Volume 19 Issue 4

Forest and biomass resource utilization for bioenergy and bioproducts is crucial for mitigating climate change and promoting a sustainable bioeconomy. Given that the biomass supply chain is a complex system, one of the most concerning issues is selecting and using appropriate modeling and analytical technologies to optimize the advantages of multi-feedstock biomass supply chains. Machine learning (ML) can enhance biomass supply chain management (BSCM) efficiency and sustainability. It can address the complexities in cultivation, harvesting, preprocessing, storage, transportation, and conversion. ML workflows involve data collection, preprocessing, model training, and optimization, using algorithms for prediction and decision-making. Accurate supply and demand forecasting via ML improves production planning and inventory management. Despite its potential, ML applications in BSCM need to deal with challenges such as data availability and quality, interpretability of models, and their generalization capabilities. Overcoming such challenges requires interdisciplinary efforts in data management and model development to fully leverage ML’s applicability.

The US and the EU have adopted contrasting pathways in their pursuit of increased sustainability. This editorial highlights such contrasts with respect to paper and nonwovens products. The American way, at least at the federal level, depends on consumer input, which can have an impact on corporate decisions and practices. Progress with respect to sustainability in the European Union has a higher reliance upon regulations. Each approach has merits as well as deficiencies. A regulation-dependent approach sometimes just moves problems to other parts of the world. A consumer-driven approach does not have a good way to deal with a need for systematic change, such as systems to recycle textile and nonwoven materials. It follows that a combined approach, involving both consumer demand and regulations, can be recommended as a most effective path forward. In addition, applications of artificial intelligence have potential to reconcile societal needs with future industrial practices.

Lignin, a subject of extensive study in academies and industries, is known for its natural abundance, biodegradability, and potential to be transformed into biochemicals and biomaterials. However, the original biomass and extraction treatments, such as kraft pulping and the organosolv process, significantly influence its chemical structure, leading to variations in reactivity. Unfortunately, many scientific publications fail to provide comprehensive lignin property descriptions, which hampers experiment reproducibility and literature comparison. This, in turn, hinders fundamental studies and scientific advancements. This editorial aims to address this issue by advocating for including lignin characteristics in scientific papers when possible.

In recent years, lignocellulosic materials have become regarded as attractive and noteworthy natural resources owing to their renewability, recyclability, easy processability, abundance, biodegradability, and low cost. The developments in nanotechnology have opened new doors in the field of bio-based nanosensor technology, which is utilized in electronics, optical products, communication, automotive, packaging, tissue engineering, biomedical, textile, etc. This paper mainly focuses on the usage of lignocellulosic materials in nanosensors.

Trees represent a cherished treasure for each nation. They provide a living, soulful, and earthly material, wood, which most often survives millennia and embellishes our everyday life. Wood can be transformed into valuable pieces of art under skilled hands and tools through carving. In Romania, wood is omnipresent in each milepost of people’s life journey from birth to death. It becomes a true and empathic companion of both happy and sad events. Wood teaches us to focus on the present moment and to let go of stressful thoughts and feelings. It is a real valuable “good” in our life.

This editorial draws a parallel between important papermaking innovations that were implemented by Mahatma Gandhi and some more recent ventures in papermaking in India. Both of these examples share common themes of fostering the skills of local people, using local resources, and contributing to a better future. A key insight is the scaling of the equipment to be well matched with the size of the production team and enabling a broad range of product grades. The case study considered introduces a modern twist – using papermaking to achieve circularity in the production of textiles. As in the early days of European papermaking, once again waste textile products are serving as the primary source of material.

In this study artificial neural network (ANN) models were developed for predicting the effects of wood species, density, modifying time, and temperature on the equilibrium moisture content (EMC) and swelling of six different thermally modified hardwood species, as previously published by the authors. Lumber of Yellow-poplar (Liriodendron tulipifera), red oak (Quercus borealis), white ash (Fraxinus americana), red maple (Acer rubrum), hickory (Carya glabra), and black cherry (Prunus serotina) were selected. Treatment type, species, temperature, time, and density were used as inputs for the models. Using Keras and Pytorch libraries in Python, different feed forward and back propagation multilayer ANN models were created and tested. The best prediction models, determined based on the errors in training iterations, were selected and used for testing. Based on the performance analysis, the prediction ANN models were accurate, reliable, and effective tools in terms of time and cost-effectiveness, for predicting the EMC and swelling characteristics of thermally modified wood. The multiple-input model was more accurate than the single-input model and it provided a prediction with R² of 0.9975, 0.92, and MAPE of 1.36, 7.77 for EMC and swelling.

The food packaging characteristics and environmental impact of paper coated with polysaccharide dispersions were analyzed. Colloidal dispersions of xylan and xylan derivatives, as well as their combinations with chitosan and nanocrystalline cellulose, were applied in thin layers on both sides of the paper surface (5 g/m²). The barrier properties to water, water vapor, gases, oil/grease, and the antimicrobial properties of the coated paper were evaluated. Generally, polysaccharide coatings improved the barrier and antimicrobial features of coated papers compared to uncoated paper. Significant improvements were obtained by combining xylan derivatives and chitosan, where the contact angle of the coated paper reached 92.8° and achieved 100% inhibition of Bacillus sp. Furthermore, food simulant tests indicated that all tested polysaccharide combinations are suitable for use in food packaging, especially for fatty products. After 28 and 42 days of soil degradation, all samples of xylan and xylan/xylan derivatives/chitosan/nanocellulose coatings reached similar degradation levels (70 to 80% and 14 to 16 mg CO₂ production).

Cinnamon is a plant with significant medicinal value and used extensively as a spice, flavoring, and fragrance ingredient. Phytochemical characterization via HPLC, antimicrobial activity against various microorganisms, anti-diabetic activity via α-amylase and α-glucosidase assessment, antioxidant activity via 2,2-diphenyl-1-picryl-hydrazyl-hydrate, total antioxidant capacity and Ferric reducing Antioxidant Power methods, besides anticancer activity against MCF-7 (human breast cancer cells) and WI-38 cells (human fetal lung fibroblast cells) of cinnamon bark extract were detected. A promising antimicrobial action towards Enterococcus faecalis, Staphylococcus aureus, Klebsiella pneumoniae, Salmonella typhi, Candida albicans, and Mucor circinelloides with inhibition zones of 25, 23, 18, 19, 29, and 16 mm, respectively was recorded. Cinnamon bark has IC₅₀ of 5.01 and 2.58 µg/mL compared to standard acrobose with their IC₅₀ values 4.32 and 1.99 µg/mL, for α-amylase and α-glucosidase inhibition, respectively. Cinnamon bark extract have IC₅₀ of 77.39 ± 0.84 and 162.67 ± 0.28 μg/mL against MCF-7, and WI-38 cell lines, respectively. The protective effect of cinnamon in accelerating the apoptosis of MCF-7 cells has been verified by flow cytometric evaluation employing Annexin-V and cell cycle kits, besides increasing the amounts of malondialdehyde, hydrogen peroxide and nitric oxide with decreasing glutathione and catalase levels.

Hornification is a well-known phenomenon describing what happens during the drying of lignocellulosic materials, often within and between cellulosic pulp fibers. For wood fibers used in papermaking, this phenomenon decreases fiber wall swelling, and internal and external fibrillation. It reduces flexibility of damp fibers, which leads to a diminished ability to form effective fiber networks, resulting in lower paper strength. This work investigates how drying temperature affects the changes in fiber morphology, connects this to the changes in sheet behavior, and proposes a combination of bonding mechanisms for hornification. Results show that hornification depends on drying temperature; higher temperature gives higher degrees of hornification with decreased WRV of about half the numerical value, from 1.5 g/g for never-dried pulp to 0.7 g/g for hardwood pulp samples. Higher temperatures, above 100°C, also change the pulp color, as measured by increased yellowness. Decreased swelling capacity and pulp yellowness are connected. This indicates parallel reactions, which both contribute to hornification. The mechanisms are proposed to be chains of hydrogen bonds, dominating at low temperatures and providing no color change, and dehydration reactions via pyrolysis, giving a yellow-to-brown color shift. Compression strength measurements show that major hornification adversely affects sheet strength due to poor network bonding. However, minor hornification can be beneficial for applications where compression strength is an important parameter.