Natural porous materials such as nanoporous clays are used as green and low-cost adsorbents and catalysts. The key factors determining their performance in these applications are the pore morphology and...

Natural clays are used as thixotropic agents and viscosity controllers. When dispersed in water, laminar clays create a fluid suspension which can be further thickened by incorporating acrylic and cellulosic polymers. Typically, the identification of formulations exhibiting the optimal characteristics for specific rheological applications requires experimental testing of a large number of prototype mixtures, for which yield points and apparent and plastic viscosities are obtained from the measured shear-stress curves. Herein, we report an alternative approach involving high-throughput virtual experiments aimed at discovering high-performing clay–polymer rheological agent formulations at a fraction of the time and cost of experimental campaigns. Specifically, we developed customized feature vectors to represent the rheological formulations and combined them with Random Forest models trained on experimental data points. The latter were identified by latin hypercube sampling with a multidimensional uniformity designer algorithm to generate homogeneous information and amplify the initial historical data set. The final Random Forest models (R 2 > 0.91) of rheological targets were used to perform multiobjective optimizations of the formulations based on the Pareto front algorithm. The three highest-performing formulations identified by the screening process were prepared, and their water dispersions were characterized in terms of their rheological behavior. The prototypes create viscoplastic fluids with a yield point and apparent and plastic viscosity ranges of 54.5–57 Pa (26–27 lb/100 ft2), 28.5–30 mPa s, and 15–18 mPa s, respectively, reflecting a suitable gel strength to be employed in engineering operations such as horizontal directional drilling.

The development of food and feed additives involves the design of materials with specific properties that enable the desired function while minimizing the adverse effects related with their interference with the concurrent complex biochemistry of the living organisms. Often, the development process is heavily dependent on costly and time-consuming in vitro and in vivo experiments. Herein, we present an approach to design clay-based composite materials for mycotoxin removal from animal feed. The approach can accommodate various material compositions and different toxin molecules. With application of machine learning trained on in vitro results of mycotoxin adsorption–desorption in the gastrointestinal tract, we have searched the space of possible composite material compositions to identify formulations with high removal capacity and gaining insights into their mode of action. An in vivo toxicokinetic study, based on the detection of biomarkers for mycotoxin-exposure in broilers, validated our findings by observing a significant reduction in systemic exposure to the challenging to be removed mycotoxin, i.e., deoxynivalenol (DON), when the optimal detoxifier is administrated to the animals. A mean reduction of 32% in the area under the plasma concentration–time curve of DON-sulphate was seen in the DON + detoxifier group compared to the DON group (P = 0.010).