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Rice husk extract, obtained using acid and alkali pretreatment extraction (AAPE), contains bioactive compounds and exhibits reducing abilities. Phenolic composition in rice husk extract was analyzed and the mechanism of silver nanoparticle (AgNP) biosynthesis by using AAPE rice husk extract was investigated in this study. Stable and spherically shaped AgNPs with a size of <15 nm were prepared under the following conditions: 0.001 M AgNO, AAPE rice husk extract diluted 10 times, pH 10, and reacted at 25 °C for 60 min. Synergistic effects among phenolic acids contributed to the formation of AgNPs, with the acids acting as excellent reducing agents (owing to their abundant hydroxyl groups) and excellent dispersants (owing to their derived CO groups), which enhanced the NPs' stability. Caffeic acid (CA) was demonstrated to synthesize AgNPs independently and is suggested to be the most crucial compound for reducing Ag during the biosynthesis with rice husk extract. A possible mechanism and reaction process for the formation of AgNPs synthesized using CA in rice husk extracts is proposed.

In the quest for advanced propulsion and power-generation systems, high-fidelity simulations are too computationally expensive to survey the desired design space, and a new design methodology is needed that combines engineering physics, computer simulations and statistical modeling. In this paper, we propose a new surrogate model that provides efficient prediction and uncertainty quantification of turbulent flows in swirl injectors with varying geometries, devices commonly used in many engineering applications. The novelty of the proposed method lies in the incorporation of known physical properties of the fluid flow as simplifying assumptions for the statistical model. In view of the massive simulation data at hand, which is on the order of hundreds of gigabytes, these assumptions allow for accurate flow predictions in around an hour of computation time. To contrast, existing flow emulators which forgo such simplications may require more computation time for training and prediction than is needed for conducting the simulation itself. Moreover, by accounting for coupling mechanisms between flow variables, the proposed model can jointly reduce prediction uncertainty and extract useful flow physics, which can then be used to guide further investigations.

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