Research Articles

All-cellulose composites (ACCs) have been fabricated by using a variety of cellulosic sources, versatile technologies, and are sustainable alternatives for traditional composites. In this study, nonwoven-woven ACC laminates were created from wood-based Spinnova short fibers and Lyocell fabrics via partial dissolution and an NaOH-urea solvent system. The less-known wood-based Spinnova fiber is created for the textile industry, but it also has great potential for the composite industry. To identify the mechanical properties of ACCs—which greatly influence the range of material application—tensile, impact, and flexural tests were conducted. The mechanical properties indicated only moderate properties, which are influenced by high porosity and weak fiber bonding. Despite this, valuable information on the nonwoven-woven structured ACCs was obtained. To improve the ACC laminate’s ability to resist moisture, bio-based coatings (e.g., commercially available birch bark betulin and suberin acid mixture) were applied on the surface of ACCs and it successfully improved the wetting resistance. The results of contact angle analyses demonstrated that the highest contact angle of 128° was measured for betulin-coated laminates and the best stable hydrophobicity calculated a minute after the beginning of the experiment were observed at 109° for the uncommercial pressurized hot ethanol (PHE) extract of birch bark.

Static life cycle assessment (LCA) methodologies fail to consider the temporal profiles of system inputs and outputs (including emission timing), such that they underestimate the benefits of temporarily stored biogenic carbon in bioproducts, such as cotton. This research focuses on greenhouse gas emission timing and applies dynamic emission accounting to the life cycle of cotton woven pants. The significance of temporary biogenic carbon storage and emission timing is illustrated by converting the 2017 Cotton Incorporated static LCA to a dynamic model using the Dynamic Carbon Footprinter (baseline scenario). A reduction in cumulative radiative forcing for dynamic relative to static modeling of 22%, 5%, and 2% are observed at 10-years, 30-years, and 100-years, respectively. Alternative scenarios analyzed include converting cotton woven pants at end of life to bioenergy, to compost, or to building insulation, an alternative cotton production scenario using regenerative agricultural practices, and two pants extended lifetime scenarios. The regenerative agricultural practice scenario provides reductions in cumulative impacts compared to the baseline scenario of 96%, 69%, and 105% after 10, 30, and 100-years, respectively. A 3x extension in the lifetime of pants provides a benefit in reduced cumulative impacts of 31%, 40%, and 41%, after 10, 30, and 100-years, respectively. This case study with cotton demonstrates that dynamic LCA is a useful tool for assessing the benefits of biobased products, and it allows for more nuanced analysis of reductions in climate impacts in both the short- and long-term time horizons.

γ-Valerolactone (GVL) is a promising biomass-based platform compound that can be used for the removal of hemicellulose in pulp. In this study, a paper-grade pulp was treated using GVL to generate dissolving pulp for viscose production. The GVL concentration, treatment temperature, and reaction time had significant effect on the hemicellulose dissolution. The dissolving pulp with α-cellulose content of 92.3% and hemicellulose content of 5.45% was achieved with 60% GVL at 120 °C for 2 h. The Fock reactivity and intrinsic viscosity of the obtained dissolving pulp were 54.6% and 595 mL/g, which is comparable with the commercial product. In addition, GVL spent liquor was also recycled and reused to upgrade paper-grade pulp. By using purified recycled GVL to treat original pulp, the Fock reactivity of pulp was improved, and the cellulose content of as-prepared upgraded pulp increased to 92.1%, which was close to the cellulose content of dissolving pulp obtained from fresh GVL solution, while the intrinsic viscosity decreased significantly to 598 mL/g. Therefore, the efficient reuse of GVL not only ensured the high quality of dissolving pulp, but it also saved production costs and reduced environmental pollution.

In recent years, the custom wardrobe market has been steadily developing. While meeting the functional needs of users, it is gradually shifting towards aesthetic preferences. Rapidly grasping users’ preferences for the appearance of custom wardrobes is a key focus of current research. This study collected a large number of decorative surface images of custom wardrobes and objectively analyzed the design features based on color moments and Tamura texture feature data in computer image analysis methods. K-means cluster analysis was performed on the feature data. Collected images of the points closest to the cluster centers were further screened to select representative finish images, and finally a questionnaire survey was conducted at Nanjing Forestry University, with the help of semantic differential method and factor analysis. The characteristics of the samples were comprehensively summarized to infer design elements. The study found that warm-toned, medium-low saturation, and medium brightness surfaces were preferred by the panel. Different colors, contrasts, saturations, brightness, element features, and arrangements have significantly different effects on visual perception. These conclusions can provide a reference for subsequent custom wardrobe design.

Bark, the outermost tissue, plays an important role in protecting trees from damage induced by living organisms and the surrounding environment. Bark differs from the xylem primarily by the presence of suberin in cork cell walls. However, few studies have examined the role of suberin and its interactions with other chemical components in the cork. Consequently, this study aimed to understand the distribution of chemical components, including suberin and lignin, and their respective roles in cork cell walls, using Cerasus jamasakura (Siebold ex Koidz.) H. Ohba. Suberin and lignin were gradually and selectively removed from thin strip specimens. Fourier transform infrared (FTIR) spectroscopy suggested that desuberinization removed both suberin and part of the other matrix substances within a few minutes of treatment, whereas delignification exclusively removed lignin. Further microscopic observation revealed that suberin present was mainly in the secondary wall of cork cells, whereas lignin was present in both the tertiary wall and compound middle lamella. In addition, the cell wall collapse of the cork was only found in desuberinized specimens, whereas delignified specimens only showed monotonic contraction. Taken together, these results suggest that the presence of suberin in the cork contributes to the shape stability of cork cell walls.

It is difficult to predict index values or stock prices with a single financial formula. They are affected by many factors, such as political conditions, global economy, unexpected events, market anomalies, and the characteristics of the relevant companies, and many computer science techniques are being used to make more accurate predictions about them. This study aimed to predict the values of the XKAGT index by using the monthly closing values of the Borsa Istanbul (BIST) Forestry, Paper and Printing (XKAGT) index between 2002 and 2023, and the machine learning techniques artificial neural networks (ANN), random forest (RF), k-nearest neighbor (KNN), and gradient boosting machine (GBM). Furthermore, the performances of four machine learning techniques were compared. Factors affecting stock prices are generally classified as macroeconomic and microeconomic factors. As a result of examining the studies on determining the macroeconomic factors affecting the stock markets, 10 macroeconomic factors were determined as input. The macroeconomic variables used were crude oil price, exchange rate of USD/TRY, dollar index, BIST100 index, gold price, money supply (M2), S&P 500 index, US 10-year bond interest, export-import coverage rate in the forest products sector, and deposits interest rate. It was determined that all machine learning techniques used in the study performed successfully in predicting the index value, but the k-nearest neighbor algorithm showed the best performance with R²=0.996, RMSE=71.36, and a MAE of 40.8. Therefore, in line with the current variables, investors can make analyzes using any of the ANN, RF, KNN, and GBM techniques to predict the future index value, which will lead them to accurate results.

Giant snowdrop (Galanthus elwesii Hook. f.) is reported on CITES lists as the most important wild-collected export flower bulb of Turkey. After the destruction caused by extensive collecting for many years, collection and trade have been controlled by the government since the late 1980’s. Export quotas are annually set by a technical committee, following field inspections by scientific teams. Small bulbs (under-sized) are also unintentionally collected from nature together with marketable sized bulbs (> 4 cm). Low success rate at cultivation of field grown snowdrop and loss of these small bulbs reveals the necessity of the studies on soilless culture. This study aimed to select the suitable growing media and mineral nutrition for snowdrop bulb production. Under-sized (unmarketable) bulbs of Galanthus elwesii Hook. f. were cultivated in four different growing media (perlite, coco-peat, soil, or spent mushroom compost + perlite) with four different nutrient solutions. The growing media affected most of the investigated parameters (sprouting time, sprouting ratio, total weight and increase ratio, number of harvested bulbs, and bulb grades). No significant effect of nutrient solutions was determined on the parameters. Spent mushroom compost + perlite appeared to be the most effective growing media.

In the 21^st century, with active policy support, the smart hardware industry ushered in rapid development of the Internet of things, big data, and other emerging technologies, which has brought people a more intelligent way of life. Smart product retail experience stores have also emerged as a result. One of the key aspects of the consumer shopping experience is highlighted by the furniture design of the smart display table. This study considers the background of consumer rejuvenation and the diversification of needs, through in-depth examination of user pain points, and output of the design of smart product display table from the user experience perspective. Analytic Hierarchy Process (AHP) and Function Analysis System Technique (FAST) are used to systematically refine the smart display table requirements, synthesize the weights of the sorting factors, quantify the demand contribution value, and transform the requirements into functional design points. The goal is to enhance the user experience, while achieving exterior styling and usage features of the smart display table. This study provides theoretical and practical support for furniture design in the smart hardware industry, emphasizing the role of consumer experience and needs in product design and providing considerations for design research.

Angelica sinensis polysaccharides are important active ingredients and  biological resources in traditional Chinese medicine. Properly changing the fermentation conditions of microorganisms may alter the yield of fermentation products. Based on single factor test results, three factors with great influence on the yield of Angelica sinensis polysaccharides produced by Aspergillus niger were optimized: initial pH value, fermentation time, and culture temperature. According to the box Behnken central combination principle, a response surface analysis scheme with three factors and three levels was designed, and the yields of active polysaccharide of Angelica sinensis were taken as the response value to optimize the fermentation process. The results of response surface analysis showed that under the optimized fermentation temperature of 30 °C, cultivation time of 8 days, and initial pH value of 5, the predicted yield of active polysaccharides from Angelica sinensis was 15.5%, while the actual value was 15.35%, which was 0.11% lower than the predicted value. This indicated that using response surface analysis to optimize the fermentation conditions of Angelica sinensis polysaccharides was reasonable and feasible. This method can effectively improve the biological resource utilization rate of Angelica sinensis polysaccharides.

The flexural performance of a glulam wood/PVC composite (GWPVC) hollow member, which was assembled from four elements with WPVC composite hollow sections to create a double I-section, was studied using two methods: the analytical method with iteration technique (AMIT) and the finite element method (FEM). Experiments verified the predictions from AMIT and simulations from FEM to determine the most suitable method for the parametric studies. This investigation explored the variations in slenderness ratio and the flange and web thickness to enhance and study their impact on flexural performance. In parametric studies, equal cross-sectional areas were used to facilitate comparisons and maintain production costs. The importance of bond strength at the contact surfaces in assembling a GWPVC hollow member led to bonding tests and confirmed sufficient strength at contact surfaces. In four-point bending tests, the GWPVC hollow member exhibited a brittle mode with flexural tensile failure without delamination. The initial MOE, MOR, and maximum deflection values were 5,140 MPa, 29.8 MPa, and 47.8 mm, respectively. The parametric study employs AMIT to investigate flexural performance and reveals that varying the slenderness ratio affected the initial stiffness, maximum deflection, and ultimate load. Flexural performance can be improved by making minor adjustments.