Buckwheat (Fagopyrum esculentum) is a gluten-free crop valued for its protein, fiber, and essential minerals. This study investigates the rheological properties of buckwheat (BW) dough, both with and without the addition of gums (no gum, guar (GG), xanthan (XG)), at varying barrel temperatures (25, 55, and 85 °C) of the rheometer and at different water content levels (45, 50, and 55% w/w) to optimize dough formulations for 3D food printing. Using high shear stress capillary tests, the consistency coefficient (K) and flow behavior index (n) were measured. The results indicated that GG significantly increases the apparent viscosity of buckwheat dough across shear rates ranging from 200 to 2000 s⁻¹, under all temperature and water content conditions. XG also enhanced viscosity but to a lesser extent at moderate temperatures (55 °C, 85 °C). All BW dough formulations exhibited a non-Newtonian shear-thinning behavior, crucial for 3D printing applications. In addition, computational fluid dynamics (CFD) simulations were conducted to analyze the extrusion process of BW dough formulations (50% W, 50% W + XG, and 50% W + GG), focusing on shear rate, viscosity, and pressure distribution. The simulations demonstrated that shear rates increased as the dough moved through the nozzle, while viscosity decreased, facilitating extrusion. However, gum-added formulations required higher pressures for extrusion, indicating an increased difficulty in dough flow. The study highlights the complex interactions between temperature, water content, and additive type on the rheological properties of buckwheat dough, while also incorporating CFD simulations to analyze the extrusion process. These insights provide a foundation for developing nutrient-dense, gluten-free 3D-printed foods tailored to specific dietary needs.

Conventional in-situ light non-aqueous phase liquid (LNAPL) remediation techniques often face challenges of high costs and limited efficiency, leaving residual hydrocarbons trapped in soil pores. This study investigates the efficiency of an alcohol-in-biopolymer emulsion for enhancing diesel-contaminated soil remediation. The emulsion, formulated with xanthan gum biopolymer, sodium dodecyl sulfate surfactant, and the oil-soluble alcohol 1-pentanol, was evaluated through rheological tests, interfacial tension measurements, and one-dimensional sand-column experiments under direct injection and post-waterflooding scenarios. The emulsion exhibited non-Newtonian shear-thinning behavior with high viscosity, ensuring stable propagation and efficient delivery of 1-pentanol to mobilize trapped diesel ganglia. It achieved 100 % diesel recovery within 1.2 PV during direct injection, outperforming shear-thinning polymer-only and polymer-surfactant solutions, which achieved recovery factors of 83.4-92.9 %. Post-waterflooding experiments also demonstrated 100 % diesel recovery within 1.3 PV, regardless of initial diesel saturation. Key mechanisms include reduced interfacial tension, diesel swelling and mobilization induced by 1-pentanol, and uniform displacement facilitated by the emulsion's viscosity. Additionally, the emulsion required lower injection pressures compared to more viscous alternatives, enhancing its injectability into the soil and reducing energy demands. These findings highlight the emulsion's potential to overcome conventional remediation limitations, offering a highly effective and sustainable solution for diesel-contaminated soils and groundwater.

The coupled Computational Fluid Dynamics and Discrete Element Method (CFD-DEM) is a useful tool for modeling the dynamics of sand production that occurs in oil and gas reservoirs. To perform accurate, physically relevant and efficient calculations, the optimal size of the simulation time-step should be selected. In this study, we investigate the selection of an appropriate time-step interval between CFD and DEM models in sand production simulations. The CPU time, speedup and root mean squared relative error of the obtained results are examined to compare the sand production phenomenon at different coupling numbers. Most of the results including the final sand production rate, bond number and bond ratio indicate that the simulations with coupling numbers of N = 10 and N = 100 produce more accurate results. Moreover, these outcomes demonstrate significant improvements in terms of acceleration of the modeling process.