Thermogravimetric Analysis of Moisture in Natural and Thermally Treated Clay Materials.

Clays are a class of porous materials; their surfaces are naturally covered by moisture. Weak thermal treatment may be considered practical to remove the water molecules, changing the surface properties and making the micro- and/or mesoporosities accessible to interact with other molecules. Herein, a modulated thermogravimetric analysis (MTGA) study of the moisture behavior on the structures of five, both fibrous and laminar, clay minerals is reported. The effect of the thermal treatment at 150 °C, which provokes the release of weakly adsorbed water molecules, was also investigated. The activation energies for the removal of the adsorbed water (Ea) were calculated, and they were found to be higher, namely, from 160 to 190 kJ mol-1, for fibrous clay minerals compared to lamellar structures, ranging in this latter case from 80 to 100 kJ mol-1. The thermal treatment enhances the rehydration in Na-montmorillonite, stevensite, and sepiolite structures with a decrease in the energy required to remove it, while Ea increases significantly in palygorskite (from 164 to 273 kJ mol-1). As a proof of concept, the MTGA results are statistically correlated, together with a full characterization of the physico-chemical properties of the five clay minerals, with the adsorption of two molecules, i.e., aflatoxin B1 (AFB1) and ß-carotene. Herein, the amount of adsorbed molecules ranges from 12 to 97% for the former and from 22 to 35% for the latter, depending on the particular clay. The Ea was correlated with AFB1 adsorption with a Spearman score of -0.9. When the adsorbed water is forcibly removed, e.g., under vacuum conditions and high temperatures, the structure becomes the most important, decreasing the Spearman score between ß-carotene and Ea to -0.6.

Machine learning-aided design of composite mycotoxin detoxifier material for animal feed.

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).

Machine-learning-accelerated multimodal characterization and multiobjective design optimization of natural porous materials.

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 surface activity, which are typically represented by properties such as specific surface area, pore volume, micropore content and pH. The latter may be modified and tuned to specific applications through material processing and/or chemical treatment. Characterization of the material, raw or processed, is typically performed experimentally, which can become costly especially in the context of tuning of the properties towards specific application requirements and needing numerous experiments. In this work, we present an application of tree-based machine learning methods trained on experimental datasets to accelerate the characterization of natural porous materials. The resulting models allow reliable prediction of the outcomes of experimental characterization of processed materials (R 2 from 0.78 to 0.99) as well as identification of key factors contributing to those properties through feature importance analysis. Furthermore, the high throughput of the models enables exploration of processing parameter-property correlations and multiobjective optimization of prototype materials towards specific applications. We have applied these methodologies to pinpoint and rationalize optimal processing conditions for clays exploitable in acid catalysis. One of such identified materials was synthesized and tested revealing appreciable acid character improvement with respect to the pristine material. Specifically, it achieved 79% removal of chlorophyll-a in acid catalyzed degradation.