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.
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.
In this article, turbulent forced convection heat transfer in different geometries of solar air heaters (SAHs) where turbulators are set at different angles is investigated for best thermal performance. Six turbulator arrangements are examined (labeled G1-G6), and a comprehensive analysis is presented. Each configuration is equipped with 32 discrete, rotatable turbulators, along with various surface modifications designed to enhance heat transfer. The model developed is validated using experimental, numerical, and correlation data from the published literature. The performance is measured in terms of the Reynolds number (3000 <= Re-Dh <= 13, 000), solar irradiance (200 <= I-center dot <= 1000), and the turbulator angle (0 deg <= alpha <= 45 deg). The model developed can predict several key thermal and flow parameters, including the average absorber temperature (T-a), absorber temperature contours, local air temperature (T-l), magnitude of the resultant air velocity (U), pressure drop (Delta P), average Nusselt number (Nu(Dh)), and the performance enhancement coefficient (PEC). The model outcomes reveal that the turbulator angle exerts a noticeable influence on the heat transfer rate. Both the Reynolds number and turbulator angles are found to positively correlate with the average Nusselt number (Nu(Dh)), indicating enhanced heat transfer by convection at higher flowrates and larger turbulator angles. This is especially true for the G1 and G3 configurations due to enhanced flow disturbance and the more effective mixing associated with these two configurations. Additionally, increasing solar irradiance is observed to slightly reduce the Nusselt number. This is the case primarily due to the elevated initial surface temperature, which reduces the temperature difference between the heated surface and the airflow. Analysis of the PEC further confirms that G1 consistently outperforms the other configurations, achieving PEC values up to 2 at alpha =45 deg and high Reynolds numbers. Despite a general decrease in the PEC at moderate Reynolds numbers, the PEC increases again at higher flowrates due to the fact that the increase in thermal gains outpaces the increase in the frictional pressure drop. Overall, the findings highlight the critical role of turbulator geometry and angles. The developed correlations are derived to calculate the average Nusselt number, the average pressure drop, and the performance enhancement coefficient for various configurations, turbulator angles, and their effects on airflow within a SAH in various turbulator settings.
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.
Density, refractive index, viscosity, surface tension, and speed of sound were experimentally determined and analyzed for [C2mim][MeSO4], [C2mim][EtSO4], [C2eim][EtSO4], [C4mpy][BF4] and [Cnpy][BF4] (n = 3, 4, 6) at atmospheric pressure and temperatures ranging from 293.15 K to 343.15 K. Expectedly, temperature exhibited the greatest impact on viscosity, as all properties decrease as temperatures increase. Linear temperature-dependent correlations were utilized for density, surface tension, refractive index, and speed of sound, while the Vogel–Fulcher–Tammann (VFT) model was applied to describe the temperature dependence of viscosity. The Laplace-Newton equation was used to calculate isentropic compressibility, and thermal expansion was calculated using the experimental density data. Furthermore, the study examined the impact of anion type and alkyl chain length on the thermophysical properties of the ionic liquids. The results reveal that the physical properties of the ionic liquids are significantly influenced by the anion type, whereas the alkyl chain length has a less significant impact. Density, speed of sound, and surface tension decrease as the length of the alkyl chain increases, while viscosity and refractive index exhibit opposing trends. Additionally, the experimental data obtained in this study is compared to theoretical models for density, surface tension, and speed of sound.
