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Copper-loaded organo-montmorillonite showed improved affinity towards hydrogen under ambient conditions. Clay ion exchange with a propargyl-ended cation followed by thiol-yne coupling with thioglycerol resulted in a porous structure with a 6 fold higher specific surface area, which dramatically decreased after copper incorporation. X-ray diffraction and photoelectron spectrometry, nuclear magnetic resonance (H and C) and CO-thermal programmed desorption revealed strong sulfur:Cu and oxygen:Cu interactions. This was explained in terms of structure compaction that 'traps' Cu nanoparticles (CuNPs) and reduces their mobility. Transmission electron microscopy showed predominant 1.0-1.5 nm CuNPs. Hydrogen capture appears to involve predominantly physical interaction, since differential scanning calorimetry measurements gave low desorption heat and almost complete gas release between 20 °C and 75 °C. Possible hydrogen condensation within the compacted structure should hinder gas diffusion inside CuNPs and prevent chemisorption. These results allow safe hydrogen storage with easy gas release to be envisaged even at room temperature under vacuum. The reversible capture of hydrogen can be even more attractive when using natural inorganic supports and commercial plant-derived dendrimers judiciously functionalized, even at the expense of porosity.

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SummaryBatch fermentation of milk inoculated with lactic acid bacteria was conducted in the presence of hydrotalcite-type anionic clay under static and ultrasonic conditions. An experimental study of the eff ect of fermentation temperature (t=38-43 °C), clay/milk ratio (R=1-7.5 g/L) and ultrasonic fi eld (ν=0 and 35 kHz) on process dynamics was performed. A mathematical model was selected to describe the fermentation process kinetics and its parameters were estimated based on experimental data. A good agreement between the experimental and simulated results was achieved. Consequently, the model can be employed to predict the dynamics of batch lactic acid fermentation with values of process variables in the studied ranges. A statistical analysis of the data based on a 2 3 factorial experiment was performed in order to express experimental and model-regressed process responses depending on t, R and ν factors.