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Natural bearing (raw and calcined at 500 °C) and iron-enriched (impregnation and pillaring) montmorillonitic clay samples were prepared. The obtained samples were characterized (X-ray diffraction, Fourier Transformed Infrared Spectroscopy, Scanning Electron Microscopy, and Energy Dispersive X-ray Spectroscopy) and evaluated as catalysts in catalytic wet oxidation of Brilliant Green and Crystal Violet. Experiments were conducted in the same conditions (0.5 g catalysts, 300 mL air/min or 0.5 mL H2O2, 25 mL of dye solution, 25 °C, initial solution pH = 6.0, for 3 h) in thermostated batch reaction tubes. Process evolution was followed using UV-Vis spectrometry (200–1100 cm−1) and total organic carbon. Dye removal efficiencies (decolorization) between 98 and 99% were determined, while total organic carbon removal efficiencies were calculated to be in the 53–98% range. Iron leakage investigation showed that iron is lost in higher amounts for the catalysts prepared using the impregnation method by comparison with the pillared sample. 

Soil organic matter (SOM) stocks, decom- position and persistence are largely the product of controls that act locally. Yet the controls are shaped and interact at multiple spatiotemporal scales, from which macrosystem patterns in SOM emerge. Theory on SOM turnover recognizes the resulting spatial and temporal conditionality in the effect sizes of controls that play out across macrosystems, and couples them through evolutionary and community assembly pro- cesses. For example, climate history shapes plant functional traits, which in turn interact with contem- porary climate to influence SOM dynamics. Selection and assembly also shape the functional traits of soil decomposer communities, but it is less clear how in turn these traits influence temporal macrosystem patterns in SOM turnover. Here, we review evidence that establishes the expectation that selection and assembly should generate decomposer communities across macrosystems that have distinct functional effects on SOM dynamics. Representation of this knowledge in soil biogeochemical models affects the magnitude and direction of projected SOM responses under global change. Yet there is high uncertainty and low confidence in these projections. To address these issues, we make the case that a coordinated set of empirical practices are required which necessitate (1) greater use of statistical approaches in biogeochem- istry that are suited to causative inference; (2) long- term, macrosystem-scale, observational and experi- mental networks to reveal conditionality in effect sizes, and embedded correlation, in controls on SOM turnover; and (3) use of multiple measurement grains to capture local- and macroscale variation in controls and outcomes, to avoid obscuring causative understanding through data aggregation. When employed together, along with process-based models to synthesize knowledge and guide further empirical work, we believe these practices will rapidly advance understanding of microbial controls on SOM and improve carbon cycle projections that guide policies on climate adaptation and mitigation.