戈尔甘农业科学与自然资源大学

Cyclone separators (CySep) receive special attention due to the collection of powder matter that benefits different industries by preventing loss of product and pollution, where the food industry represents an outstanding case. A tangential-descendent-ascendent flow pattern characterizes the internal flow that suffers a high swirl behavior, secondary flows, and considerable turbulence effects. These elements affect the powder collection efficiency of CySep and depend on their geometric design. Thus, the research of CySep has pretended to predict the flow patterns in order to enhance the powder collection efficiency for a wide range of particle sizes. This study describes the remarkable experimental and theoretical approaches, such as the application of CFD, to improve the efficiency of CySep and motivate its application in foods.

This review provides a focused evaluation of recent advances in the green upcycling of polyethylene terephthalate (PET) waste into polymer quantum dots (PQDs), emphasizing strategies that transform discarded plastic into high-value nanomaterials. It systematically examines environmentally friendly approaches for converting PET waste into PQDs and analyzes their impact on the electronic, optical and surface properties that determine performance in environmental and energy applications. The novelty lies in the comparative assessment of upcycling and synthesis pathways, revealing how processing routes control structural and physicochemical features of PQDs. These features directly enhance performance in two key domains: (i) improved fuel cell electrodes through PQD-assisted charge transfer and reactivity, and (ii) advanced membranes with high adsorption capacity for heavy metals (Hg, Pb, Cd, Cr) in aquaculture wastewater. The review also highlights the dual environmental benefit of plastic waste reduction and toxic metal remediation. Furthermore, it discusses how molecular-level control over PQD structure and surface chemistry can optimize electrochemical activity and metal-binding selectivity, advancing both energy conversion and environmental safety. By synthesizing the latest research, this work identifies challenges and future directions, guiding the sustainable design of PET-derived PQDs under green chemistry and circular economy principles.

This study reports the green synthesis and characterization of bacterial nanocellulose (BNC) film derived from shrimp shell waste and its application in a continuous bioreactor for the removal of sulfur chemical oxygen demand (COD) and biochemical oxygen demand (BOD) from petrochemical wastewater. The produced BNC film was characterized using FTIR XRD BET surface analysis TGA DTG and FESEM. The results confirmed the formation of a crystalline porous and thermally stable nanocellulose matrix with a uniform three dimensional nanonetwork structure which is favorable for adsorption and biologically assisted removal mechanisms. Process optimization was performed using response surface methodology (RSM) by considering five independent variables including pH reaction time temperature number of BNC films and initial pollutant concentration while sulfur COD and BOD removal efficiencies were selected as response variables. Statistical analysis indicated that all investigated parameters significantly affected pollutant removal (p < 0.05). The optimal operating conditions were pH 7 temperature 30 °C reaction time 120 min and two BNC films under which maximum removal efficiencies of 95.6

This study presents a novel application of source-specific particle size distribution (PSD) and moisture content—derived from laser gradation analysis of mining wastes—as explicit inputs to the AERMOD dispersion model, significantly improving its predictive realism for PM10 from complex mining operations at the Jajarm Alumina Plant in Iran. Sampling targeted key waste types, including bauxite crusher slime, red mud, and lime tailings, with laser diffraction (HORIBA LA-950) used to quantify PSD and ASTM D2216 for moisture determination. Emission rates were calculated using AP-42 methodologies and integrated into AERMOD with seasonal surface parameters and high-resolution meteorological data. Model validation against field measurements on 21 June 2018 showed excellent agreement (R2 = 0.88, RMSE = 127 µg/m3), confirming that incorporation of gradation-specific data enhances prediction accuracy compared to generic assumptions. The highest 24-h PM10 concentration reached 2087 µg/m3 near crushers, with an annual average of 542 µg/m3, far exceeding the air standards for particulate matter set by the EPA and the air quality standard in Iran (150 μg/m3). Results identify bauxite crusher slime (60

Efficient in vitro propagation of Phalaenopsis orchids is essential for improving plant quality and production efficiency. This study applied artificial neural networks (ANNs) alongside experimental validation to identify optimal concentrations of two biostimulants, i.e., chitosan and 24-epibrassinolide (24-EBL), for enhancing morphological and photosynthetic performance in Phalaenopsis plantlets. A dataset generated from controlled experiments was utilized to train an ANN with the Levenberg–Marquardt back-propagation algorithm. Model accuracy was confirmed through five-fold cross-validation. Predictions indicated that 12 mg L-¹ chitosan combined with 0.3 mg L-¹ 24-EBL produced the most favorable growth responses. The optimized treatment yielded remarkable improvements, including a > 700