Volume 21 Issue 3

In recent years, the increasing demand for sustainable and renewable materials has intensified interest in biomass resources capable of supporting green processing and circular bioeconomy strategies. Among these, Opuntia spp. (commonly known as nopal or prickly pear cactus) has emerged as a promising biomass resource due to its adaptability to arid environments and its rich and diverse chemical composition. This review aims to provide a comprehensive and integrated analysis of Opuntia spp. biomass, focusing on its chemical composition, extraction and processing technologies, and its potential for the development of bioproducts and biomaterials. One of the key aspects addressed is the comparison between conventional and environmentally friendly extraction approaches, considering their efficiency, limitations, and suitability for recovering important fractions such as mucilage, structural polysaccharides, and bioactive compounds. This review evaluates the utilization of these fractions in food systems, biodegradable materials, hydrogels, composite materials, and environmental remediation, with an emphasis on their functional attributes and technological potential. Sustainability is considered from a biorefinery perspective, focusing on the efficient use of biomass and the mitigation of environmental impacts. This work gives a critical analysis of recent progress and highlights mayor challenges concerning process standardization, scalability and material properties. The findings support the potential of Opuntia spp. as a versatile platform for sustainable materials and point to future research needed for industrial implementation.

Plastic waste is one of the most concerning issues for the environment in the world. Many researchers are seeking sustainable solutions by replacing petroleum-based plastic with bio-based plastic. Bioplastic is synthesized from natural resources, and it can degrade in different ecosystems. Therefore, it could serve as alternative to combat the harmful impacts of conventional plastic. This review explores different bio-based polymers such as polylactic acid (PLA), polyhydroxyalkanoates (PHA), polybutylene succinate (PBS), starch blends (SB), and cellulose-based bioplastics (CB). These polymers can be used for many industrial applications, including agriculture, automobiles, textiles, packaging, and medical. To give insight into the procedure of bioplastic manufacture, various processes are analyzed in this review, including the solvent casting technique, extrusion techniques, injection molding, and 3D printing. The products of bioplastics such as micro-nano plastic, monomers, and oligomers released after degradation are critically analyzed and the toxicity of bioplastic on different ecosystems has been discussed. A research gap was also identified, as most toxicological studies of bioplastic do not include the LC₅₀/LD₅₀ threshold.

Surface inspection plays a critical role in the wood industry, as it helps companies to enhance product quality, improves the utilization of wood resources, and increases the value of final products. In recent years, deep learning has emerged as a promising technique in this domain, offering significant advantages over traditional methods by enabling high-precision, real-time inspection. This paper presents a comprehensive review of advancements in the field from 2021 to 2025. It begins with a brief overview of three foundational aspects: common types of wood defects, publicly available datasets, and evaluation metrics. The core of the review then examines recent deep learning applications, organized according to three computer vision tasks—classification, detection, and segmentation. The paper concludes by discussing key challenges and proposing viable directions for future research, thereby offering a clear technical roadmap.

Heavy metal contamination in agricultural soils poses persistent risks to crop safety and food-chain exposure. Although biochar is widely proposed—and increasingly applied—as a remediation amendment, field performance remains highly variable across soil constraints, metal speciation, and biochar designs. This review addresses this uncertainty by translating immobilization pathways (sorption/ complexation, precipitation, and redox-mediated stabilization) into a decision-oriented “mechanism–lever–endpoint” framework, thus linking mechanistic hypotheses to controllable engineering strategies such as feedstock selection, pyrolysis windows, and mineral/composite design. Beyond established plant–microbe interactions, there is a critical assessment of under-synthesized biochar–soil fauna pathways, with a focus on earthworms, and a reconciliation of conflicting evidence by highlighting boundary conditions that shift biological responses. Agronomic trade-offs and environmental risks are considered, associated with biochar production and application, emphasizing failure modes relevant to long-term soil health and remediation reliability. To support decision-grade deployment under heterogeneous evidence, a bias-aware AI-assisted workflow is outlined, which stresses standardized reporting, interpretability, and leakage-safe validation. Overall, the review integrates engineering options with biological synergies into a practical roadmap for more predictable and site-specific remediation in agricultural soils.

Lignin, a prospective bioresource from plants, has been undervalued for several dozen years because of the unpredictable structures and their changeability during extraction. Recently, lignin has become a star for certain researchers who are aiming to develop sunscreen products offering production against UV radiation. The preparation of lignin into sunscreens as a natural alternative to chemical products may offer new perspectives. This review discusses how lignin isolation methods and the resulting structural characteristics affect UV absorption, thereby determining the potential of lignin as a UV-absorbing and blocking agent in sunscreen formulations. The application of lignin in cosmetics may present great benefit to humans and their skin care needs.

Lignocellulosic biomass is an abundant renewable energy resource whose total energy content far exceeds current global demand. Its polysaccharides, cellulose, and hemicelluloses are primary targets for valorization; however, the recalcitrant structure of plant cell walls necessitates effective pretreatment. Among physical, physicochemical, chemical, and biological methods, advanced oxidation processes (AOPs) have emerged as an efficient and environmentally compatible chemical strategy. This review systematically evaluates nine major AOPs for lignocellulosic biomass pretreatment: Fenton and Fenton-like processes, alkaline H₂O₂, peracetic acid, persulfate, ozone, photocatalysis, electrochemical oxidation, wet air oxidation, and cavitation-assisted methods. For each, reaction mechanisms, advantages and limitations, recent advances, economic considerations, and scale-up challenges are discussed. Persulfate-based systems, wet air oxidation, and hybrid strategies (e.g., photo-Fenton, alkaline H₂O₂–cavitation, electrochemical–ozone–H₂O₂) are identified as particularly promising. Future research should prioritize novel catalyst development, reactor optimization, multi-mechanism integration, and rigorous techno-economic and life-cycle assessments. Coupling AOPs with renewable energy sources will be critical to improving energy efficiency and enabling cost-effective large-scale application.

The paper industry has achieved relatively high levels of recovery and reuse of cellulosic fibers by the recycling of paper. The industry’s record of recovering material from used paper products far exceeds that of, for instance, the plastics industry. However, in the course of that success there are challenges. This paper provides a tutorial review of problems related to the stickies content of recovered used paper material. The review considers what stickies are composed of, how they can affect papermaking and its products, and how papermakers can minimize their adverse effects. Stickies tend to be problematic in mills without deinking systems. Though some stickies can be removed by screening, much of the material is expected to deform and thereby pass through industrial screens. Larger stickies (macrostickies) can be removed using hydrocyclones, whereas smaller ones (microstickies) are often removed using flotation. Other approaches to dealing with stickies include using detackifiers, which essentially means covering the stickies with a polymer or a mineral product. Production teams can optimize conditions to retain the stickies, ideally as small particles, onto cellulosic fibers. Because the processes are complex, and the composition of recovered stock tends to change over time, continuing efforts will be required. Effective control of stickies can be expected to require effective collaboration among people at the mill, chemical supplier companies, and machinery specialists.

This review examines the biomethane value chain from feedstock selection and feedstock-specific pretreatment to digester operation, gas upgrading, utilization pathways, and life-cycle constraints. Rather than evaluating biomethane by methane purity alone, it emphasizes the combined effects of feedstock quality, pretreatment intensity, process stability, upgrading energy demand, methane slip, digestate management, and end-use infrastructure. Key anaerobic digestion variables, including temperature, pH, volatile fatty acids, carbon-to-nitrogen ratio, organic loading rate, hydraulic retention time, and inhibitor control, are linked to process robustness. Major upgrading technologies, including water scrubbing, chemical absorption, pressure swing adsorption, membrane separation, and cryogenic separation, are compared in terms of methane purity, methane loss, energy demand, cost tendency and deployment fit. Biomethane shows the strongest mitigation potential when wastes are locally sourced, methane losses are controlled and digestate is safely recycled.

Corrugated board, traditionally used in packaging, is increasingly explored as a structural material for lightweight engineering applications, including furniture, panels, and temporary building components. This transition requires moving beyond empirical design rules toward performance-driven and optimization-based methodologies. This paper presents a critical review of optimization techniques applied to the structural design of corrugated board, with emphasis on the mechanical and numerical foundations required for physically reliable optimization. The reviewed studies were selected from major scientific databases using keywords related to corrugated board, structural optimization, finite element modeling, homogenization, surrogate modeling, machine learning, hygro-mechanical behavior, failure mechanisms, and experimental validation. Unlike earlier reviews focused mainly on industrial optimization practices and algorithms, this review highlights model fidelity, validation hierarchy, moisture sensitivity, local failure, interface damage, and the maturity of data-driven workflows. Analytical models, finite element approaches, homogenization frameworks, reduced-order models, and surrogate-assisted strategies are discussed in terms of their applicability to iterative optimization. Key design variables, including flute geometry, layer configuration, material anisotropy, and environmental conditions, are related to stiffness, strength, stability, ECT, BCT, and material efficiency. The main gaps include limited validation, simplified moisture-dependent and failure descriptions, weak interface-damage modeling, and the early development of digital-twin concepts for corrugated board design.

Machine Vision (MV) software has emerged as a powerful tool for timber species identification, offering significant advantages including species-level accuracy, cost-effectiveness, and the elimination of human errors. The development of MV software relies on three major supporting technologies: computer vision, machine learning (ML), and deep learning (DL). This paper provides an in-depth exploration of the role of DL, with a particular focus on convolutional neural networks (CNNs), in enhancing MV software for timber identification. The potential of CNN architectures is examined in detail, including a review of commonly used CNN models and their effectiveness in identifying different timber species. This paper also discusses current practices in developing CNN models for integration into MV software for timber identification tasks, highlighting the standards and procedures researchers should follow to ensure optimal performance and reliability. Additionally, the challenges associated with implementing CNN models for timber identification are addressed. They include limited model usability due to the diversity of timber species and geographical variation. Variability in methodologies, imaging devices, and data processing approaches across studies further complicates the comparison and integration of results. This paper emphasises the need for standardised practices and further empirical research to address these inconsistencies and improve CNN-based MV systems for timber identification.