A brief overview on the development of wood research

Wood science covers in particular the areas of the formation and composition as well as the chemical, biological and physical-mechanical properties of wood. First comprehensive studies have already been published in the last century. Detailed knowledge of wood is required for the processing of wood, the production of wood-based materials, and the utilization of wood and wood-based materials as buildings and various other products such as furniture. This review gives a brief overview on the progress in wood chemistry, wood biology (including photosynthesis and biodeterioration), and physical-mechanical properties of wood and wood-based materials. These fundamentals are also essential for understanding technological processes and product development.

Dieter Eckstein, 1939-2021 and his rich legacy of dendrochronology in Slovenia and the world

Prof. Dr. Dieter Eckstein (1939-2021) was a leading scientist, teacher, mentor, leader, promoter and motivatorin the field of dendrochronology and wood biology. After graduating in wood science and receiving a PhD indendrochronology, he was professor of wood biology at the University of Hamburg. From 1995-2004, he was Director of the Department of Wood Biology, University of Hamburg, and of the Institute of Wood Biology and Wood Protection at the Federal Research Centre for Forestry and Forest Products in Hamburg, Germany. His work had a decisive influence on the development of wood anatomy, wood biology and dendrochronology and his laboratory was a reference point for dendrochronology worldwide. He supported dendrochronologists throughout Europe and around the world in their pioneering work to establish dendrochronology laboratories and develop dendrochronology in numerous countries, including Slovenia.

Multi-Fusion Approach for Wood Microscopic Images Identification Based on Deep Transfer Learning

With the wide increase in global forestry resources trade, the demand for wood is increasing day by day, especially rare wood. Finding a computer-based method that can identify wood species has strong practical value and very important significance for regulating the wood trade market and protecting the interests of all parties, which is one of the important problems to be solved by the wood industry. This article firstly studies the establishment of wood microscopic images dataset through a combination of traditional image amplification technology and Mix-up technology expansion strategy. Then with the traditional Faster Region-based Convolutional Neural Networks (Faster RCNN) model, the receptive field enhancement Spatial Pyramid Pooling (SPP) module and the multi-scale feature fusion of Feature Pyramid Networks (FPN) module are introduced to construct a microscopic image identification model based on the migration learning fusion model and analyzes the three factors (Mix-up, Enhanced SPP and FPN modules) affecting the wood microscopic image detection model. The experimental results show that the proposed approach can identify 10 kinds of wood microscopic images, and the accuracy rate has increased from 77.8% to 83.8%, which provides convenient conditions for further in-depth study of the microscopic characteristics of wood cells and is of great significance to the field of wood science.

Microscopy, Working and Types

Microscopy is a technique for making very small things visible to the unaided eye. An instrument used to make the small things visible to the naked (unaided) eye is called a microscope. Scanning electron microscopy is discussed in light of its principles, advantages, and applications. Comparisons of this system are made with the light microscopic and transmission electron systems. A cross section of pertinent literature on the scanning electron microscope, its development and use, has been integrated into the initial sections to provide a reference base for this general field. A detailed literature view on the use of this system in the field of wood science has also been included.

Sustainability in wood materials science: an opinion about current material development techniques and the end of lifetime perspectives

Wood is considered the most important renewable resource for a future sustainable bioeconomy. It is traditionally used in the building sector, where it has gained importance in recent years as a sustainable alternative to steel and concrete. Additionally, it is the basis for the development of novel bio-based functional materials. However, wood's sustainability as a green resource is often diminished by unsustainable processing and modification techniques. They mostly rely on fossil-based precursors and yield inseparable hybrids and composites that cannot be reused or recycled. In this article, we discuss the state of the art of environmental sustainability in wood science and technology. We give an overview of established and upcoming approaches for the sustainable production of wood-based materials. This comprises wood protection and adhesion for the building sector, as well as the production of sustainable wood-based functional materials. Moreover, we elaborate on the end of lifetime perspective of wood products. The concept of wood cascading is presented as a possibility for a more efficient use of the resource to increase its beneficial impact on climate change mitigation. We advocate for a holistic approach in wood science and technology that not only focuses on the material's development and production but also considers recycling and end of lifetime perspectives of the products. This article is part of the theme issue ‘Bio-derived and bioinspired sustainable advanced materials for emerging technologies (part 1)’.

A Novel Polyaniline Nanofiber Combined with Toluidine Blue as a Sensitive Detection Platform for Lignin by Raman Spectroscopy

Raman spectroscopy is widely applied in wood science because of its features of being nondestructive, rapidity, and high resolution. However, Raman scattering is weak, and the Raman signal is easily disturbed by autofluorescence arising from endogenous fluorescent molecules in biological tissue. In this work, a sensitive lignin detection platform was fabricated by a composite with a polyaniline (PANI) nanofiber and toluidine blue (TB) under the excitation of visible light. In this platform, TB acts as a specific marker for lignin, and a PANI nanofiber was used as a reinforcing reagent to improve the Raman intensity of TB. When wood slice is impregnated with TB/PANI, the lignin in wood can be precisely labeled with the TB, and the Raman intensity of TB had a threefold increase at 532 nm excitation. This TB/PANI detection platform is expected to make a significant contribution in qualitative and quantitative analysis of lignin to avoid autofluorescence in various lignin-based biosciences.

Geometric and Topological Bases of a New Classification of Wood Vascular Tissues Part 1: Shape and Arrangement Classifications of Vessels

The need to identify wood by its anatomical features requires a detailed analysis of all the elements that make it up. This is a significant problem of structural wood science, the most general and complete solution of which is yet to be sought. In recent years, increasing attention has been paid to the use of computer vision methods to automate processes such as the detection, identification, and classification of different tissues and different tree species. The more successful use of these methods in wood anatomy requires a more precise and comprehensive definition of the anatomical elements, according to their geometric and topological characteristics. In this article, we conduct a detailed analysis of the limits of variation of the location and grouping of vessels in the observed microscopic samples. The present development offers criteria and quantitative indicators for defining the terms shape, location, and group of wood tissues. It is proposed to differentiate the quantitative indicators of the vessels depending on their geometric and topological characteristics. Thus, with the help of computer vision technics, it will be possible to establish topological characteristics of wood vessels, the extraction of which would be used to develop an algorithm for the automatic classification of tree species.

Success factors of small and medium enterprises in the Malaysian furniture industry: Discerning the growth of entrepreneurs

Entrepreneurs and small and medium enterprises are the foundation of the Malaysian furniture industry. Yet, in a multi-ethnic society such as Malaysia, the success factors of entrepreneurs and small and medium enterprises (SMEs) in the furniture industry have not been studied. Therefore, this study evaluated the success factors of entrepreneurs of the Malay and Chinese ethnic groups in the furniture industry and discerned the growth trajectory of young entrepreneurs from wood science and technology programs in the furniture industry. A questionnaire-based survey was used with the assistance of relevant trade associations and universities. The results were statistically analyzed to establish the significant differences between the two ethnic groups in their perceived success factors. The results revealed that Malay entrepreneurs pay more attention to political and socio-cultural factors to gain success, whereas Chinese entrepreneurs focus on enhancing their competitiveness to remain viable. Further, young graduates showed a reduced desire to pursue a career in the furniture industry, as they deem it not environmentally sustainable in addition to limited career growth. These results suggested that current entrepreneurship development programs may need to be revised, so as to assist in producing more resilient and successful entrepreneurs in the future in the furniture industry.

Computer vision-based wood identification and its expansion and contribution potentials in wood science: A review

The remarkable developments in computer vision and machine learning have changed the methodologies of many scientific disciplines. They have also created a new research field in wood science called computer vision-based wood identification, which is making steady progress towards the goal of building automated wood identification systems to meet the needs of the wood industry and market. Nevertheless, computer vision-based wood identification is still only a small area in wood science and is still unfamiliar to many wood anatomists. To familiarize wood scientists with the artificial intelligence-assisted wood anatomy and engineering methods, we have reviewed the published mainstream studies that used or developed machine learning procedures. This review could help researchers understand computer vision and machine learning techniques for wood identification and choose appropriate techniques or strategies for their study objectives in wood science.