulating lignin synthesis in branches of S. tonkinensis, thereby influencing the lignin content. Full article
(This article belongs to the Special Issue Advances in Wood Chemical Traits)
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18 pages, 7659 KiB  
Article
SRTM DEM Correction Based on PSO-DBN Model in Vegetated Mountain Areas
by Xinpeng Sun, Cui Zhou, Jian Xie, Zidu Ouyang and Yongfeng Luo
Forests 2023, 14(10), 1985; https://doi.org/10.3390/f14101985 - 01 Oct 2023
Viewed by 858
Abstract
The Shuttle Radar Topography Mission (SRTM) digital elevation model (DEM) is extensively utilized in various fields, such as forestry, oceanography, geology, and hydrology. However, due to limitations in radar side-view imaging, the SRTM DEM still contains gaps and anomalies, particularly in areas with [...] Read more.
The Shuttle Radar Topography Mission (SRTM) digital elevation model (DEM) is extensively utilized in various fields, such as forestry, oceanography, geology, and hydrology. However, due to limitations in radar side-view imaging, the SRTM DEM still contains gaps and anomalies, particularly in areas with an intricate topography, like forests. To enhance the accuracy of the SRTM DEM in forested regions, commonly employed approaches include regression analysis and artificial neural networks (ANN). Nevertheless, existing regression methods struggle to accurately capture the intricate nonlinear relationship between the error and influencing factors. Additionally, traditional ANN models are susceptible to overfitting, resulting in subpar accuracy. Deep Belief Network (DBN) is a highly precise algorithm in deep learning. However, the intricate combination of hyperparameters often leads to limited generalization ability and model robustness when correcting DEM. The present study proposes an error prediction model based on the DBN optimized by Particle Swarm Optimization (PSO) for SRTM DEM correction. By utilizing the PSO algorithm, we aim to identify the optimal combination of hyperparameters of DBN, including the number of neurons in the hidden layer and the learning rates. The experiment focuses on two regions in Hunan Province, China, characterized by abundant vegetation cover. The reference data utilized for comparison is ICESat/GLAS data. The experimental results demonstrate that the mean error (ME) and root mean square error (RMSE) of the SRTM DEM corrected by the proposed algorithm in these two regions are significantly reduced by 93.5%–96.0% and 21.5%–23.5%, respectively. Moreover, there is an improvement of over 26.1% in accuracy within complex terrain areas. Specifically, in broadleaf forest, the PSO-DBN method exhibits a remarkable accuracy improvement of 26.2%, while the DBN-corrected SRTM DEM shows an improvement of 15.3%. In coniferous forest, the PSO-DBN method achieves an accuracy improvement of 14.8%, whereas the DBN-corrected SRTM DEM demonstrates a gain of 5.8%. The approach provides a more effective and robust tool for correcting SRTM DEM or other similar DEMs over vegetated mountain areas. Full article
(This article belongs to the Special Issue Advances in Wood Chemical Traits)
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14 pages, 3079 KiB  
Article
Fluorescence Properties of Pterocarpus Wood Extract
by Renjie Li, Junyuan Li, Jiangtao Shi, Yongyue Zhang, Yuxin Sun, Yuxi Chen and Zhipeng Liu
Forests 2023, 14(6), 1094; https://doi.org/10.3390/f14061094 - 25 May 2023
Cited by 1 | Viewed by 1034
Abstract
The water immersion of Pterocarpus wood produces strong blue fluorescence, which comes from the extract. The fluorescence contained in the extract is of interest for the identification of Pterocarpus wood. We conducted an investigation into the extraction solution of Pterocarpus wood and analyzed [...] Read more.
The water immersion of Pterocarpus wood produces strong blue fluorescence, which comes from the extract. The fluorescence contained in the extract is of interest for the identification of Pterocarpus wood. We conducted an investigation into the extraction solution of Pterocarpus wood and analyzed the mechanism of fluorescence in this species. Possible species of the fluorescent molecules are discussed based on the mixture. Liquid chromatography mass spectrometry (LC-MS) is used for an analysis of the extract, the obtained substances that may be fluorescent in Pterocarpus wood. In addition, the change in the fluorescence intensity with changes in the pH and concentration in the extract is also studied. The results show that the fluorescent molecule is quenched by aggregation (Aggregation-Caused Quenching; ACQ) and is unstable in over-acidic and over-alkaline conditions (especially acidic). Full article
(This article belongs to the Special Issue Advances in Wood Chemical Traits)
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16 pages, 4757 KiB  
Article
B and N Co-Doped Wood Scrap Charcoal for Decorated Supercapacitor with High Conductivity
by Gaojun Chen, Yudong Li, Enshan Han, Ziqiang Zhang, Xiaohui Yang, Desheng Zhou and Yanzhen He
Forests 2023, 14(5), 965; https://doi.org/10.3390/f14050965 - 07 May 2023
Cited by 2 | Viewed by 1273
Abstract
The optimization of supercapacitive properties in carbon materials derived from cheap and sustainable wood scraps exhibits great application potential. Herein, in support of interfacial groups, polyaniline nanospheres were in situ constructed in the internal pore structure of wood scraps; further, the B and [...] Read more.
The optimization of supercapacitive properties in carbon materials derived from cheap and sustainable wood scraps exhibits great application potential. Herein, in support of interfacial groups, polyaniline nanospheres were in situ constructed in the internal pore structure of wood scraps; further, the B and N elements were imported by carbonized and hydrothermal methods. The doped B regulated the species of N doping to improve the electrical conductivity of carbonized wood scraps, and it endowed a certain pseudo-capacitance. Coupled with the fine double-layer capacitance from the hierarchical structures constructed by carbonized nanospheres and wood scrap channels, a high specific capacitance of 406 F g−1 at 0.5 A g−1, high energy density (17.71 Wh kg−1 at 250 W kg−1) and cycle stability (93.04% capacitance retention after 10,000 cycles) were performed simultaneously. This study provided a new strategy to improve the supercapacitive performance of bio-carbon materials in terms of structure and conductance. Full article
(This article belongs to the Special Issue Advances in Wood Chemical Traits)
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11 pages, 4898 KiB  
Article
TiO2-WO3 Loaded onto Wood Surface for Photocatalytic Degradation of Formaldehyde
by Song Li, Zequn Li, Luming Li, Xiangdong Dai, Meiling Chen and Wenkai Zhu
Forests 2023, 14(3), 503; https://doi.org/10.3390/f14030503 - 03 Mar 2023
Cited by 3 | Viewed by 1348
Abstract
In this work, a facile method was adopted to prepare TiO2-WO3 loaded onto a wood surface by a two-step hydrothermal method. The as-prepared wood composite material can be used as a photocatalyst under UV irradiation for the photodegradation of formaldehyde. [...] Read more.
In this work, a facile method was adopted to prepare TiO2-WO3 loaded onto a wood surface by a two-step hydrothermal method. The as-prepared wood composite material can be used as a photocatalyst under UV irradiation for the photodegradation of formaldehyde. Related tests showed that TiO2-WO3 nano-architectonic materials with spherical particles loaded onto the wood substratewere mainly caused by self-photodegradation of formaldehyde. The TiO2-WO3 nanostructured material firmly adheres to the wood substrate through electrostatic and hydrogen bonding interactions. Meanwhile, the appearance of the new chemical bond Ti-O-W indicates the successful loading of TiO2-WO3 onto the wood surface. The photodegradation rate was measured and it was confirmed that the highest photodegradation performance of the modified wood was achieved at a molar ratio of 5:1 of TiO2 to WO3. This work provides a new strategy for the preparing of novel photocatalysts based on wood substrate. Moreover, the wood loaded with TiO2-WO3 is a promising candidate for indoor formaldehyde treatment in practical applications. Full article
(This article belongs to the Special Issue Advances in Wood Chemical Traits)
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