Bacterial cellulose (BC) is reported as an extracellular polysaccharide distinguished by its exceptional purity, mechanical features, and biocompatibility, making it an attractive material for applications in food, plant tissue culture, and Biomedicine. Traditionally, BC production relies on chemically defined synthetic media under either static or agitated fermentation conditions. However, the high production cost remains a critical barrier, limiting its large-scale utilization and broader adoption. To address this challenge, recent advances in sustainable strategies, such as employing agro-industrial byproducts and low-cost carbon sources, have significantly reduced costs while improving yield and functional properties. In particular, diverse environmental waste streams, including agricultural residues, industrial wastes, and food processing byproducts, have been explored as renewable substrates for BC synthesis. BC can be obtained through static fermentation, yielding gelatinous films (pellicles), or agitated fermentation, generating suspended fibers or pellets, each with unique structural features. Moreover, innovative functionalization approaches, including in situ and ex situ modifications, incorporation of bioactive agents, and the development of BC-based nanocomposites, have further expanded its biomedical potential. This review emphasizes sustainable strategies to overcome the cost limitations of BC production, while also highlighting recent advances in functionalization techniques and their pivotal role in advancing medical applications, including cartilage engineering, bone regeneration, wound healing, and dentistry.
The continuing imperative to reduce sulfur oxide emissions from transportation fuels has intensified the search for efficient desulfurization strategies. In this work, a comprehensive thermodynamic investigation of extractive desulfurization was conducted using model diesel systems composed of n-dodecane, n-tetradecane, or n-hexadecane in the presence of a homologous series of methylimidazolium bis(trifluoromethylsulfonyl)imide ([Cₙmim][NTf₂]) ionic liquids. Liquid-liquid equilibrium (LLE) data for ternary mixtures containing thiophene were determined experimentally at 313.15 K and 101.3 kPa. The extraction performance was assessed through distribution ratios and selectivity values, with systematic evaluation of the effect of alkyl chain length in both the paraffinic solvent and ionic liquid cation. The experimental data were accurately correlated using the Non-Random Two-Liquid (NRTL) model, yielding excellent agreement with minimal root-mean-square deviations. The resulting ternary phase diagrams revealed high extraction efficiency, with distribution ratios between 2.20 and 3.39 and selectivity values in the range of 66264. These findings demonstrate the promising potential of [Cₙmim][NTf₂] ionic liquids as tunable, thermodynamically favorable solvents for deep extractive desulfurization of hydrocarbon fuels.
Sustainable multifunctional composites derived from waste biomass and silica are under intense investigation for wastewater remediation owing to their economic and environmental advantages. In the current study, Cellulose nanofibers (CNF) were prepared via the TEMPO-mediated oxidation process, and (3-Aminopropyl)triethoxysilane (APTES) was used as a silica precursor for surface modification of the fibers. The CNF/silica–NH₂/Ag-NPs composite polymer was characterized by SEM, TEM, TGA, and FT-IR, confirming its successful formation. SEM investigation exhabit bright, well-dispersed spots observed in the SEM images correspond to Ag-NPs embedded within the porous CNF/silica matrix. TEM analysis showed a non-uniform coating of amorphous, spherical silica particles with an average size of 25 nm for Ag-NPs. The developed CNF/silica–NH₂/Ag-NPs composite polymer was evaluated as a multifunctional material for the removal of both cationic and anionic dyes, as well as for its antimicrobial activity against pathogenic microorganisms. The bioadsorbent showed better removal efficiency for methylene blue (MB) and methyl orange (MO) dyes due to electrostatic attraction. From the isotherm studies, the Langmuir isotherm model provided the best fit, and CNF/silica-NH₂/Ag composite polymer displayed maximum adsorption capacities of 370 mg/g and 151 mg/g for MB and MO, respectively. From the reusability studies, it was observed that the multifunctional cellulose adsorbent exhibited appreciable adsorption values even after 5 cycles adsorption-desorption. The antimicrobial potential of the synthesized composite polymer showed different inhibition zone against Escherichia coli (18 mm), Salmonella typhimurium (20 mm), Staphylococcus aureus (16 mm), Streptococcus mutans (17 mm), and Candida albicans (15 mm). The findings revealed that the CNF/Silica-NH₂/Ag-NPs composite polymer exhibited vigorous antimicrobial activity against all tested pathogens, with minimum inhibitory concentrations (MICs) as low as 0.0012 mg/mL. Among them, E. coli demonstrated the highest MIC of 0.04 mg/mL, indicating relatively lower sensitivity to the composite polymer.
Turbulent forced convection heat transfer from a flat-plate solar air heater (SAH) with mul-tiple parameters is investigated. The SAH is equipped with 32 discrete rotatable turbula-tors and different surface modifications to enhance heat transfer. Independent parameters to be varied include the Reynolds number (3000 <= Re-Dh <= 13,000), solar irradiance (200 <= I <= 1000), the angle of the turbulators (0 deg <=alpha <= 45deg), the non-dimensional pin height of the turbulators (H-T(& lowast;)=0.01and 0.02), and the non-dimensional length of the turbulators on each side (L-T(& lowast;)=0.02and 0.03). The developed model is capable of comput-ing the average absorber temperature (T-a), local absorber temperature contours, local air temperatures (T-l), magnitude of the resultant air velocity (U), pressure drop (Delta P), the average Nusselt number (NuDh), and the performance enhancement coefficient (PEC) of the SAH. The results indicate that the angle of the turbulators has a moderate effect on the heat transfer rate. Increasing the Reynolds number and the angle of the turbulators increases the average Nusselt number (NuDh). An increase in the Reynolds number initially causes a decrease in the PEC, but with further increases in the Reynolds number, the PEC switches to an upward trend. The results also indicate that longer and larger turbulator pin height contribute to an improved heat transfer rate. New correlations to calculate the average Nusselt number, the average pressure drop, and the performance enhancement coefficient for air flow within the solar air heater at different turbulator angles have also been developed. This study should provide insight and practical design guidelines of the performance of SAHs
Natural gas occupies a central position in today's global energy system, acting as a bridge fuel in the transition toward low-carbon systems. Yet, the solidification of carbon dioxide (CO2) during processing and transportation creates significant operational and safety concerns. Accurate prediction of the CO2 frosting temperature (Tf) in natural gas mixtures is therefore essential for ensuring flow assurance and efficient process design. Traditional estimation methods remain constrained: experimental techniques, though precise, are costly and time-intensive, while thermodynamic and empirical approaches often suffer from limited applicability and moderate reliability under diverse conditions. To overcome these limitations, this study proposes an integrated and explainable data-driven framework that employs advanced machine learning paradigms for high-fidelity Tf prediction under complex operating conditions. Three models, namely Categorical Boosting (CatBoost), Tabular Prior-data Fitted Network (TabPFN), and Least Squares Support Vector Machine (LSSVM), were implemented and benchmarked using a comprehensive experimental database to accurately predicting CO2 frosting temperature under extensive operational conditions. Among the applied predictive schemes, the TabPFN model delivered superior accuracy, achieving a mean absolute percentage error of 0.13% and a coefficient of determination of 0.9992. Model interpretability was ensured using Shapley Additive Explanations (SHAP), providing transparent insight into feature influence and reinforcing the physical credibility of the predictions. Statistical reliability was further evaluated using the leverage approach, confirming model robustness. Overall, the framework demonstrates the rigorous integration of high-accuracy prediction, explainable artificial intelligence, and statistical reliability assessment within a unified platform, offering a practical and reliable tool for industrial applications.
